Research on machine-vision-based method for head-tail detection of corn ears

To address the issues of manual feeding and placement in the processing of fresh corn ears, this study develops a fresh corn ear detection system by using machine vision and deep learning techniques. The hardware setup and acquisition of clear image data were completed. The G channel image in the RGB channel was selected for subsequent image processing. Contrast enhancement and image sharpening techniques were employed to improve the clarity of the ear region. Additionally, a median filtering method was used to eliminate noise caused by lighting conditions. Morphological processing and Otsu threshold segmentation techniques were applied to extract complete corn ear regions. Based on the contour feature, three kinds of feature extraction of ear shape, gray value, and ear texture are selected. By comparison, the ear texture feature recognition algorithm with higher recognition accuracy and shorter detection time was selected. The algorithm was used to detect the ear handle region, and the cutting position was predicted in this region. This paper studied the method of predicting the cutting position by using the slope change feature of the spikelet region contour and used C# and Halcon mixed programming to realize the automatic detection of the ear. Experimental results show that the detection accuracy of the spikelet region can reach 98.23% by using the texture feature detection algorithm, and the detection time of a single ear is 23.90 ms.