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http://dx.doi.org/10.9717/kmms.2022.25.6.769

A Sweet Persimmon Grading Algorithm using Object Detection Techniques and Machine Learning Libraries  

Roh, SeungHee (Dept. of Information and Communications Engineering, Changwon National University)
Kang, EunYoung (Dept. of System Engineering, Changwon National University)
Park, DongGyu (Dept. of Information and Communications Engineering, Changwon National University)
Kang, Young-Min (Dept. of Game Engineering, Tongmyong University)
Publication Information
Journal of Korea Multimedia Society / v.25, no.6, 2022 , pp. 769-782 More about this Journal
Abstract
A study on agricultural automation became more important. In Korea, sweet persimmon farmers spend a lot of time and effort on classifying profitable persimmons. In this paper, we propose and implement an efficient grading algorithm for persimmons before shipment. We gathered more than 1,750 images of persimmons, and the images were graded and labeled for classifications purpose. Our main algorithm is based on EfficientDet object detection model but we implemented more exquisite method for better classification performance. In order to improve the precision of classification, we adopted a machine learning algorithm, which was proposed by PyCaret machine learning workflow generation library. Finally we acquired an improved classification model with the accuracy score of 81%.
Keywords
Persimmon Classification; Convolutional Neural Network Classifier; EfficientDet Object Detection Model; Machine Learning Library;
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Times Cited By KSCI : 4  (Citation Analysis)
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