Proposed Grade Discrimination Model Combining Classification and Grade Regression

Image processing techniques based on neural networks enable highly accurate discrimination in fruit and vegetable sorting. However, conventional classification models may not be sufficiently accurate for grading based on fine features, such as in the simultaneous output of high estimated probabiliti...

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Published inAgricultural Information Research Vol. 33; no. 2; pp. 109 - 116
Main Authors Iwadate, Kenji, Ninomiya, Kazunori, Ozawa, Katsuya, Suzuki, Ikuo
Format Journal Article
LanguageEnglish
Japanese
Published Japanese Society of Agricultural Informatics 01.07.2024
Subjects
automatic sorting
convolutional neural network
deep learning
image recognition
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Abstract Image processing techniques based on neural networks enable highly accurate discrimination in fruit and vegetable sorting. However, conventional classification models may not be sufficiently accurate for grading based on fine features, such as in the simultaneous output of high estimated probabilities for multiple distant grades. Here we propose a classification model combining conventional classification and grade regression for grade discrimination and verified its effectiveness by using the grade discrimination of onion peelings as a test case. The model reduced misclassification to distant grades without decreasing discrimination accuracy, relative to conventional classification and regression models.
AbstractList Image processing techniques based on neural networks enable highly accurate discrimination in fruit and vegetable sorting. However, conventional classification models may not be sufficiently accurate for grading based on fine features, such as in the simultaneous output of high estimated probabilities for multiple distant grades. Here we propose a classification model combining conventional classification and grade regression for grade discrimination and verified its effectiveness by using the grade discrimination of onion peelings as a test case. The model reduced misclassification to distant grades without decreasing discrimination accuracy, relative to conventional classification and regression models.
Author Suzuki, Ikuo
Iwadate, Kenji
Ozawa, Katsuya
Ninomiya, Kazunori
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Cites_doi 10.1109/CVPR.2016.91
10.3390/app10103443
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SubjectTerms automatic sorting
convolutional neural network
deep learning
image recognition
Title Proposed Grade Discrimination Model Combining Classification and Grade Regression
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