Stress-Crack detection in maize kernels based on machine vision

•We designed a hardware system which can detect cracks accurately and efficiently.•We propose a cascade model using machine-vision to detect cracks automatically.•We find three empirical constraints to distinguish the real cracks from noises.•We integrate the algorithm and hardware to obtain an automatic crack-detection system. 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.