Design and Experimentation of a Machine Vision-Based Cucumber Quality Grader

文献类型： 外文期刊

第一作者： Liu, Fanghong

作者： Liu, Fanghong;Du, Chengtao;Ren, Xu;Huang, Bo;Zhang, Yanqi;Chai, Xiujuan

作者机构：

关键词： quality grader; cucumber grading; deep learning; mass prediction

期刊名称：FOODS （ 影响因子：5.2； 五年影响因子：5.5 ）

ISSN：

年卷期： 2024 年 13 卷 4 期

页码：

收录情况： SCI

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%.

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