DESIGN OF A DETECTION AND SORTING SYSTEM FOR BROKEN CORN KERNELS WITH AN ONLINE IDENTIFICATION METHOD

文献类型： 外文期刊

第一作者： Cui, Chunxiao

作者： Cui, Chunxiao;Yao, Yanchun;Lin, Jie;Wang, Faying;Li, Xibin;Yao, Yanchun;Cui, Chunxiao;Yao, Yanchun;Li, Xibin;Jiang, Wenjuan;Zhao, Bo

作者机构：

关键词： Broken; Convolutional neural network; Corn kernels; Detection; Sorting

期刊名称：APPLIED ENGINEERING IN AGRICULTURE （ 影响因子：0.9； 五年影响因子：1.1 ）

ISSN： 0883-8542

年卷期： 2025 年 41 卷 2 期

页码：

收录情况： SCI

摘要： During threshing and harvesting processes, corn is exposed to compression force, impact, and collisions, which collectively contribute to kernel breakage. To overcome the limitations of low accuracy and high cost inherent in manual sorting, a novel detection and sorting system for broken corn kernels has been developed. This system combines a seed discharge unit, a sorting unit, a control system, and image recognition technology to enhance sorting precision. A Raspberry Pi serves as the host computer, while an STM32 microcontroller serves as the subordinate unit, responsible for detecting the status of limit switches. Furthermore, mechanical analysis and innovative design enhancements were performed on key components of the detection system. To improve recognition accuracy, a corn kernel image processing method based on spatial color threshold segmentation was developed. AlexNet, GoogleNet, and ResNet34 were utilized to train six datasets of varying magnitudes. By loading 18 weightfiles, the optimal image scale that achieved the highest accuracy was identified. The recognition accuracy of the three network models was compared across the validation set, test set, and broken corn kernels. Results indicated that the GoogleNet network model demonstrated the best overall performance, achieving an accuracy of 95.58% on the validation set and 94.55% on the test set. In evaluating the detection device, the GoogleNet model achieved accuracies of 92.00%, 95.00%, and 98.00% for identifying broken, moldy, and intact corn kernels, respectively, with a sorting accuracy of90.00% specifically for broken kernels.

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