Stress-Crack detection in maize kernels based on machine vision

文献类型： 外文期刊

第一作者： Li, Jia

作者： Li, Jia;Zhao, Bo;Wu, Jincan;Zhang, Shuaiyang;Lv, Chengxu;Li, Jia;Wu, Jincan;Li, Lin

作者机构：

关键词： Stress-crack detection; Machine vision; Maize kernel; Cascade model; Constraint

期刊名称：COMPUTERS AND ELECTRONICS IN AGRICULTURE （ 影响因子：6.757； 五年影响因子：6.817 ）

ISSN： 0168-1699

年卷期： 2022 年 194 卷

页码：

收录情况： SCI

摘要： Stress-crack detection is important for determining seed quality. This paper presents both a machine-vision-based method and a prototype of an industrial hardware design for stress-crack detection in maize kernels. Specifically, we present a cascade model incorporating kernel-status classification, region-of-interest segmentation, and crack detection models. The status-classification model selects kernels with the correct camera orientation, whereas the region-of-interest segmentation model locates the main axis of the kernel and supplies a kernel mask for endosperm segmentation. Further, we utilise the EDLines algorithm to detect cracks and apply three novel constraints to distinguish real cracks from noise. The results of experiments conducted indicate that our integrated hardware-software system can detect stress cracks in maize kernels effectively and automatically. The observed precision and recall of the overall system were 92.7% and 94.4%, respectively.

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