Method for generating a control signal for a smart spraying device with at least one individual spray nozzle, and a method for controlling a smart spraying device, using PWM and field data relating to a vegetative indicator for providing an improved adaptive application of products onto an area to be treated.
A computer-implemented method for determining location-specific seeding rate and/or seeding depth for planting seeds in an agricultural field by means of an agricultural equipment, comprising the steps.
(step 1) identifying and/or indicating a parameter set comprising at least two seeding parameters, wherein the seeding parameter is a parameter having an impact and/or a potential impact to the seeding rate and/or seeding depth,
(step 2) receiving by the computing unit-from a database and/or from user input-field data, data indicative of the characteristics of the at least two seeding parameters of the parameter set, and data indicative of the agricultural equipment setup
(step 3) based on the field data, determining at least one level 1 zone, at least two level 2 zones and at least four level 3 zones,
(step 4) based on the data indicative of the characteristics of the at least two seeding parameters of the parameter set, and/or based on the data indicative of the equipment setup, determining for each seeding parameter whether it is a
level 1 parameter, level 2 parameter, or level 3 parameter,
(step 5) if at least one level 1 parameter is present, generating a seeding logic for level 1 parameter(s),
(step 6) if at least one level 2 parameter is present, generating a seeding logic for level 2 parameter(s), based on the seeding logic for level 1 parameter(s),
(step 7) if at least one level 3 parameter is present, generating a seeding logic for level 3 parameter(s), based on the seeding logic for level 1 parameter(s) and on the seeding logic for level 2 parameter(s),
(step 8) outputting the seeding rate and/or the seeding depth based on the determined seeding logics.
A computer-implemented method for cross-account model deployment is provided. According to the method, at least one artifact identifier of at least one to-be-deployed model artifact is provided. Further, at least one account tuple, the account tuple comprising a source account identifier identifying a source account and a target account identifier identifying a target account, is provided. Finally, the at least one to-be-deployed model artifact is moved from the source account to the target account. Further, a computer program element and a computer readable medium are provided.
The present invention relates to fungal disease management. In order to improve fungal disease management, a computer-implemented method is provided for determining a disease progression usable for fungicide spray schedule on an agricultural field. The method comprising the step of receiving data including crop variety data, environmental data, crop management data, and location data of the agricultural field. The crop variety data relates to a crop grown or to be grown on an agricultural field. The environmental data is indicative of an environmental condition for the agricultural field. The crop management data is indicative of fungicide spray history for the agricultural field. The method further comprises the step of applying a machine-learning model to the received data to determine disease progression time-series data of a fungal disease, wherein the machine-learning model has been trained to learn the disease progression under a condition defined by crop variety data, environmental data, crop management data, and location data based on historic data collected from one or more agricultural fields. The method further comprises the step of determining, based on the determined disease progression time-series data, a disease onset date of the fungal disease.
Disclosed is a computer-implemented method for generating control data for dose adaptation for an application device for applying a crop protection product, the method comprising obtaining, for each of a plurality of portions of an agricultural field, a sensor parameter value of a sensor parameter, wherein the sensor parameter value is derived from the sensor data depicting the respective portion and is indicative of harmful organism characteristics in the respective portion; determining, for each of the plurality of portions, one or more harmful organisms present in the portion; determining a portion-specific dosage for each of the plurality of portions based on the sensor parameter value, on at least one of the one or more harmful organisms, and on the crop protection product; and generating control data for dose adaptation for the application device based on the determined portion-specific dosage and optionally a current location of the application device.
Computer implemented method for generating target domain data, a computer implemented method for training a neural network, and a system, an apparatus, a use, and a computer program element adapted therefor, including providing to the neural network source domain data relating to a first agricultural domain; providing to the neural network target domain data relating to a second agricultural domain; determining a transformation relation between the source domain image data and target domain image data; and generating predicted target domain data with the target domain image data including the target domain background data and the target domain item data, and the target domain labeling data relating to a parameter of at least one target domain item identified in the target domain image of the second agricultural domain.
G06F 18/214 - Génération de motifs d'entraînementProcédés de Bootstrapping, p. ex. ”bagging” ou ”boosting”
G06V 10/82 - Dispositions pour la reconnaissance ou la compréhension d’images ou de vidéos utilisant la reconnaissance de formes ou l’apprentissage automatique utilisant les réseaux neuronaux
Disclosed is a computer-implemented method of determining a productivity variability index of an agricultural area. The method may comprise a step of providing a time-series of remotely sensed images of an agricultural area. The method may comprise a step of selecting from the time series of remotely sensed images of the agricultural area at least one subset of remotely sensed images based on a critical period for crop growth on the agricultural area. The method may comprise a step of transforming each subset of the remotely sensed images into a productivity image indicating a productivity distribution within the agricultural area to obtain at least one set of productivity images. The method may comprise a step of determining, based on the at least one set of productivity images, the productivity variability index of the agricultural area, wherein the productivity variability index indicates a degree of heterogeneity for the productivity distribution.
A computer-implemented method for providing variable application rate data for at least two application means of an application system of an application device for applying a treatment product onto an agricultural field, comprising: providing application configuration parameter data for the at least two application means of the application system of the application device; providing position and/or movement data of the application device; providing product application data comprising spatial information about the target application amount for the treatment product to be applied onto the agricultural field; generating a grid of application polygons; and determining variable application rate data for each of the at least two application means of the application system of the application device based on the application configuration parameter data, the position and/or movement data, and the product application data, wherein the variable application rate data is determined for each application polygon in the generated grid.
A method is provided, comprising providing crop data (10), (S20) proving field data (20), (S30) providing organism data (30), (S40) providing agricultural inputs data (40), (S50) providing user preference data (50), (S60) based on the crop data (10), field data (20), organism data (30), and agricultural inputs data (40), determining a set of treatment schedules for treating the agricultural field, (S70) determining the crop yield and the environmental impact associated with each treatment schedule within the determined set of treatment schedules, (S80) based on the user preference data (50), determining a user preference matching indicator for each treatment schedule within the determined set of treatment schedules using a matching model, (590) selecting at least one treatment schedule based on the user preference matching indicator, (S100) based on the selected treatment schedule, outputting a control file usable for controlling an agricultural equipment which can be used to treat the agricultural field.
The present invention relates to an plant disease detection at onset stage. Provided is a computer-implemented method for determining an onset and/or onset time of a plant (12) disease in agriculture. The method comprises providing (S110) first data including field data (14) associated with the plant's cultivation and weather data (16) associated with a location where said plant is cultivated to a computer model (20). The method further comprises determining (S120), by using said computer model (20), a plant disease presence prediction for said plant and its infestation with said plant disease and determining, from said computer model (20) output including said plant disease presence prediction and second data (18) including one or more vegetation indices associated with said plant, the onset and/or onset time to which said plant disease is expected to onset at said plant, by using the plant disease presence prediction and a change in the one or more vegetation indices.
A01G 7/06 - Traitement des arbres ou des plantes en cours de croissance, p. ex. pour prévenir la décomposition du bois, pour teinter les fleurs ou le bois, pour prolonger la vie des plantes
B64G 1/10 - Satellites artificielsSystèmes de tels satellitesVéhicules interplanétaires
11.
DECISION LOGIC PROVISION DEPENDING ON NETWORK COVERAGE
Computer implemented method and system for providing to an agricultural treatment device (107) a set of operation schedule data (321, 351) being indicative for an operation schedule (320, 350) comprising at least one task which includes instructions following a decision logic which is provided locally or via the cellular network (300) upon availability of a cellular network at the time when the task is executed in the zone of the agricultural field and providing control data based thereon.
G05D 1/00 - Commande de la position, du cap, de l'altitude ou de l'attitude des véhicules terrestres, aquatiques, aériens ou spatiaux, p. ex. utilisant des pilotes automatiques
G05D 105/15 - Applications spécifiques des véhicules commandés pour récolter, semer ou faucher, dans l’agriculture ou la sylviculture
Computer-implemented method and system for controlling a device on an agricultural field where it is determined whether an actual sensor setup covers a sensor setup requirement relating to the operation in the zone of the agricultural field and: in case the actual sensor setup is not capable of covering a sensor setup requirement providing a first operation schedule where a task is carried out based on parameters for which data of sensors being covered by the actual sensor setup are available and predetermined pre-settings for parameters for which no data of sensors is available; and in case the actual sensor setup is capable of covering the sensor setup requirement providing a second operation schedule where a task is carried out based on parameters for which data of sensors are available; and controlling the device based thereon.
A method for controlling and/or monitoring an agronomical resource is provided, comprising: receiving, in an agronomical assistant apparatus (200), agronomical querying data (205') associated with an agronomical query from a user (205); forming a stack (209'') of agronomical querying data by enhancing the agronomical querying data (205') with prompting data (209'); wherein the prompting data (209') comprise an indication for intended agronomical response data and/or intended answer (203''') in response to the agronomical querying data (205'); forwarding the stack (209'') of agronomical querying data to a prediction device (203) for generating agronomical response data (203'') linked to the stack (209') of agronomical querying data by a predefined probability relation; receiving the agronomical response data (203'') from the prediction device (203); checking the received agronomical response data (203''); if the received agronomical response data (203'') comprise querying instruction data (203') for at least one tool and/or for at least one repository (204); invoking the at least one tool and/or the at least one repository (204); and executing the querying instruction data (203', 207) on the at least one tool and/or on the at least one repository (204); providing intended agronomical response data (203'''); wherein the intended agronomical response data (203''') include the agronomical response data (203''); and/or the result of executing the querying instruction data (203') on the at least one tool and/or on the at least one repository (204).
A method is provided, comprising providing crop data (10), (S20) proving field data (20), (S30) providing organism data (30), (S40) providing agricultural inputs data (40), (S50) providing field potential data (50), (S60) based on the crop data (10), field data (20), organism data (30), and agricultural inputs data (40), determining a set of treatment schedules for treating the agricultural field, (S70) determining the crop yield and the environmental impact associated with each treatment schedule within the determined set of treatment schedules, (S80) based on the field potential data (50), determining a field potential matching indicator for each treatment schedule within the determined set of treatment schedules using a matching model, (S90) selecting at least one treatment schedule based on the field potential matching indicator, (S100) based on the selected treatment schedule, outputting a control file usable for controlling an agricultural equipment which can be used to treat the agricultural field.
The invention relates to a method for controlling and/or monitoring an agronomical resource, comprising: authenticating a user for retrieving an identity; authorizing the user in order to get an access rule set for the user; getting the access rule set of the user for accessing a repository having user specific data associated with the agronomic resource; encoding the identity of the user and the access rule set of the user for accessing the repository to an encoded user identifier; getting repository description data; wherein the repository description data comprise information about data retrievable from the repository by using the encoded user identifier; receiving agronomical querying data associated with an agronomical query from the user; wherein the agronomical query is related to the agronomical resource; forwarding the agronomical querying data and the repository description data to a prediction device; generating, by the prediction device, a querying instruction associated with the agronomical querying data such, that the generated querying instruction and the agronomical querying data are linked by a predefined probability relation; and such that the querying instruction comprises at least one request to the repository having the user specific data associated with the agronomic resource.
The disclosure relates to providing agronomical data for re-training a pre-trained transformer-based model for an agronomical task. The disclosure further relates to using the trained transformer-based model for controlling and/or monitoring an agronomical environment.
A computer-implemented method for generating a soil property map of an agricultural field, comprising the steps: receiving crop property distribution data of the agricultural field comprising at least one crop related parameter (S1); determining equivalent areas having a crop related parameter value within a certain range in the crop property distribution data (S2); receiving soil data with respect to at least one soil parameter for each of the determined equivalent areas (S3); generating a soil property map of the agricultural field based on the soil data and the equivalent areas (S4).
METHOD FOR PROVIDING VARIABLE APPLICATION RATE DATA FOR APPLICATION SUB-AREAS OF A FIELD FOR AGRICULTURAL PRODUCTS BASED ON MEASUREMENTS OF THE GREEN AREA INDEX (GAI)
Computer-implemented method for providing variable application rate data for applying an agricultural product on an application sub-area of an agricultural field (120), comprising: providing green area index data for at least one measurement sub-area, wherein the green area index data are provided by at least one detection system (14), wherein the at least one detection system (14) is configured to detect a green leaf area of a crop canopy in at least one measurement sub-area of an agricultural field (120) while an application device is moving through the agricultural field (120); providing a variable application rate model (15) configured to provide variable application rate data for applying the agricultural product on the application sub-area at least based on the green area index data of the at least one measurement sub-area; providing variable application rate data for applying the agricultural product on the application sub-area at least based on the green area index data by utilizing the variable application rate model (15).
Computer-implemented method for setting a threshold value for applying an agricultural product on an application sub-area of an agricultural field (120) in a spot spray application, comprising: providing weed coverage data for at least one measurement sub-area and a preceeding measurement subarea, wherein the weed coverage data are provided by at least one detection system, wherein the detection system is adapted to detect weed coverage at least in the at least one measurement sub-area while an application device (100) is moving through the agricultural field (120); providing a threshold model configured to provide a threshold value at least based on the provided weed coverage data in the at least one measurement sub-area; setting the threshold value for applying the agricultural product on the application sub-area based on the weed coverage data in the at least one measurement sub-area by utilizing the threshold model. The method applies aside of the uses case for an in-crop weed control also to the vegetation burndown and the use of defoliant and/or dessicants.
The present invention relates to digital farming. In order to support seasonal recommendations and planning, an apparatus (10) for generating an application scheme for controlling a harmful organism on an agricultural field. The apparatus comprises an input unit (12), a processing unit (14), and an output unit (16). The input unit (12) is configured to receive information on an expected presence of the harmful organism for an upcoming period. The processing unit (14) is configured to determine a rough control schedule comprising a plurality of control time periods for controlling the harmful organism, based on the information about an expected presence of the harmful organism for the upcoming period; determine, based on product data that comprises information on a plurality of agricultural products, at least one program of control measures to be sequentially applied to the agricultural field to control the harmful organism at the plurality of control time periods, wherein each control time period is associated with at least one respective control measure selected from a chemical measure or a mechanical measure; and generate the application scheme that comprises the determined rough control schedule with the plurality of control time periods and/or the at least one program of control measures. The output unit (16) is configured to provide the generated application scheme.
A computer-implemented method for monitoring the treatment of an agricultural field (11) with a pesticide product by an agricultural machine (10), wherein the agricultural machine (10) comprises at least one sensor device (31) and at least one treatment component comprising at least one nozzle (21), the method comprising the steps: providing (40) location-specific sensor data of the agricultural field (10) from the at least one sensor device (31); analyzing (42) the location-specific sensor data with respect to at least one harmful organism as one treatment indicator; generating location-specific control data for the at least one treatment component based on the analyzed location¬ specific sensor data; providing (44) a pesticide savings parameter in real-time, wherein the pesticide savings parameter relates to an amount of pesticide product based on the location-specific sensor data in relation to an amount of pesticide product based on a reference treatment with a pesticide product.
The invention relates to a computer-implemented method for providing control data (1) for con- trolling an agricultural device (2). According to the method, height data (6) of objects (4) situated on an agricultural field (5) is provided, wherein the height data (6) comprises information about the height of the objects (4) and their position in the agricultural field (5) at a time t1. Further, control data (1) for controlling the agricultural device (2) at a time t2 based on the provided height data (6) is provided. The invention further relates to a corresponding system, apparatus, treatment device (2.1), rolling unit (2.2), and computer program element.
A01D 75/18 - Dispositifs de sécurité pour certaines parties des machines
A01B 69/04 - Adaptations particulières de la conduite automatique du tracteur, p. ex. systèmes électriques pour labourage selon les courbes de niveau
23.
COMPUTER-IMPLEMENTED METHOD FOR PROVIDING OPERATION DATA FOR TREATMENT DEVICES ON AN AGRICULTURAL FIELD, CORRESPONDING SYSTEMS, USE AND COMPUTER ELEMENT
A computer-implemented method for providing operation data for treatment devices (102, 104, 106, 107) on an agricultural field (112). wherein the treatment devices include at least a first treatment device (102) and a second treatment device (104) for treating the agricultural field, the method comprising the steps: obtaining field data for at least one section (113) of the agricultural field at least by the first treatment device, wherein the obtained field data indicates a monitoring and treatment status associated with the at least one section: based on the monitoring and treatment status associated with the at least one section providing operation data associated with the at least one section of the agricultural field for the second treatment device.
B64U 101/40 - Véhicules aériens sans pilote spécialement adaptés à des utilisations ou à des applications spécifiques à l’agriculture ou à la sylviculture
24.
METHOD AND SYSTEM FOR GENERATING A CROP FAILURE MAP
A method for generating a crop failure map is provided. The method comprises providing annotated training data, the annotated training data comprising aerial images of zones of an agricultural field and the annotations relating to failures of the crops within the agricultural field, the crops being perennial crops. The method further comprises training an artificial intelligence with the annotated training data and providing field data, the field data comprising at least one aerial image of an agricultural field to be inspected. The trained artificial intelligence is run on the field data to generate a crop failure map. Also, a system for generating a crop failure map and a use of a crop failure map are provided.
G06V 10/774 - Génération d'ensembles de motifs de formationTraitement des caractéristiques d’images ou de vidéos dans les espaces de caractéristiquesDispositions pour la reconnaissance ou la compréhension d’images ou de vidéos utilisant la reconnaissance de formes ou l’apprentissage automatique utilisant l’intégration et la réduction de données, p. ex. analyse en composantes principales [PCA] ou analyse en composantes indépendantes [ ICA] ou cartes auto-organisatrices [SOM]Séparation aveugle de source méthodes de Bootstrap, p. ex. "bagging” ou “boosting”
G06V 10/82 - Dispositions pour la reconnaissance ou la compréhension d’images ou de vidéos utilisant la reconnaissance de formes ou l’apprentissage automatique utilisant les réseaux neuronaux
Computer-implemented method for evaluating application threshold values for an application of an agricultural product on an agricultural field, comprising the steps: providing field data comprising geographic data about an agricultural field; segmenting at least a part of the agricultural field in sections and assigning different application threshold values for the agricultural product to different sections; applying the agricultural product on the sections according to the assigned application threshold values for the agricultural product; obtaining evaluation data for the different sections representing the effectiveness of the treatment with the different application threshold values; evaluating the different application threshold values at least based on the evaluation data (e.g. efficacy and or yields).
A method for modifying a treatment performance for treating an agricultural field by an agricultural machine, whereas the method comprises a modification function and the agricultural machine comprises at least one treatment component, characterized in that the method having the steps of —Obtaining field data (S1, S10); —Determining a treatment performance by analyzing the field data (S2, S12); —Providing a treatment performance modification via the modification function and a representation parameter (S3, S13); —Modifying the treatment performance with the treatment performance modification (S4, S15).
Computer-implemented method for providing nitrogen uptake data of plants and/or plant parts of an agricultural field, comprising: providing (100) leaf area index data for the agricultural field; providing (110) chlorophyll content data for the agricultural field; providing (120) an nitrogen uptake model configured to provide nitrogen uptake data of plants and/or plant parts based on leaf area index data and chlorophyll content data; providing (130) nitrogen uptake data of plants and/or plant parts of the agricultural field based on the provided leaf area index data and chlorophyll content data utilizing the nitrogen uptake model. The method further comprises determining stem weight data based on a provided crop specific leaf weight ratio and determining stem nitrogen data based on the stem weight data and a provided crop specific stem nitrogen concentration. Providing nitrogen uptake data of plants and/or plant parts of the agricultural field is further based on the determined stem nitrogen data.
Computer-implemented method for determining and providing an application scheme for fertilizers, comprising the following steps: providing agricultural crop data (S10) comprising information about an agricultural crop species sown or planned to sow in a field; providing application time data (S20) comprising information about at least one planned application time of applying fertilizers in the field; providing nutrient specifier data (S30) comprising information about at least one nutrient specifier present or expected in the field; performing a database search in a fertilizer product database (S50) at least based on the provided agricultural crop data, the provided application time data, the provided nutrient specifier data and determining the fertilizer products matching the agricultural crop and the application time and one of the nutrient specifiers, wherein the fertilizer product database comprises information about a plurality of fertilizer products, and wherein the information about the fertilizer products comprises at least information about an application area of each fertilizer product, an active ingredient of each fertilizer product and a suitable application time for each fertilizer product; ranking generated application schemes (S70), wherein the different application schemes (S60) are generated by combining the determined fertilizer products in such a way that at least one nutrient specifier is covered with an efficacy above a defined threshold; wherein the step of ranking is based on one or more of specific statistics per application scheme. and generating control data configured to be used or usable in an agricultural equipment, wherein the control data is based on the highest ranked application scheme, or an application scheme selected by a user.
A computer-implemented method for providing operation data for treatment devices for treating an agricultural field (14), wherein the treatment devices include at least a first treatment device (21) and a second treatment device (22) for treating the agricultural field (14), the method comprising the steps: obtaining field data for at least one section of the agricultural field at least from the first treatment device; based on the field data associated with the at least one section providing selection data for selecting the second treatment device associated with the at least one section of the agricultural field.
A01M 7/00 - Adaptations ou aménagements particuliers des appareils de pulvérisation de liquides aux fins couvertes dans la présente sous-classe
B64D 1/18 - Largage en vol d'une matière poudreuse, liquide ou gazeuse, p. ex. pour la lutte contre l'incendie par pulvérisation, p. ex. d'insecticides
B64U 101/40 - Véhicules aériens sans pilote spécialement adaptés à des utilisations ou à des applications spécifiques à l’agriculture ou à la sylviculture
B64U 101/45 - Véhicules aériens sans pilote spécialement adaptés à des utilisations ou à des applications spécifiques à l’épandage en vol de liquides ou de poudres, p. ex. sur les cultures
The present disclosure relates to targeted treatment of specific weed species with multiple treatment devices. Provided is a system and computer-implemented method for controlling operation of multiple treatment devices (102, 103, 104, 107) having treatment device configurations different to each other for treating an agricultural field (112). The method comprises analyzing field data, monitored as at least one of the multiple treatment devices (102, 103, 104, 107) traverses the field, to identify weed present at a certain field location (113) of the field (112) by weed species; and targeted instructing at least one treatment device (102, 103, 104, 107) among the multiple treatment devices (102, 103, 104, 107) that has a matching treatment device configuration for an identified weed species to treat the field (112) at the corresponding certain field location (113) against the identified weed species.
B64U 101/30 - Véhicules aériens sans pilote spécialement adaptés à des utilisations ou à des applications spécifiques à l’imagerie, à la photographie ou à la vidéographie
B64U 101/40 - Véhicules aériens sans pilote spécialement adaptés à des utilisations ou à des applications spécifiques à l’agriculture ou à la sylviculture
B64U 101/45 - Véhicules aériens sans pilote spécialement adaptés à des utilisations ou à des applications spécifiques à l’épandage en vol de liquides ou de poudres, p. ex. sur les cultures
The present disclosure relates to a multi-device treatment of an agricultural field. Proposed is a computer-implemented method for treating an agricultural field (10). The method comprises the step of analyzing field data (S120), monitored as a first treatment device (100) traverses the field (SI 10), to determine whether the field (10) at a certain field location (10a) has a field condition which is treatable with a first device configuration of the first treatment device (100). If it is determined that the field at the certain field location associated with the respective field condition is treatable with the first device configuration of the first treatment device (100), the first treatment device (100) is controlled (S130) to treat the field at the certain field location (10a). Otherwise, if it is determined that the field at the certain field location (10a) associated with the respective field condition is not treatable with the first device configuration of the first treatment device (100), the certain field location (10a) is provided (S140) for at least one further, second treatment device (200) having a second device configuration that is different to the first device configuration and capable of treating the field at the certain field location.
A01B 69/04 - Adaptations particulières de la conduite automatique du tracteur, p. ex. systèmes électriques pour labourage selon les courbes de niveau
A01G 7/06 - Traitement des arbres ou des plantes en cours de croissance, p. ex. pour prévenir la décomposition du bois, pour teinter les fleurs ou le bois, pour prolonger la vie des plantes
32.
METHOD AND SYSTEM FOR DETERMINING A SOWING ROW DIRECTION
A method for determining a sowing row direction (4) is provided. The method comprises providing location data for at least one location of interest (5), the at least one location of interest (5) being located within an agricultural field (2). The method further comprises providing a sowing row information map (1; 6; 8), the sowing row information map (1; 6; 8) comprising information on the sowing rows (7) of the agricultural field (2) with a sub-field resolution. Finally, the method comprises identifying the sowing row direction (4) at the at least one location of interest (5) based on the sowing row information map (1; 6; 8). Further, a system (14) for determining a sowing row direction (4), a computer program element, a computer readable medium and a system (10) for generating a processed sowing row information map (6; 8) from a sowing track map (1) are provided.
A01B 69/00 - Direction des machines ou instruments agricolesGuidage des machines ou instruments agricoles selon un parcours déterminé
33.
METHOD FOR DETERMINING A RANKING OF TREATMENT PARAMETERS (SUCH AS CROP PROTECTION PRODUCTS) FOR TREATING AN AGRICULTURAL FIELD VIA AN EFFICACY ADJUSTMENT MODEL BASED ON GENETIC DATA
A computer-implemented method for generating a control file usable for controlling an agricultural equipment based on at least one treatment parameter, comprising the following steps: (step 1) (110) providing genetic measurement data (40) of at least one organism which existed or is existing or is expected to exist in the agricultural field, (step 2) (120) providing treatment parameter data (42) for at least two treatment parameters capable of targeting the at least one organism, (step 3) (130) based on the treatment parameter data (42), providing first level efficacy data (44) comprising efficacies (“first level efficacies”) of the at least two treatment parameters relating to the at least one organism on a first level of the taxonomic rank, (step 4) (140) based on the treatment parameter data (42) and the first level efficacy data (44), determining a first ranking (46) of the at least two treatment parameters, (step 5) (150) providing an efficacy adjustment model (50), (step 6) (160) by modifying the first level efficacy data (44) based on the genetic measurement data (40) and the treatment parameter data (42) via the efficacy adjustment model (50), obtaining second level efficacy data (52) comprising efficacies (“second level efficacies”) of the at least two treatment parameters relating to the at least one organism on a second level of the taxonomic rank being below the first level of the taxonomic rank, (step 7) (170) based on the treatment parameter data (42) and the second level efficacy data (52), determining a second ranking (54) of the at least two treatment parameters. (step 8) (180) outputting the highest ranked or user-selected treatment parameter as a control file usable for controlling an agricultural equipment
A01M 7/00 - Adaptations ou aménagements particuliers des appareils de pulvérisation de liquides aux fins couvertes dans la présente sous-classe
G06Q 10/06 - Ressources, gestion de tâches, des ressources humaines ou de projetsPlanification d’entreprise ou d’organisationModélisation d’entreprise ou d’organisation
34.
METHOD FOR GENERATING A ZONE SPECIFIC APPLICATION MAP FOR TREATING AN AGRICULTURAL FIELD WITH PRODUCTS
A method for generating a zone specific application map (8) for treating an agricultural field with products is provided. The method comprises providing a hypermodel (1) comprising a product recommendation model, PRM (2) and a biophysical parameter model, BPM (3). The method further comprises providing PRM input parameters (4) for the product recommendation model (2) and generating PRM output (5) by the product recommendation model (2). The method also comprises providing BPM input parameters (6) for the biophysical parameter model (3) and generating BPM output (7) by the biophysical parameter model (3). Finally, the method comprises generating the zone specific application map (8) by the hypermodel (1), using at least parts of the PRM output (5) and parts of the BPM output (7). Further, a system (19) for generating a zone specific application map (8), a computer program element, a use of a zone specific application map (8) and an agricultural equipment (23) are provided.
Computer-implemented method for providing control data, a crop failure map and/or a replanting map, comprising: providing image data of at least a part of the agricultural field, wherein the image data comprise at least one image of the at least one part of the agricultural field at a time when sown crops have emerged; providing an image classification model configured to identify crop failures in a crop line; determining crop failures in the at least one image of the at least one part of the agricultural field utilizing the image classification algorithm; providing, based on the determined crop failures, control data for an agricultural device, wherein the control data at least comprising position data of one or more of the determined crop failures; and/or providing a crop failure map indicating the crop failures in the agricultural field; and/or providing a replanting map indicating where in the agricultural field a crop failure is above a predetermined crop failure threshold.
Computer-implemented method for providing corrected plant-related index data, comprising the steps of: providing initial plant-related index data for an agricultural field, preferably based on at least one satellite image; and correcting the initial plant-related index data for the agricultural field at least based on historical plant-related index data for the agricultural field and providing corrected plant-related index data.
G06V 10/70 - Dispositions pour la reconnaissance ou la compréhension d’images ou de vidéos utilisant la reconnaissance de formes ou l’apprentissage automatique
A method for generating a crop phenology prediction (7) is provided. The method comprises the steps of providing crop phenology training data (1) for a plurality of crops and a plurality of locations (17); training a machine learning system (6) using the crop phenology training data (1); providing a selection of the plurality of crops and a specific location (18); and generating a crop phenology prediction (7) for a selection of the plurality of crops at a specific location (18) using the trained machine learning system (6). Further, a system (21) for generating a crop phenology prediction (7) is provided. The system (21) comprises at least one input interface (25) for providing a selection of crops and a specific location (18), at least one processing unit (22) configured to carry out the method for generating a crop phenology prediction (7) and at least one output interface (23) for outputting the crop phenology prediction (7), the agronomic recommendation (8) and/or the agronomic control data (26) for the selection of crops at the specific location (18). Further, a computer program element, a use of a crop phenology prediction (7) and a use of agronomic control data (26) are provided.
A computer-implemented method for generating a suitability index for the application of an agricultural product to a field with an application device is provided, wherein the method comprises providing vegetation data (302) associated with a biomass distribution on the field, providing machinery characteristics associated with a machinery footprint of the application device (202) on the field (108'), generating a machinery cell grid (305) from the machinery characteristics, wherein the machinery cell grid (305) comprises at least one cell (305a, 305b), generating a dosage cell grid (307) from the vegetation data and the machinery cell grid (305), wherein the dosage cell grid (307) indicates a dosage for the application of the agricultural product for the at least one cell (305a, 305b), generating a matching factor by matching the dosage cell grid and the vegetation data, generating the suitability index (309) based on the matching factor and providing the suitability index (309).
Computer-implemented method for providing control data for controlling a drone comprising a dropleg-type nozzle during an application of an agricultural product on an agricultural field, comprising: providing a control model configured to determine a nominal distance between a plant and the dropleg-type nozzle of the drone based on a growth height of a plant and a distance between the dropleg-type nozzle and the drone; providing growth height data comprising the growth height of the plant cultivated on the agricultural field; providing distance data comprising the distance between the dropleg-type nozzle and the drone; providing nominal distance data based on the growth height data and the distance data by utilizing the control model; providing control data for the drone based on the nominal distance data, wherein the control data at least comprise flight height data for the drone.
G05D 1/00 - Commande de la position, du cap, de l'altitude ou de l'attitude des véhicules terrestres, aquatiques, aériens ou spatiaux, p. ex. utilisant des pilotes automatiques
A01M 7/00 - Adaptations ou aménagements particuliers des appareils de pulvérisation de liquides aux fins couvertes dans la présente sous-classe
A computer implemented method for validating a nozzle configuration for a planned treatment of an agricultural relevant organism on an agricultural field, comprising the steps of: providing operation data associated with the planned treatment, determining one or more spray characteristics of an individual nozzle based on the provided operation data with a model relating operation data to one or more spray characteristics, validating the individual nozzle for the planned treatment based on the determined spray characteristics of the individual nozzle and one or more reference spray characteristics, generating control data in response to the validation step, providing the generated control data.
A computer implemented method for measuring spray characteristics of a nozzle, comprising obtaining fluid sensor data associated with a spray pattern of the nozzle, providing a model relating fluid sensor data associated with a spray pattern of the nozzle with a measure for spray characteristics based on the spray pattern inside a target spot region and outside a target spot region, measuring the spray characteristics based on the obtained fluid sensor data and the provided model, providing the measure for spray characteristics of the nozzle in particular via the communication interface.
In order to achieve a more effective application of a seed product and/or crop nutrition product, a computer-implemented method is provided for applying a seed product of at least one crop and/or applying crop nutrition product to at least one crop in a field. The method comprises the steps of collecting remotely-sensed data of the field before an application of the seed product and/or crop nutrition product in the field, determining, based on the collected remotely-sensed data, at least one soil parameter at a plurality of locations in the field, generating, for each of the plurality of locations, a predicted yield response to the application of the seed product and/or crop nutrition product for the at least one crop based on the at least one determined soil parameter and a prediction model, wherein the prediction model is parametrized or trained based on a sample set including a plurality of different values of the at least one soil parameter and associated yield responses for the at least one crop under the application of the seed product and/or crop nutrition product, deciding, for each of the plurality of locations in the field, whether to treat or not based on the predicted yield response, and outputting information indicative of the decision useable to activate at least one treatment device to comply with the decision.
G06Q 10/0637 - Gestion ou analyse stratégiques, p. ex. définition d’un objectif ou d’une cible pour une organisationPlanification des actions en fonction des objectifsAnalyse ou évaluation de l’efficacité des objectifs
Computer-implemented method for generating control data configured to be used or usable in an agricultural equipment for treating a field, comprising the following steps: (S10) providing crop data; (S20) providing field data; (S30)—optionally—providing historic treatment data; (S40)—optionally—providing environmental data; (S50) at least based on the crop data and on the field data, initiating and/or performing data processing in at least one database and/or database system, (S60) determining the nutrient-specific risk based on the result of the data processing, (S70) providing and/or determining the nutrient-specific threshold, (S80) determining, based on the nutri-ent-specific risk and the nutrient-specific threshold and based on the data processing in at least one treatment-related database, at least two treatment schedules capable of targeting the at least one nutrient, (S90) ranking the at least two treatment schedules, based on one or more of the specific statistics (Q1) to (Q28), (S100) outputting the ranked list of the at least two treatment schedules, (S110) generating control data configured to be used or usable in an agricultural equipment, wherein the control data is based on the highest ranked treatment schedule from the ranked list, or a treatment schedule selected by a user from the ranked list.
Computer-implemented method for generating control data configured to be used or usable in an agricultural equipment for treating a field, comprising the following steps: (S10) providing crop data, wherein the crop data comprise information about an agricultural crop species providing crop data grown or sown or planned to be grown or sown in a field; (S20) providing field data, wherein the field data comprise information about the field; (S30)—optionally—providing historic treatment data, (S40)—optionally—providing environmental data, (S50) at least based on the crop data and on the field data, initiating and/or performing data processing in at least one database and/or database system, (S60) determining the organism-specific risk based on the result of the data processing, (S70) providing and/or determining the organism-specific threshold, (S80) determining, based on the organism-specific risk and the organism-specific threshold and based on the data processing in at least one treatment-related database, at least two treatment schedules capable of targeting the at least one organism, (S90) ranking the at least two treatment schedules, based on one or more of the specific statistics (Q1) to (Q28): (S100) outputting the ranked list of the at least two treatment schedules, (S110) generating control data configured to be used or usable in an agricultural equipment, wherein the control data is based on the highest ranked treatment schedule from the ranked list, or a treatment schedule selected by a user from the ranked list.
Computer-implemented method for providing herbicide application data for applying a herbicide product on an agricultural field, comprising: providing image data of an agricultural field; providing a weed classification model configured to provide weed data based on the image data of the agricultural field; providing weed data for the agricultural field based on the weed classification model and the image data; providing a weed growth model configured to provide weed emergence data and/or weed growth data for the agricultural field based on the weed data for the agricultural field; providing weed emergence data and/or weed growth data for the agricultural field based on the weed growth model and the weed data for the agricultural field; providing herbicide application data at least comprising application timing data for at least one herbicide product for applying on the agricultural field.
A01N 25/30 - Biocides, produits repoussant ou attirant les animaux nuisibles, ou régulateurs de croissance des végétaux, caractérisés par leurs formes, ingrédients inactifs ou modes d'applicationSubstances réduisant les effets nocifs des ingrédients actifs vis-à-vis d'organismes autres que les animaux nuisibles caractérisés par les agents tensio-actifs
46.
METHOD FOR DETERMINING FIELD-OR ZONE-SPECIFIC SEEDING RATE, DEPTH, AND TIME FOR PLANTING A CROP IN AN AGRICULTURAL FIELD BASED ON MULTIPLE DATA INPUTS SUCH AS CROP, FIELD, YIELD, WEATHER, AND/OR SOIL DATA
A computer-implemented method for determining at least one of the treatment parameters selected from the group consisting of: (a) at least one rate (seeding rate) for planting at least one crop in a field or a sub-field zone, (b) at least one depth (seeding depth) for planting at least one crop in a field or a sub-field zone, and (c) at least one time window (seeding time) for planting at least one crop in a field or a sub-field zone, comprising the following steps: (step 1) receiving by the computing unit—from a database and/or from user input and/or from real-time measurements—crop data relating to the at least one crop to be planted in the field or in the sub-field zone and static field data relating to the field or the sub-field zone, (step 2) receiving by the computing unit—from a database and/or from user input and/or from real-time measurements—at least one type of additional data selected from the group consisting of: (A) yield data relating to the field or the sub-field zone, (B) weather data relating to the field or the sub-field zone, and (C) soil data relating to the field or the sub-field zone, (step 3) at least based on the crop data, and the static field data, and the at least one type of additional data, initiating and/or performing data processing in at least one database and/or database system containing (i) data related to crop data and/or data related to static field data, (ii) data related to the at least one treatment parameter, and (iii) data related to at least one type of additional data selected from the group consisting of: yield data, weather data, and soil data, (step 4) outputting the at least one treatment parameter based on the result of the data processing.
A method for generating a control file to operate a treatment device (10) on an agricultural area (11) to be treated, the method comprising the steps of: providing (40) treatment data signifying a type of treatment to be conducted by the treatment device and crop data relating to a crop present on the agricultural area (11) to be treated to a preparation system (13); determining (42), by the preparation system (13), from the treatment data and the crop data at least one operation parameter, wherein the determined operation parameter is related to a real-time and/or location-specific condition to be monitored during treatment; generating (44), by the preparation system (13), a control file comprising the at least one operation parameter, the generated control file usable to operate the treatment device (10) based on the real-time and/or location-specific condition to be monitored during treatment.
The present invention relates to the planning and implementation of agricultural measures using remote sensing data and local field data. Using remote sensors, the total required amount and partial-area-specific required amounts of plant protection agents and/or nutrients and/or seeds and/or the like can be determined, and based on this information, the use of an application device can be planned. Using local field sensors, the current local required amounts in the field are determined so that the application device can apply the corresponding amounts as required.
Computer-implemented method for providing combined application data comprising variable application data for applying a first agricultural product on an agricultural field and spot application data for applying a second agricultural product on the agricultural field, the method comprising: - providing field parameter data comprising spatial distribution data of at least one field parameter in the agricultural field; - providing variable application data for applying the first agricultural product on the agricultural field at least based on the field parameter data; - providing sensor data with respect to the agricultural field; - providing spot application data for applying the second agricultural product on the agricultural field at least based on the sensor data; - providing combined application data comprising the variable application data for applying the first agricultural product on the agricultural field and the spot application data for applying the second agricultural product on the agricultural field.
Computer-implemented method for providing pesticide application data for applying a pesticide product onto an agricultural field, comprising: providing soil property data of an agricultural field comprising at least one soil property parameter of the agricultural field; providing pesticide product data comprising at least information on the application of a pesticide product with respect to the at least one soil property parameter; providing variable pesticide application data for applying a pesticide product on the agricultural field at least based on the soil property data and the pesticide product data.
Computer-implemented method and system (100) for enhancing a plant image database (230) for improved damage identification on plants. The system receives a real-world image (91) of a plant (11), recorded at a particular geographic location (2), together with image metadata comprising location data (LD1) indicating the particular geographic location (2), and a time stamp (TS1) indicating the point in time (3) when the real-world image (91) was recorded. A damage identifier (110), trained for identifying damage classes associated with damage symptoms present on plants of particular plant species, generates, from the real-world image (91), an output including a damage class (DC1) for the damage symptoms on the real-world image. A similarity checker (120) determines feature similarities of the real-world image with selected images (232, 233, 234, 235) in a plant image database (230), and further identifies at least a subset (230s) of the selected images having a feature similarity with the real-world image exceeding a minimum similarity value (124). The generated damage class (DC1) and the images of the subset (230s) with respective damage classes and plant species identifier are provided to a user (9). In response, the system receives from the user (9) a confirmed damage class (CDC1) for the real-world image (91). A database updater (140) of the system updates the plant image database (230) by storing the received real-world image (91) together with its plant species identifier, its location data, its time stamp and the confirmed damage class.
G06F 16/587 - Recherche caractérisée par l’utilisation de métadonnées, p. ex. de métadonnées ne provenant pas du contenu ou de métadonnées générées manuellement utilisant des informations géographiques ou spatiales, p. ex. la localisation
G06V 10/74 - Appariement de motifs d’image ou de vidéoMesures de proximité dans les espaces de caractéristiques
G06V 10/764 - Dispositions pour la reconnaissance ou la compréhension d’images ou de vidéos utilisant la reconnaissance de formes ou l’apprentissage automatique utilisant la classification, p. ex. des objets vidéo
G06V 10/94 - Architectures logicielles ou matérielles spécialement adaptées à la compréhension d’images ou de vidéos
G06V 10/774 - Génération d'ensembles de motifs de formationTraitement des caractéristiques d’images ou de vidéos dans les espaces de caractéristiquesDispositions pour la reconnaissance ou la compréhension d’images ou de vidéos utilisant la reconnaissance de formes ou l’apprentissage automatique utilisant l’intégration et la réduction de données, p. ex. analyse en composantes principales [PCA] ou analyse en composantes indépendantes [ ICA] ou cartes auto-organisatrices [SOM]Séparation aveugle de source méthodes de Bootstrap, p. ex. "bagging” ou “boosting”
G06V 10/77 - Traitement des caractéristiques d’images ou de vidéos dans les espaces de caractéristiquesDispositions pour la reconnaissance ou la compréhension d’images ou de vidéos utilisant la reconnaissance de formes ou l’apprentissage automatique utilisant l’intégration et la réduction de données, p. ex. analyse en composantes principales [PCA] ou analyse en composantes indépendantes [ ICA] ou cartes auto-organisatrices [SOM]Séparation aveugle de source
G06V 10/82 - Dispositions pour la reconnaissance ou la compréhension d’images ou de vidéos utilisant la reconnaissance de formes ou l’apprentissage automatique utilisant les réseaux neuronaux
52.
A METHOD FOR FORECASTING OF A PARAMETER OF A CULTIVATION AREA
The present invention relates to a method for forecasting of a parameter value of a cultivation area, the method comprising: generating (S1), by an ensemble modelling structure, at least one first-model output parameter related to the cultivation area using ensemble modelling based on at least one first-model input parameter; generating (S2), by an machine learning structure, at least one second-model output parameter related to the cultivation area using machine learning based on at least one second-model input parameter; and merging (S3), by a model merging structure, the at least one first-model output parameter and the at least one first-model output parameter to calculate the parameter value of the cultivation area.
The present invention relates to digital farming. In order to reduce residual amounts of a treatment device. There is provided a method for treatment management on an agricultural area applying a treatment product to the agricultural area via a treatment device, the method comprising the following steps: providing (40) a field identifier and optionally a treatment specifier; determining (42) a treatment map based on the field identifier and the optional treatment specifier; determining (44) a field path for applying the treatment product to the field via the treatment device based on the treatment map, wherein the field path comprises at least a first section and a second section; generating (46) a control file for the treatment device, wherein the control file includes a recording mode for the first section of the field path and a first application mode for the second section of the field path; and providing (48) the control file, which is usable for controlling the treatment device.
A computer-implemented method for determining location-specific seeding rate and/or seeding depth for planting seeds in an agricultural field by means of an agricultural equipment, comprising the steps. (step 1) identifying and/or indicating a parameter set comprising at least two seeding parameters, wherein the seeding parameter is a parameter having an impact and/or a potential impact to the seeding rate and/or seeding depth, (step 2) receiving by the computing unit ‒ from a database and/or from user input ‒ field data, data indicative of the characteristics of the at least two seeding parameters of the parameter set, and data indicative of the agricultural equipment setup (step 3) based on the field data, determining at least one level 1 zone, at least two level 2 zones and at least four level 3 zones, (step 4) based on the data indicative of the characteristics of the at least two seeding parameters of the parameter set, and/or based on the data indicative of the equipment setup, determining for each seeding parameter whether it is a level 1 parameter, level 2 parameter, or level 3 parameter, (step 5) if at least one level 1 parameter is present, generating a seeding logic for level 1 parameter(s), (step 6) if at least one level 2 parameter is present, generating a seeding logic for level 2 parameter(s), based on the seeding logic for level 1 parameter(s), (step 7) if at least one level 3 parameter is present, generating a seeding logic for level 3 parameter(s), based on the seeding logic for level 1 parameter(s) and on the seeding logic for level 2 parameter(s), (step 8) outputting the seeding rate and/or the seeding depth based on the determined seeding logics.
A computer-implemented method for applying a product on an agricultural field, comprising the steps: receiving a control signal to start an adaptation of a product rate and/or of a frequency during a current application of the product on the agricultural field (S10); continuously determining a current amount of the product in a tank of an application device for applying the product (S20); continuously determining a current position of the application device in a route through the agricultural field (S30); continuously adapting the product rate and/or the frequency based on the current position of the application device in the route through the agricultural field and the current amount of the product in the tank of the application device such that at the end of the route of the application device through the agricultural field a predetermined amount of the product is in the tank (S40).
B05B 12/08 - Aménagements de commande de la distributionAménagements de réglage de l’aire de pulvérisation sensibles à l'état du liquide ou d'un autre matériau fluide expulsé, du milieu ambiant ou de la cible
56.
COMPUTER-IMPLEMENTED METHOD FOR APPLYING A PRODUCT ON AN AGRICULTURAL FIELD
A computer-implemented method for applying a product on an agricultural field, comprising the steps: receiving a control signal to start an adaptation of a product rate and/or of a frequency during a current application of the product on the agricultural field (S10); continuously determining a current amount of the product in a tank of an application device for applying the product based on a treatment savings parameter (S20); continuously determining a current position of the application device in a route through the agricultural field (S30); continuously adapting the product rate and/or the frequency based on the current position of the application device in the route through the agricultural field and the current amount of the product in the tank of the application device such that at the end of the route of the application device through the agricultural field a predetermined amount of the product is in the tank (S40).
A01M 7/00 - Adaptations ou aménagements particuliers des appareils de pulvérisation de liquides aux fins couvertes dans la présente sous-classe
B05B 12/08 - Aménagements de commande de la distributionAménagements de réglage de l’aire de pulvérisation sensibles à l'état du liquide ou d'un autre matériau fluide expulsé, du milieu ambiant ou de la cible
B05B 12/12 - Aménagements de commande de la distributionAménagements de réglage de l’aire de pulvérisation sensibles à l'état du liquide ou d'un autre matériau fluide expulsé, du milieu ambiant ou de la cible sensibles à l'état du milieu ambiant ou de la cible, p. ex. à l'humidité, à la température
A01C 23/00 - Dispositifs distributeurs spécialement adaptés pour répandre le purin ou d'autres engrais liquides, y compris l'ammoniaque, p. ex. réservoirs de transport ou voitures arroseuses
Method for generating a control signal for a smart spraying device with at least one individual spray nozzle, and a method for controlling a smart spraying device, using PWM and field data relating to a vegetative indicator for providing an improved adaptive application of products onto an area to be treated.
Method for providing control data for an application vehicle (12) when selectively applying pesticides, comprising the following steps: providing (S10) location data of sub-areas (13, 14) of a field (10) to which at least one first pesticide has been applied in a first application; determining (S20) transition areas (20) associated with the sub-areas (13, 14) to which the at least one first pesticide has been applied and the adjacent areas to which the at least one first pesticide has not been applied, based on the provided sub-area location data; providing (S30) control data for controlling an application vehicle (12) for applying at least one second pesticide to the determined transition areas (20) in a second application.
The present invention relates to a system and a method for operating a treatment device (120) applying a treatment product to an agricultural area, the method comprising: obtaining (S210) at least one dataset of an area of interest within the agricultural area (110) to a control system (12.10); determining (S220), by the control system (12.10), from the at least one dataset a plant indicator, wherein a basic threshold for triggering application of the treatment product is dynamically adjustable in relation to the plant indicator; and providing a control signal (S230), by the control system, to control the treatment device (120) based on the determined plant indicator and the threshold for triggering application of the treatment product.
A01M 21/04 - Appareils pour destruction par la vapeur, les produits chimiques, le feu ou l'électricité
G05B 19/4155 - Commande numérique [CN], c.-à-d. machines fonctionnant automatiquement, en particulier machines-outils, p. ex. dans un milieu de fabrication industriel, afin d'effectuer un positionnement, un mouvement ou des actions coordonnées au moyen de données d'un programme sous forme numérique caractérisée par le déroulement du programme, c.-à-d. le déroulement d'un programme de pièce ou le déroulement d'une fonction machine, p. ex. choix d'un programme
The present invention relates to a system and method for operating a treatment device applying a treatment product to an agricultural area, the method comprising: obtaining (S210) at least one dataset relating to an area of interest within the agricultural area (110) to a control system (12.10); determining (S220), by the control system (12.10), from the at least one dataset a vegetative indicator relating to real-time conditions on the agricultural area (110), wherein a basic threshold for triggering application of the treatment product is dynamically adjustable in relation to the vegetative indicator; and providing (S230) a control signal, by the control system (12.10), to control the treatment device (120) based on the determined vegetative indicator and the threshold for triggering application of the treatment product.
A01M 7/00 - Adaptations ou aménagements particuliers des appareils de pulvérisation de liquides aux fins couvertes dans la présente sous-classe
A01M 21/04 - Appareils pour destruction par la vapeur, les produits chimiques, le feu ou l'électricité
61.
COMPUTER IMPLEMENTED METHOD FOR PROVIDING TEST DESIGN AND TEST INSTRUCTION DATA FOR COMPARATIVE TESTS ON YIELD, GROSS MARGIN, EFFICACY OR VEGETATION INDICES FOR AT LEAST TWO PRODUCTS OR DIFFERENT APPLICATION TIMINGS OF THE SAME PRODUCT
Computer implemented method for providing test design and test instruction data for comparative tests for yield and/or gross margin, efficacy and/or effects on certain vegetation indices on a field for at least a first product and a second product having a similar area of use.
G06Q 10/0637 - Gestion ou analyse stratégiques, p. ex. définition d’un objectif ou d’une cible pour une organisationPlanification des actions en fonction des objectifsAnalyse ou évaluation de l’efficacité des objectifs
COMPUTER IMPLEMENTED METHOD FOR PROVIDING TEST DESIGN AND TEST INSTRUCTION DATA FOR COMPARATIVE TESTS FOR YIELD, GROSS MARGIN, EFFICACY AND/OR EFFECTS ON VEGETATION INDICES ON A FIELD FOR DIFFERENT RATES OR APPLICATION MODES OF ONE PRODUCT
Computer implemented method for providing test design and test instruction data for comparative tests for yield and/or gross margin, efficacy and/or effects on certain vegetation indices on a field for one product comprising the following steps: providing field data (S10) comprising at least biomass distribution data and geographic data about the field on which the comparative tests are to be performed; providing test data (S20) comprising at least product use rate data about different constant product use rates of said product, and/or different variable product use rates of said product at a single application time or a sequence of application times of said product whose effect, e.g. on yield, are to be compared by the comparative tests for yield and/or gross margin, efficacy and/or effects on certain vegetation indices; generating test design data (S30) based on the provided geographic data by segmenting the field in plots and/or strips; generating test instruction data (S40) by specifying at least two plots and/or at least two strips having comparable biomass data and assigning different use rates and/or application timings of said product to these at least two plots and/or at least two strips.
G06Q 10/0637 - Gestion ou analyse stratégiques, p. ex. définition d’un objectif ou d’une cible pour une organisationPlanification des actions en fonction des objectifsAnalyse ou évaluation de l’efficacité des objectifs
REAL-TIME FERTILIZATION AND/OR CROP PROTECTION DECISION MAKING BASED ON SOIL-, CROP, FIELD- AND WEATHER-RELATED DATA WHEREIN THE SOIL-RELATED DATA ARE OBTAINED BY A SOIL SENSOR
A computer-implemented method for controlling an agricultural treatment device (200) in an agricultural field, the method comprising the following steps: (a) receiving by the computing unit (120) soil-related data relating to the sub-field zone (G1), wherein the soil-related data are obtained by real-time measurements using a soil sensor (110) and wherein (G1) which is located within the agricultural field, (b) receiving by the computing unit (120)—from a database (130) and/or from real-time measurements—crop-related data, field-related data, and weather-related data relating to the sub-field zone (G1), (c) determining via a computing unit (120)—based on the soil-related data, crop-related data, field-related data and optionally weather-related data—at least one indicator indicative of the crop protection demand and/or crop nutrition demand relating to the sub-field zone (G1), (d) dynamically generating via the computing unit (120), an output signal (140) dependent from the determined at least one indicator, wherein the output signal (140) is generated during real-time operation of the agricultural treatment device (200) and is usable for controlling the agricultural treatment device (200) at the sub-field zone (G1).
A computer-implemented method (1) for cross-account model deployment is provided. According to the method, at least one artifact identifier (3) of at least one to-be-deployed model artifact (m) is provided. Further, at least one account tuple (4), the account tuple (4) comprising a source account identifier identifying a source account (s1; s2) and a target account identifier identifying a target account (t1; t2; t3; t4), is provided. Finally, the at least one to-be-deployed model artifact (m) is moved from the source account (s1; s2) to the target account (t1; t2; t3; t4). Further, a computer program element and a computer readable medium are provided.
A computer-implemented method for estimating a product consumption of an agricultural product for an agricultural field, comprising the steps: providing a target application rate map (10) comprising an application rate distribution of the agricultural product of the agricultural field, wherein the application rate distribution comprises different target areas with predetermined 5 application rates of the agricultural product (S100); providing a route (34, 52) of an agricultural vehicle and/or an application device of the agricultural vehicle through the agricultural field for applying the agricultural product (S200); providing a working width (37, 52) of the agricultural vehicle and/or the application device of the agricultural vehicle; determining application rates of the agricultural vehicle and/or the application device of the agricultural vehicle at least based on 10 the route (34, 52) of the agricultural vehicle and/or the application device of the agricultural vehicle through the agricultural field (S300); determining the product consumption for the agricultural field based on the determined application rates and the working width (37, 53) of the agricultural vehicle and/or the application device of the agricultural vehicle (S400). 15
A spray device (20) with more than one spray nozzle (28) to treat an agricultural area (11), wherein at least two spray nozzles (28) are fluidly connected via a common fluidic line (26), the spray device (20) including: - a control system (32) configured to control activation of individual spray nozzle(s) based on an activation signal, - a sensor (34) configured to measure a fluid property in the common fluidic line (26), - a monitoring unit configured to monitor spray nozzles (28) based on the measured fluid property and the activation signal.
The present teachings relate to a method for validating an agricultural farming operation prior to and/or during executing an agricultural farming operation at a geographical location using a machine, the machine being operatively coupled to a computing unit, which method comprises: —providing to the computing unit one or more signals retrieved from the machine; the one or more signals being indicative of one or more parameters related to the machine and/or to the farming operation; —determining, via the computing unit, whether any one or more of the parameters related to the machine and/or to the farming operation lie within an acceptable range or value, which acceptable range or value is specified using field specific data that are provided at a memory storage operatively coupled to the computing unit; and—generating, via the computing unit, an output signal in response to the determination; wherein the output signal is usable for validating and/or specifying the farming operation to monitor and/or control the machine. The teachings also relate to a machine, a software product and a computing unit.
G07C 5/08 - Enregistrement ou indication de données de marche autres que le temps de circulation, de fonctionnement, d'arrêt ou d'attente, avec ou sans enregistrement des temps de circulation, de fonctionnement, d'arrêt ou d'attente
68.
HYBRID MODEL TO OPTIMIZE THE FUNGICIDE APPLICATION SCHEDULE
The present invention relates to fungal disease management. In order to improve fungal disease management, a computer-implemented method (200) is provided for determining a disease progression usable for fungicide spray schedule on an agricultural field. The method comprising the step of receiving (210) data including crop variety data, environmental data, crop management data, and location data of the agricultural field. The crop variety data relates to a crop grown or to be grown on an agricultural field. The environmental data is indicative of an environmental condition for the agricultural field. The crop management data is indicative of fungicide spray history for the agricultural field. The method further comprises the step of applying (220) a machine-learning model to the received data to determine disease progression time-series data of a fungal disease, wherein the machine-learning model has been trained to learn the disease progression under a condition defined by crop variety data, environmental data, crop management data, and location data based on historic data collected from one or more agricultural fields. The method further comprises the step of determining (230), based on the determined disease progression time-series data, a disease onset date of the fungal disease.
A computer-implemented method for seeding, planting and/or fertilizing, comprising: providing (S100) soil data by transmitting via transmitting means at least one transmitting signal into at least a part of a soil and by receiving via receiving means a response signal from the soil; providing (S200) configuration parameters of the transmitting means and receiving means; determining (S300) a soil structure of the soil by a subsurface model based on the soil data and the configuration parameters; determining (S400) a seeding, planting and/or fertilizing depth in the determined soil structure; and providing (S500) at least one seed (30), plant and/or fertilizer (31) in the determined seeding, planting and/or fertilizing depth in the soil.
A computer-implemented method for providing variable application rate data for at least two application means of an application system of an application device for applying a treatment product onto an agricultural field, comprising: providing application configuration parameter data for the at least two application means of the application system of the application device; providing position and/or movement data of the application device; providing product application data comprising spatial information about the target application amount for the treatment product to be applied onto the agricultural field; generating a grid of application polygons; and determining variable application rate data for each of the at least two application means of the application system of the application device based on the application configuration parameter data, the position and/or movement data, and the product application data, wherein the variable application rate data is determined for each application polygon in the generated grid.
The present invention relates to an plant disease detection at onset stage. Provided is a computer-implemented method for determining an onset and/or onset time of a plant (12) disease in agriculture. The method comprises providing (S110) first data including field data (14) associated with the plant's cultivation and weather data (16) associated with a location where said plant is cultivated to a computer model (20). The method further comprises determining (S120), by using said computer model (20), a plant disease presence prediction for said plant and its infestation with said plant disease and determining, from said computer model (20) output including said plant disease presence prediction and second data (18) including one or more vegetation indices associated with said plant, the onset and/or onset time to which said plant disease is expected to onset at said plant, by using the plant disease presence prediction and a change in the one or more vegetation indices.
The present invention relates to the autonomous application of crop protection products by means of a drone. The present invention relates to a process and to an unmanned aerial vehicle for applying crop protection product taking into consideration drift phenomena. The present invention furthermore relates to a computer program product which can be employed for controlling the process according to the invention.
B64D 1/18 - Largage en vol d'une matière poudreuse, liquide ou gazeuse, p. ex. pour la lutte contre l'incendie par pulvérisation, p. ex. d'insecticides
A01M 7/00 - Adaptations ou aménagements particuliers des appareils de pulvérisation de liquides aux fins couvertes dans la présente sous-classe
A01M 11/00 - Adaptations ou aménagements particuliers des appareils combinés de pulvérisation de liquides et de poudrage aux fins couvertes dans la présente sous-classe
B64C 39/02 - Aéronefs non prévus ailleurs caractérisés par un emploi spécial
B64D 1/16 - Largage en vol d'une matière poudreuse, liquide ou gazeuse, p. ex. pour la lutte contre l'incendie
B64U 101/00 - Véhicules aériens sans pilote spécialement adaptés à des utilisations ou à des applications spécifiques
B64U 101/40 - Véhicules aériens sans pilote spécialement adaptés à des utilisations ou à des applications spécifiques à l’agriculture ou à la sylviculture
G05D 1/00 - Commande de la position, du cap, de l'altitude ou de l'attitude des véhicules terrestres, aquatiques, aériens ou spatiaux, p. ex. utilisant des pilotes automatiques
73.
METHOD FOR AN "ON-THE-FLY" TREATMENT OF AN AGRICULTURAL FIELD USING A SOIL SENSOR
The present invention relates to a method for treatment of an agricultural field, the method comprising the steps: 1) receiving (S10) a parametrization (10) for controlling a treatment device (200) by the treatment device (200) from a field manager system (100); 2) receiving (S20) from at least one soil sensor (400) real-time soil information on the real-world situation of the geographical location G1 in the agricultural field; 3) processing (S30) the real-time soil information to generate processed information (30), 4) determining (S40) a control signal (50) for controlling a treatment arrangement (270) of the treatment device (200) based on the received parametrization (10) and the processed information (30), 5) executing (S50) a treatment on the geographical location G2 in the agricultural field, wherein the treatment is executed based on the control signal (50) real-time after receiving the real-time soil information in such a way that the distance between location G1 and location G2 does not exceed 100 meters.
G01N 21/3563 - CouleurPropriétés spectrales, c.-à-d. comparaison de l'effet du matériau sur la lumière pour plusieurs longueurs d'ondes ou plusieurs bandes de longueurs d'ondes différentes en recherchant l'effet relatif du matériau pour les longueurs d'ondes caractéristiques d'éléments ou de molécules spécifiques, p. ex. spectrométrie d'absorption atomique en utilisant la lumière infrarouge pour l'analyse de solidesPréparation des échantillons à cet effet
74.
METHOD FOR DETERMINING A TREATMENT SCHEDULE BASED ON THE MATCHING WITH THE USER PREFERENCE
A method is provided, comprising providing crop data (10), (S20) proving field data (20), (S30) providing organism data (30), (S40) providing agricultural inputs data (40), (S50) providing user preference data (50), (S60) based on the crop data (10), field data (20), organism data (30), and agricultural inputs data (40), determining a set of treatment schedules for treating the agricultural field, (S70) determining the crop yield and the environmental impact associated with each treatment schedule within the determined set of treatment schedules, (S80) based on the user preference data (50), determining a user preference matching indicator for each treatment schedule within the determined set of treatment schedules using a matching model, (590) selecting at least one treatment schedule based on the user preference matching indicator, (S100) based on the selected treatment schedule, outputting a control file usable for controlling an agricultural equipment which can be used to treat the agricultural field.
A computer-implemented method for generating a soil property map of an agricultural field, comprising the steps: receiving crop property distribution data of the agricultural field comprising at least one crop related parameter (S1); determining equivalent areas having a crop related parameter value within a certain range in the crop property distribution data (S2); receiving soil data with respect to at least one soil parameter for each of the determined equivalent areas (S3); generating a soil property map of the agricultural field based on the soil data and the equivalent areas (S4).
A method is provided, comprising providing crop data (10), (S20) proving field data (20), (S30) providing organism data (30), (S40) providing agricultural inputs data (40), (S50) providing field potential data (50), (S60) based on the crop data (10), field data (20), organism data (30), and agricultural inputs data (40), determining a set of treatment schedules for treating the agricul-tural field, (S70) determining the crop yield and the environmental impact associated with each treatment schedule within the determined set of treatment schedules, (S80) based on the field potential data (50), determining a field potential matching indicator for each treatment schedule within the determined set of treatment schedules using a matching model, (S90) selecting at least one treatment schedule based on the field potential matching indicator,(S100) based on the selected treatment schedule, outputting a control file usable for controlling an agricultural equipment which can be used to treat the agricultural field.
The present invention relates to a method for enabling a computer model to predict a crop disease in agriculture, the method comprising: training (S210) a first computer model (113A) to be enabled for predicting a first crop disease by feeding a first training data set (113A-1) having a first data size into the first computer model (113A); generating (S220) a second computer model (113B) to be enabled for predicting a second crop disease being different to the first crop disease; and training (S230) the second computer model (113B) by feeding a second training data set (113B-1) having a second data size smaller than the first data size of the first training data set (113A-1) into the second computer model (113B); wherein the generating and/or the training of the second computer model (113B) uses knowledge being obtained from the first computer model (113A) trained with the first training data set (113A-1).
G06V 10/80 - Fusion, c.-à-d. combinaison des données de diverses sources au niveau du capteur, du prétraitement, de l’extraction des caractéristiques ou de la classification
G06V 10/82 - Dispositions pour la reconnaissance ou la compréhension d’images ou de vidéos utilisant la reconnaissance de formes ou l’apprentissage automatique utilisant les réseaux neuronaux
A computer-implemented method for monitoring the treatment of an agricultural field (11) with a pesticide product by an agricultural machine (10), wherein the agricultural machine (10) comprises at least one sensor device (31) and at least one treatment component comprising at least one nozzle (21), the method comprising the steps: providing (40) location-specific sensor data of the agricultural field (10) from the at least one sensor device (31); analyzing (42) the location-specific sensor data with respect to at least one harmful organism as one treatment indicator; generating location-specific control data for the at least one treatment component based on the analyzed location¬ specific sensor data; providing (44) a pesticide savings parameter in real-time, wherein the pesticide savings parameter relates to an amount of pesticide product based on the location-specific sensor data in relation to an amount of pesticide product based on a reference treatment with a pesticide product.
A method for modifying a treatment performance for treating an agricultural field by an agricultural machine, whereas the method comprises a modification function and the agricultural machine comprises at least one treatment component, characterized in that the method having the steps of - Obtaining field data (S1, S10); - Determining a treatment performance by analyzing the field data (S2, S12); - Providing a treatment performance modification via the modification function and a representation parameter (S3, S13); - Modifying the treatment performance with the treatment performance modification (S4, S15).
The present application provides a method for determining a plant protection treatment plan of an agricultural plant, the method carried out by a data processing unit (111), and the method comprising the steps of: obtaining (S110), by the data processing unit, plant observation data indicative for a current state of health of the agricultural plant or of a reference plant, obtaining (S120), by the data processing unit, weather data associated with a location at which the agricultural plant is cultivated, predicting (S130), by a computational model (113) executed by the data processing unit, based on the obtained observation data and the obtained weather data, a time-related disease probability of the agricultural plant, and determining (S140), by the computational model (113), based on at least the predicted disease probability, at least one plant protection treatment parameter to be included in the plant protection treatment plan.
G06Q 10/04 - Prévision ou optimisation spécialement adaptées à des fins administratives ou de gestion, p. ex. programmation linéaire ou "problème d’optimisation des stocks"
G06Q 10/06 - Ressources, gestion de tâches, des ressources humaines ou de projetsPlanification d’entreprise ou d’organisationModélisation d’entreprise ou d’organisation
81.
COMPUTER-IMPLEMENTED METHOD FOR EVALUATING APPLICATION THRESHOLD VALUES FOR AN APPLICATION OF A PRODUCT ON AN AGRICULTURAL FIELD
Computer-implemented method for evaluating application threshold values for an application of an agricultural product on an agricultural field, comprising the steps: providing field data comprising geographic data about an agricultural field; segmenting at least a part of the agricultural field in sections and assigning different application threshold values for the agricultural product to different sections; applying the agricultural product on the sections according to the assigned application threshold values for the agricultural product; obtaining evaluation data for the different sections representing the effectiveness of the treatment with the different application threshold values; evaluating the different application threshold values at least based on the evaluation data (e.g. efficacy and or yields).
A method for generating a crop failure map is provided. The method comprises providing annotated training data, the annotated training data comprising aerial images of zones of an agricultural field and the annotations relating to failures of the crops within the agricultural field, the crops being perennial crops. The method further comprises training an artificial intelligence with the annotated training data and providing field data, the field data comprising at least one aerial image of an agricultural field to be inspected. The trained artificial intelligence is run on the field data to generate a crop failure map. Also, a system for generating a crop failure map and a use of a crop failure map are provided.
G06K 9/62 - Méthodes ou dispositions pour la reconnaissance utilisant des moyens électroniques
83.
COMPUTER-IMPLEMENTED METHOD FOR PROVIDING OPERATION DATA FOR TREATMENT DEVICES ON AN AGRICULTURAL FIELD, CORRESPONDING SYSTEMS, USE AND COMPUTER ELEMENT
A computer-implemented method for providing operation data for treatment devices (102, 104, 106, 107) on an agricultural field (112), wherein the treatment devices include at least a first treatment device (102) and a second treatment device (104) for treating the agricultural field, the method comprising the steps: obtaining field data for at least one section (113) of the agricultural field at least by the first treatment device, wherein the obtained field data indicates a monitoring and treatment status associated with the at least one section; based on the monitoring and treatment status associated with the at least one section providing operation data associated with the at least one section of the agricultural field for the second treatment device.
A01B 69/00 - Direction des machines ou instruments agricolesGuidage des machines ou instruments agricoles selon un parcours déterminé
G05D 1/10 - Commande de la position ou du cap dans les trois dimensions simultanément
B64D 1/18 - Largage en vol d'une matière poudreuse, liquide ou gazeuse, p. ex. pour la lutte contre l'incendie par pulvérisation, p. ex. d'insecticides
A01M 21/04 - Appareils pour destruction par la vapeur, les produits chimiques, le feu ou l'électricité
B64C 39/02 - Aéronefs non prévus ailleurs caractérisés par un emploi spécial
A computer-implemented method for providing operation data for treatment devices for treating an agricultural field (14), wherein the treatment devices include at least a first treatment device (21) and a second treatment device (22) for treating the agricultural field (14), the method comprising the steps: obtaining field data for at least one section of the agricultural field at least from the first treatment device; based on the field data associated with the at least one section providing selection data for selecting the second treatment device associated with the at least one section of the agricultural field.
The present disclosure relates to a multi-device treatment of an agricultural field. Proposed is a computer-implemented method for treating an agricultural field (10). The method comprises the step of analyzing field data (S120), monitored as a first treatment device (100) traverses the field (SI 10), to determine whether the field (10) at a certain field location (10a) has a field condition which is treatable with a first device configuration of the first treatment device (100). If it is determined that the field at the certain field location associated with the respective field condition is treatable with the first device configuration of the first treatment device (100), the first treatment device (100) is controlled (S130) to treat the field at the certain field location (10a). Otherwise, if it is determined that the field at the certain field location (10a) associated with the respective field condition is not treatable with the first device configuration of the first treatment device (100), the certain field location (10a) is provided (S140) for at least one further, second treatment device (200) having a second device configuration that is different to the first device configuration and capable of treating the field at the certain field location.
The present disclosure relates to targeted treatment of specific weed species with multiple treatment devices. Provided is a system and computer-implemented method for controlling operation of multiple treatment devices (102, 103, 104, 107) having treatment device configurations different to each other for treating an agricultural field (112). The method comprises analyzing field data, monitored as at least one of the multiple treatment devices (102, 103, 104, 107) traverses the field, to identify weed present at a certain field location (113) of the field (112) by weed species; and targeted instructing at least one treatment device (102, 103, 104, 107) among the multiple treatment devices (102, 103, 104, 107) that has a matching treatment device configuration for an identified weed species to treat the field (112) at the corresponding certain field location (113) against the identified weed species.
A method for determining a sowing row direction (4) is provided. The method comprises providing location data for at least one location of interest (5), the at least one location of interest (5) being located within an agricultural field (2). The method further comprises providing a sowing row information map (1; 6; 8), the sowing row information map (1; 6; 8) comprising information on the sowing rows (7) of the agricultural field (2) with a sub-field resolution. Finally, the method comprises identifying the sowing row direction (4) at the at least one location of interest (5) based on the sowing row information map (1; 6; 8). Further, a system (14) for determining a sowing row direction (4), a computer program element, a computer readable medium and a system (10) for generating a processed sowing row information map (6; 8) from a sowing track map (1) are provided.
In order to improve early emergence stages discrimination of plants, an early emergence app is proposed based on a neural network optionally with ‘attention’ mechanisms, which detects the number of plants that opened after sowing and are present in the field. This way the farmer can easily determine, if crop density targets are met at an early stage after sowing and optionally receive recommendations on catch crop.
G06K 9/00 - Méthodes ou dispositions pour la lecture ou la reconnaissance de caractères imprimés ou écrits ou pour la reconnaissance de formes, p.ex. d'empreintes digitales
G06V 10/774 - Génération d'ensembles de motifs de formationTraitement des caractéristiques d’images ou de vidéos dans les espaces de caractéristiquesDispositions pour la reconnaissance ou la compréhension d’images ou de vidéos utilisant la reconnaissance de formes ou l’apprentissage automatique utilisant l’intégration et la réduction de données, p. ex. analyse en composantes principales [PCA] ou analyse en composantes indépendantes [ ICA] ou cartes auto-organisatrices [SOM]Séparation aveugle de source méthodes de Bootstrap, p. ex. "bagging” ou “boosting”
G06V 10/82 - Dispositions pour la reconnaissance ou la compréhension d’images ou de vidéos utilisant la reconnaissance de formes ou l’apprentissage automatique utilisant les réseaux neuronaux
A computer-implemented method for generating an application map for treating a field with an agricultural equipment comprising the following steps: a) providing a land cover map relating to a field to be treated; b) receiving master data selected from the group consisting of: regulatory data, machine data, field data, elevation data, c) optionally initiating the determination of, and/or determining preliminary buffer zones as a further layer to the land cover map based on the master data, d) optionally receiving validation information selected from the group consisting of: (i) validation information relating to the field to be treated or relating to the land cover map, (ii) validation information relating to master data selected from the group consisting of regulatory data, machine data, field data, elevation data, and (iii) validation information relating to preliminary buffer zones, e) initiating the determination of, and/or determining buffer zones as a further layer to the land cover map based on the master data and—optionally—based on the validation information; and f) initiating the generation of, and/or generating an application map specifying areas for treating the field with an agricultural equipment, wherein the application map is based on the buffer zones.
METHOD FOR DETERMINING A RANKING OF TREATMENT PARAMETERS (SUCH AS CROP PROTECTION PRODUCTS) FOR TREATING AN AGRICULTURAL FIELD VIA AN EFFICACY ADJUSTMENT MODEL BASED ON GENETIC DATA
A computer-implemented method for generating a control file usable for controlling an agricultural equipment based on at least one treatment parameter, comprising the following steps: (step 1) (110) providing genetic measurement data (40) of at least one organism which existed or is existing or is expected to exist in the agricultural field, (step 2) (120) providing treatment parameter data (42) for at least two treatment parameters capable of targeting the at least one organism, (step 3) (130) based on the treatment parameter data (42), providing first level efficacy data (44) comprising efficacies ("first level efficacies") of the at least two treatment parameters relating to the at least one organism on a first level of the taxonomic rank, (step 4) (140) based on the treatment parameter data (42) and the first level efficacy data (44), determining a first ranking (46) of the at least two treatment parameters, (step 5) (150) providing an efficacy adjustment model (50), (step 6) (160) by modifying the first level efficacy data (44) based on the genetic measurement data (40) and the treatment parameter data (42) via the efficacy adjustment model (50), obtaining second level efficacy data (52) comprising efficacies ("second level efficacies") of the at least two treatment parameters relating to the at least one organism on a second level of the taxonomic rank being below the first level of the taxonomic rank, (step 7) (170) based on the treatment parameter data (42) and the second level efficacy data (52), determining a second ranking (54) of the at least two treatment parameters, (step 8) (180) outputting the highest ranked or user-selected treatment parameter as a control file usable for controlling an agricultural equipment
G06Q 10/06 - Ressources, gestion de tâches, des ressources humaines ou de projetsPlanification d’entreprise ou d’organisationModélisation d’entreprise ou d’organisation
A method for generating a zone specific application map (8) for treating an agricultural field with products is provided. The method comprises providing a hypermodel (1) comprising a product recommendation model, PRM (2) and a biophysical parameter model, BPM (3). The method further comprises providing PRM input parameters (4) for the product recommendation model (2) and generating PRM output (5) by the product recommendation model (2). The method also comprises providing BPM input parameters (6) for the biophysical parameter model (3) and generating BPM output (7) by the biophysical parameter model (3). Finally, the method comprises generating the zone specific application map (8) by the hypermodel (1), using at least parts of the PRM output (5) and parts of the BPM output (7). Further, a system (19) for generating a zone specific application map (8), a computer program element, a use of a zone specific application map (8) and an agricultural equipment (23) are provided.
G06Q 10/06 - Ressources, gestion de tâches, des ressources humaines ou de projetsPlanification d’entreprise ou d’organisationModélisation d’entreprise ou d’organisation
Computer-implemented method for providing corrected plant-related index data, comprising the steps of: providing initial plant-related index data for an agricultural field, preferably based on at least one satellite image; and correcting the initial plant-related index data for the agricultural field at least based on historical plant-related index data for the agricultural field and providing corrected plant-related index data.
A method for generating a crop phenology prediction (7) is provided. The method comprises the steps of providing crop phenology training data (1) for a plurality of crops and a plurality of locations (17); training a machine learning system (6) using the crop phenology training data (1); providing a selection of the plurality of crops and a specific location (18); and generating a crop phenology prediction (7) for a selection of the plurality of crops at a specific location (18) using the trained machine learning system (6). Further, a system (21) for generating a crop phenology prediction (7) is provided. The system (21) comprises at least one input interface (25) for providing a selection of crops and a specific location (18), at least one processing unit (22) configured to carry out the method for generating a crop phenology prediction (7) and at least one output interface (23) for outputting the crop phenology prediction (7), the agronomic recommendation (8) and/or the agronomic control data (26) for the selection of crops at the specific location (18). Further, a computer program element, a use of a crop phenology prediction (7) and a use of agronomic control data (26) are provided.
G06Q 10/04 - Prévision ou optimisation spécialement adaptées à des fins administratives ou de gestion, p. ex. programmation linéaire ou "problème d’optimisation des stocks"
94.
COMPUTER-IMPLEMENTED METHOD FOR GENERATING THE VALIDITY FOR AGRICULTURAL PURPOSES OF AN IMAGE
A computer-implemented method is provided for generating an image classification index (10), the image classification index (10) being a measure of validity of an image (7) for agricultural purposes. According to the method, an image (7) taken by a camera (4) is provided; location data (9) pertaining to the image (7) is provided and geographic data (11) for at least one geographical area (16) is provided. Further, an image classification index (10) is generated based on the location data (9) and the geographic data (11). Then, the image classification index (10) is allocated to the image (7). Moreover, a computing device for generating an image classification index (10), a computer program element, a system (1) for generating an image classification index (10) and a use of an agronomic recommendation and/or of agronomic control data are provided.
A method for generating a control file to operate a treatment device (10) on an agricultural area (11) to be treated, the method comprising the steps of: providing (40) treatment data signifying a type of treatment to be conducted by the treatment device and crop data relating to a crop present on the agricultural area (11) to be treated to a preparation system (13); determining (42), by the preparation system (13), from the treatment data and the crop data at least one operation parameter, wherein the determined operation parameter is related to a real-time and/or location-specific condition to be monitored during treatment; generating (44), by the preparation system (13), a control file comprising the at least one operation parameter, the generated control file usable to operate the treatment device (10) based on the real-time and/or location-specific condition to be monitored during treatment.
Method for generating an application map (20) for treating a field with an agricultural equipment comprising the following steps: providing (S10) a field map (10) of a field to be treated; determining (S20) areas in the field map (10) with a weed and/or pest infestation by using an image classification algorithm; and generating (S30) an application map (20) specifying areas for treating the field with an agricultural equipment, wherein the application map (20) is based on the determined areas infested by weed and/or pest infestation; wherein the method further comprises the step of providing boundary data with respect to the field.
G06V 10/70 - Dispositions pour la reconnaissance ou la compréhension d’images ou de vidéos utilisant la reconnaissance de formes ou l’apprentissage automatique
Method for plantation treatment of a plantation field, the method comprising taking an image of a plantation of a plantation field; recognizing items on the taken image by running a first image recognition analysis of a first complexity on the taken image based on a stored parametrization of a machine learning algorithm; identifying an unsatisfying image analysis result; determining ambient data corresponding to the taken image; recognizing items on the taken image by miming a second image recognition analysis of a second complexity on the image based on the ambient data on an external device, wherein the second complexity is higher than the first complexity; determining an improved parametrization based on the second image recognition analysis for the machine learning algorithm for improving the first image recognition analysis; and controlling a treatment arrangement of a treatment device based on the first image recognition analysis.
G06V 10/776 - ValidationÉvaluation des performances
G06V 20/17 - Scènes terrestres transmises par des avions ou des drones
A01M 7/00 - Adaptations ou aménagements particuliers des appareils de pulvérisation de liquides aux fins couvertes dans la présente sous-classe
A01M 21/04 - Appareils pour destruction par la vapeur, les produits chimiques, le feu ou l'électricité
G05B 19/4155 - Commande numérique [CN], c.-à-d. machines fonctionnant automatiquement, en particulier machines-outils, p. ex. dans un milieu de fabrication industriel, afin d'effectuer un positionnement, un mouvement ou des actions coordonnées au moyen de données d'un programme sous forme numérique caractérisée par le déroulement du programme, c.-à-d. le déroulement d'un programme de pièce ou le déroulement d'une fonction machine, p. ex. choix d'un programme
A method for correcting remote sensor data of an agricultural field, the method comprising the following steps: receiving remote sensor data (DR) for the agricultural field from a remote sensor, wherein the remote sensor data (DR) comprises at least one remote measurement value corresponding to at least one location that is measured by the remote sensor at at least one point in time of obtaining the remote measurement value; receiving local sensor data (DL) for the agricultural field from at least one local sensor, wherein the at least one local sensor data (DL) comprises at least one local measurement value corresponding to at least one location of the at least one local sensor and corresponding to at least one point in time of obtaining the local measurement value correlating to the location and point of time of obtaining the remote measurement value; determining a correction model based on the previously received local sensor data (DL) and the previously received remote sensor data (DR); and determining corrected current remote sensor data (DRP, DRPR) by applying the correction model to current remote sensor data.
G06T 5/20 - Amélioration ou restauration d'image utilisant des opérateurs locaux
G01N 21/3563 - CouleurPropriétés spectrales, c.-à-d. comparaison de l'effet du matériau sur la lumière pour plusieurs longueurs d'ondes ou plusieurs bandes de longueurs d'ondes différentes en recherchant l'effet relatif du matériau pour les longueurs d'ondes caractéristiques d'éléments ou de molécules spécifiques, p. ex. spectrométrie d'absorption atomique en utilisant la lumière infrarouge pour l'analyse de solidesPréparation des échantillons à cet effet
G01N 33/00 - Recherche ou analyse des matériaux par des méthodes spécifiques non couvertes par les groupes
99.
METHOD FOR DETERMINING AND PROVIDING AN APPLICATION SCHEME FOR PESTICIDES
Method for determining and providing an application scheme for pesticides, comprising the following steps: providing agricultural crop data (S10) comprising information about an agricultural crop species sown or planned to sow in a field; providing application time data (S20) comprising information about at least one planned application time of applying pesticides in the field; providing weed and/or pathogen specifier data (S30) comprising information about at least one weed or pathogen specifier present or expected in the field; performing a database search in a pesticide product database (S50) at least based on the provided agricultural crop data, the provided application time data, the provided weed and/or pathogen specifier data and determining the pesticide products matching the agricultural crop and the application time and one of the weed or pathogen specifiers, wherein the pesticide product database comprises information about a plurality of pesticide products, and wherein the information about the pesticide products comprises at least information about an application area of each pesticide product, an active ingredient of each pesticide product and a suitable application time for each pesticide product; ranking generated application schemes (S70), wherein the different application schemes (S60) are generated by combining the determined pesticide products in such a way that all weeds and/or pathogen indicators are covered; wherein the step of ranking is based on one or more of the following statistics per application scheme: the number of pesticides per application scheme; the number of leading or priority weeds and/or pathogens covered with an efficacy above a defined threshold, wherein in an example, the defined threshold is above 80% and wherein in an example this defined threshold represents the most weighted statistic per application scheme; the number of all weeds and/or pathogens covered with a efficacy above a defined thresholds; maximum efficacy achieved across weeds and/or pathogens by a given pesticide; the fraction of pesticides in the application scheme of known preferred usage at the time of application; the achieved efficacy sum of all pesticides in the application scheme for leading or priority weeds and/or pathogens; the achieved efficacy sum of all pesticides in the application scheme for all weeds and/or pathogens; if applicable, the average expected control of residual efficacy duration in soil; and/or an index accounting for repeating the mode of action and/or active ingredient compared to previous applications.
A01N 25/00 - Biocides, produits repoussant ou attirant les animaux nuisibles, ou régulateurs de croissance des végétaux, caractérisés par leurs formes, ingrédients inactifs ou modes d'applicationSubstances réduisant les effets nocifs des ingrédients actifs vis-à-vis d'organismes autres que les animaux nuisibles
G06F 16/2457 - Traitement des requêtes avec adaptation aux besoins de l’utilisateur
In order to provide an efficient recognition method for agricultural applications, a decision-support device for agricultural object detection is provided. The decision-support device comprises an input unit configured for receiving an image of one or more agricultural objects in a field. The decision support system comprises a computing unit configured for applying a data driven model to the received image to generate metadata comprising at least one region indicator signifying an image location of the one or more agricultural objects in the received image and an agricultural object label associated with the at least one region indicator. The data driven model is configured to have been trained with a training dataset comprising multiple sets of examples, each set of examples comprising an example image of one or more agricultural objects in an example field and associated example metadata comprising at least one region indicator signifying an image location of the one or more agricultural objects in the example image and an example agricultural object label associated with the at least one region indicator. The decision support device further comprises an output unit, configured for outputting the metadata associated with the received image.
