外文摘要：The North China type cucumber, characterized by its dense spines and top flowers, is susceptible to damage during the grading process, affecting its market value. Moreover, traditional manual grading methods are time-consuming and labor-intensive. To address these issues, this paper proposes a cucumber quality grader based on machine vision and deep learning. In the electromechanical aspect, a novel fixed tray type grading mechanism is designed to prevent damage to the vulnerable North China type cucumbers during the grading process. In the vision grading algorithm, a new convolutional neural network is introduced named MassNet, capable of predicting cucumber mass using only a top-view image. After obtaining the cucumber mass prediction, mass grading is achieved. Experimental validation includes assessing the electromechanical performance of the grader, comparing MassNet with different models in predicting cucumber mass, and evaluating the online grading performance of the integrated algorithm. Experimental results indicate that the designed cucumber quality grader achieves a maximum capacity of 2.3 t/hr. In comparison with AlexNet, MobileNet, and ResNet, MassNet demonstrates superior cucumber mass prediction, with a MAPE of 3.9% and RMSE of 6.7 g. In online mass grading experiments, the grading efficiency of the cucumber quality grader reaches 93%.
外文关键词：deep learning;quality grader;cucumber grading;mass prediction
作者：Chai, Xiujuan;Liu, Fanghong;Zhang, Yanqi;Du, Chengtao;Ren, Xu;Huang, Bo
作者单位：Chinese Acad Agr Sci;Harbin Inst Technol
期刊名称：FOODS
期刊影响因子：0.0
出版年份：2024
出版刊次：13（4）
原文传递申请：江苏省科技资源（工程技术文献）统筹服务平台