Abstract

A non-destructive fruit defect detection method and system based on neural networks are used to solve problem of inaccurate selection of high-quality fruits by current consumers. The system includes a standard formulation module configured to formulate monitoring standards for different batches and varieties of the fruits to obtain standard detection parameters for the different batches and varieties of the fruits, a preliminary identification module configured to preliminarily identify external conditions of the different batches and varieties of the fruits, a non-destructive detection module configured to non-destructively detect the different batches and varieties of the fruits, generate a fruit abnormal signal or obtain growth deviation values of the different batches and varieties of the fruits, and a quality judgment module configured to judge quality of the different batches and varieties of the fruits. Accurate non-destructive detection for the different batches and varieties of the fruits are realized.

IPC Classes  ?

• G06K 9/00 - Methods or arrangements for reading or recognising printed or written characters or for recognising patterns, e.g. fingerprints
• A01G 25/16 - Control of watering
• G01N 33/02 - Food
• G06V 10/75 - Organisation of the matching processes, e.g. simultaneous or sequential comparisons of image or video featuresCoarse-fine approaches, e.g. multi-scale approachesImage or video pattern matchingProximity measures in feature spaces using context analysisSelection of dictionaries
• G06V 10/82 - Arrangements for image or video recognition or understanding using pattern recognition or machine learning using neural networks
• G06V 20/68 - Food, e.g. fruit or vegetables

Abstract

An intelligent recognition method of hyperspectral image of parasites in raw fish relates to optical detection technology, and includes step 1: obtaining a hyperspectral image of the raw fish in a wavelength range from 300 to 1100 nm; step 2: extracting a grayscale image of the hyperspectral image at a wavelength value of 437 nm, and obtaining a position range of fish meat in the grayscale image by performing a median filtering process and a binarization process on the grayscale image; step 3: extracting spectral signals of pixel points in the position range of the hyperspectral image, performing a first-order derivative process on the spectral signals, and import the spectral signals after the first-order derivative process into a preset first model, a second model, and a third model for analysis. The method can accurately distinguish the parasite body in the raw fish.

IPC Classes  ?

• G01N 33/12 - MeatFish
• G06V 10/28 - Quantising the image, e.g. histogram thresholding for discrimination between background and foreground patterns
• G06V 10/34 - Smoothing or thinning of the patternMorphological operationsSkeletonisation
• G06V 10/36 - Applying a local operator, i.e. means to operate on image points situated in the vicinity of a given pointNon-linear local filtering operations, e.g. median filtering
• G06V 10/58 - Extraction of image or video features relating to hyperspectral data
• G06V 10/774 - Generating sets of training patternsBootstrap methods, e.g. bagging or boosting
• G06V 40/10 - Human or animal bodies, e.g. vehicle occupants or pedestriansBody parts, e.g. hands

Abstract

An automatic peeling and splitting device for citrus fruits includes a bracket, a pomelo fixing module, and a pomelo peeling module arranged on the bracket. The pomelo fixing module includes a cylinder arranged on an upper part of the bracket, a fixed block arranged on a power output end of the cylinder, and a motor arranged on the fixed block, the power output end of the motor is connected with a rotation shaft, an end of the rotation shaft is sleeved with an air sac, and the end of the rotation shaft is further hinged with a plurality of arc baffles, a concave member of the arc baffle is bonded to a surface of the air sac, an outer convex edge of the arc baffle faces outside of the air sac; the rotation shaft and the power output end of the cylinder are both extended vertically downward.

IPC Classes  ?

• A23N 7/02 - Peeling potatoes, apples or similarly shaped vegetables or fruit
• A47J 17/16 - Peeling machines with rotary fruit-holding spindles and fixed or movable peeler blades
• A23N 7/10 - Driving mechanisms

Abstract

A feature extraction method of fruit spectrum includes taking a vector of each wavelength point in spectrum of samples as source data, and acquiring a sorting of all vectors by processing the source data by SPA; according to the sorting of the vectors, acquiring distribution points of each sample on a coordinate system; acquiring classification results of the samples by destructive analysis, and acquiring a number of first sample categories; acquiring a first Euclidean distance between the first sample categories; according to a sorting of the wavelength points, acquiring distribution points of each sample on the coordinate system; acquiring a number of second sample categories; acquiring a second Euclidean distance between the second sample categories; determining whether the first Euclidean distance is less than the second Euclidean distance; determine a (M+2)-th vector to be valid or invalid based on a comparison result.

IPC Classes  ?

• G06V 20/10 - Terrestrial scenes
• G06V 10/77 - Processing image or video features in feature spacesArrangements for image or video recognition or understanding using pattern recognition or machine learning using data integration or data reduction, e.g. principal component analysis [PCA] or independent component analysis [ICA] or self-organising maps [SOM]Blind source separation
• G06V 10/74 - Image or video pattern matchingProximity measures in feature spaces
• G06V 10/141 - Control of illumination