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

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 proposed.As 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 inputs.Considering 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 theory.Constructing neural network with ordered partitions as the base learner,the learning procedure of the Bagging-based ensemble model is detailed.Experiments on Yantai Red Fuji apples demonstrate the satisfactory grading performances of proposed evidential ensemble model for ordinal classification.

Application of Feature Extraction through Convolution Neural Networks and SVM Classifier for Robust Grading of Apples

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 apple.The 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 process.This information is fed into a one-to-one classifier that uses a support vector machine(SVM),instead of the softmax output layer of the CNN.In this manner,Yantai apples with similar shapes and low discrimination are graded using four different approaches.The 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 method.Grading tests are carried out using the automated grading device that is developed in the present work.It 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.

Evaluation indices of sour flavor for apple fruit and grading standards

In order to establish grading standards of evaluation indices for sour flavor of apples, 10 indices of samples from 106 apple cultivars were tested, including： malic acid（Mal）, oxalic acid（Oxa）, citric acid（Cit）, lactic acid（Lac）, succinic acid（Suc）, fumaric acid（Fum）, total organic acids（To A, the sum of the six organic acids tested）, titratable acid（TiA）, acidity value（AcV）, and pH value. For most of the cultivars studied（85.8%）, the order of the organic acid contents in apples was Mal〉Oxa〉Cit〉Lac〉Suc〉Fum. Mal was the dominant organic acid, on average, accounting for 94.5% of To A. Among the 10 indices, the dispersion of pH value was the smallest with a coefficient of variation of only 8.2%, while the coefficients of variation of the other nine indices were larger, ranging between 31 and 66%. There were significant linear relationships between Mal and two indices（ToA and AcV） as well as between ToA and AcV. There were significant logarithmic relationships between pH value and four indices： Mal, TiA, ToA, and AcV. All the equations had very high fitting accuracy and can be used to accurately predict related indices. According to this study, Mal, ToA, and AcV of apple were normally distributed, TiA was close to normally distributed, whereas pH value had a skewed distribution. Using the fitted normal distribution curves, the grading standards of Mal, TiA, ToA, and AcV were established. The grading node values of pH value were obtained using the logarithmic relationship between pH value and Mal. The grading standards of these five indices can be used to evaluate the sour flavor of apple. This study provides a scientific basis for evaluating apple flavor and selecting apple cultivars.

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

The grading judgment for apples is related to a variety of factors including,size,shape,color,texture,and scars.Traditional manual sorting methods are time consuming and labor intensive.In addition,the accuracy of the method is easily subjective,not repeatable,error-prone,and affected by the sorting environment.This paper presents a complete and automated grading system for apples.The system uses a single-chip microcomputer as the controller of the system,and a PC as the graphics processing unit.It also includes a conveyor,drive motor,frequency converter for motor control,photoelectric sensors,air compressor,and air jets for ejecting the graded apples.The 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 bins.At the same time,in order to ensure that an apple triggers the correct ejecting actuator,this paper designs a jet controller with proper logic.

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

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 apples.The 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 calyx.Furthermore,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 method.Finally,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.