Optimization and Cutting Decision of Potato Seed Based on Visual Detection
In response to the problem of low automation level in the current potato seed potato preparation process,a decision model for bud detection and seed potato cutting based on YoloV8n was constructed.Use an industrial grade CCD camera to capture the graded potato seeds from two different perspectives and obtain the maximum field of view image of the potato seeds to be cut.After training,the YoloV8n object detection algorithm can accurately identify the position information of bud eyes in seed potato images.Convert the image of the seed potato and the position informa-tion of the bud eye into a bird's-eye view through perspective transformation,and extract the outline of the seed po-tato using graph cutting algorithm.The cutting decision model calculates the area formed by the intersection of each cutting groove and the contour,and formulates the optimal strategy for cutting seed potatoes to ensure that each cut-ting block contains bud eyes and the projection area of each cutting block is as similar as possible,thereby guiding the cutting device to cut the seed potatoes.The experimental results show that the qualified rate of the prepared seed potato chunks reaches 93.19％,which is 5.41％higher than blind cutting.The standard deviation of the weight of the seed potato chunks is reduced by 6.87 g.Satisfied the quality requirements for seed potato preparation.
YoloV8neye bud detectioncontour extractioncutting decision model
NETL
NSTL
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