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apple grading method based on neural network with ordered partitions and evidential ensemble learning

CAAI Transactions on Intelligence Technology 2022年第4期7卷 561-569页

作者： Liyao Ma Peng Wei Xinhua Qu Shuhui Bi Yuan Zhou Tao Shen School of Electrical Engineering University of JinanJinanChina Blekinge Institute of Technology KarlskronaSweden

In order to improve the performance of the automatic apple grading and sorting system,in this paper,an ensemble model of ordinal classification based on neural network with ordered partitions and Dempster–Shafer theory is *** a non-destructive grading method,apples are graded into three grades based on the Soluble Solids Content value,with features extracted from the preprocessed near-infrared spectrum of apple serving as model *** the uncertainty in grading labels,mass generation approach and evidential encoding scheme for ordinal label are proposed,with uncertainty handled within the framework of Dempster–Shafer *** neural network with ordered partitions as the base learner,the learning procedure of the Bagging-based ensemble model is *** on Yantai Red Fuji apples demonstrate the satisfactory grading performances of proposed evidential ensemble model for ordinal classification.

关键词： apple grading Demspter-Shafer theory ensemble learning ordinal classification

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Automatic grading of apples based on multi-features and weighted K-means clustering algorithm

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Information Processing in Agriculture 2020年第4期7卷 556-565页

作者： Yang Yu Sergio A.Velastin Fei Yin College of Information and Management Science Henan Agricultural UniversityZhengzhou 450046PR China Collaborative Innovation Center of Henan Grain Crops ZhengzhouHenan 450002PR China Queen Mary University of London Mile EndLondon E14NSUK

In this paper,a fast and effective method based on multiple image features and a weighted K-means clustering algorithm is proposed to achieve the automatic grading of *** method provides a novel way of using four images(top,bottom and two sides)and average gray values for each apple to distinguish between the apple defects,stem and ***,weighted features(MCSAD(maximum cross-sectional average diameter),circularity,PRA(proportion of red area)and defect regions)were carefully selected according to the requirements of the national apple grading standard,which improves the practicality of the proposed ***,qualitative and quantitative evaluation results demonstrate that the total accuracy of the proposed multi-feature grading method is greater than 96%,which provides encouragement for the additional research and implementation of multifeature automatic grading for the fruit industry.

关键词： apple grading Multi-features Threshold segmentation K-means

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Application of Feature Extraction through Convolution Neural Networks and SVM Classifier for Robust grading of apples

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Instrumentation 2019年第4期6卷 59-71页

作者： Yuan CAI Clarence W.DE SILVA Bing LI Liqun WANG Ziwen WANG Harbin Institute of Technology at Shenzhen Shenzhen 518000 The University of British Columbia Vancouver BCCanada V6T 1Z4

This paper proposes a novel grading method of apples,in an automated grading device that uses convolutional neural networks to extract the size,color,texture,and roundness of an *** developed machine learning method uses the ability of learning representative features by means of a convolutional neural network(CNN),to determine suitable features of apples for the grading *** information is fed into a one-to-one classifier that uses a support vector machine(SVM),instead of the softmax output layer of the *** this manner,Yantai apples with similar shapes and low discrimination are graded using four different *** fusion model using both CNN and SVM classifiers is much more accurate than the simple k-nearest neighbor(KNN),SVM,and CNN model when used separately for grading,and the learning ability and the generalization ability of the model is correspondingly increased by the combined *** tests are carried out using the automated grading device that is developed in the present *** is verified that the actual effect of apple grading using the combined CNN-SVM model is fast and accurate,which greatly reduces the manpower and labor costs of manual grading,and has important commercial prospects.

关键词： apple grading k-nearest Neighbour Method Convolutional Neural Network Support Vector Machine Machine Learning

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Design of an Automated Sorting System for apples Based on Single Chip Microcomputer

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Instrumentation 2019年第4期6卷 95-108页

作者： Liqun WANG Clarence W.DE SILVA Bing LI Yuan Cai Harbin Institute of Technology at Shenzhen Shenzhen 518000 The University of British Columbia VancowerBCCanada V6T 1Z4

The grading judgment for apples is related to a variety of factors including,size,shape,color,texture,and *** manual sorting methods are time consuming and labor *** addition,the accuracy of the method is easily subjective,not repeatable,error-prone,and affected by the sorting *** paper presents a complete and automated grading system for *** system uses a single-chip microcomputer as the controller of the system,and a PC as the graphics processing *** also includes a conveyor,drive motor,frequency converter for motor control,photoelectric sensors,air compressor,and air jets for ejecting the graded *** classification algorithm is implemented by using a convolutional neural network(CNN).In order to eliminate contact damage of apples,the system specifically uses air jets as actuators to eject the graded apples into the corresponding *** the same time,in order to ensure that an apple triggers the correct ejecting actuator,this paper designs a jet controller with proper logic.

关键词： Automation Single Chip Microcomputer apple grading Convolutional Neural Network Air Jets

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