Potato bud eyes recognition based on three-dimensional geometric features of color saturation

Potato is an important food crop with planting area been increased annually. Potato should be cut according to agronomic requirements before planting to make the potato sprout early and to increase the yield and to save the seed potato. The bud position and the weight of the seed potato cutting have an important influence on late growth of potato. Accurate identification of the bud eye is the premise of automatic cutting of seed potatoes. In addition, bud eyes are easy to be confused with mechanical damages such as growth spots, soil and skin breakage. In order to reduce the false recognition rate and improve the success rate of bud eyes recognition, a method for identifying potato buds based on three-dimensional geometric features of color saturation are proposed. By comparing the color space of potato, it is found that the color saturation component S in the HSI color space is the most obvious. In addition, it is less affected by light conditions, avoiding low recognition accuracy caused by interference of brightness factors. In this paper, four eigenvectors based on their longitudinal section curves and their first derivatives were proposed by analyzing the S component in the three-dimensional geometric space. The four-feature comprehensive judgment criterion was used to identify the bud eye longitudinally, and then the bud eye vertical recognition result was screened again according to the lateral characteristics of the bud eye. The modified method can greatly reduce the bud eye false recognition rate. The specific potato bud eyes recognition algorithm was as follows: 1) Color saturation S component was obtained by converting the acquired image from RGB color space to HSI color space. 2) The S component curve was obtained by longitudinally column-by-column interception of the three-dimensional map of the S component. Then derivative curve was obtained by deriving the S component curve. 3) First, all the valley positions of the first derivative curve of the S component were obtained, and the four bud eye feature parameters corresponding to the position were obtained. Then, the bud eye was identified according to the determination rule. If the condition was satisfied, the corresponding row and column position was set to 0, otherwise it was set to 1. The bud eye binarization matrix was obtained by performing bud eye feature determination on all longitudinal columns of the S component three-dimensional map.4) According to the lateral continuity of the bud eye and the arc-like structure of the lower edge of the bud, the morphological processing of the bud eye binarization matrix was carried out, and the false bud eyes were further removed to complete the bud eyes recognition. The influence of defects such as broken skin, growth spots and mechanical damage on the accuracy of bud-eye recognition was also analyzed in this paper. The data indicated that mechanical damage, growth spots, etc. was not confused with bud eyes and caused misjudgment of the buds. The algorithm can effectively prevent the influence of these interference factors.The experimental results showed that the recognition rate of bud eyes was 91.48%, of which, the recognition rate of non-germinated buds, germinated buds and the false recognition rate of bud eyes was 92.21%, 89.00% and 4.32%, respectively. The probability of seed potato cutting without buds due to false recognition of bud eyes was less than 1.01%. This can effectively prevent the seedling shortage caused by potato cutting without buds, resulting in reduced production. The average time taken to identify a single image is 2.68 s. The results indicated that the method can provide reference for the bud eyes recognition of seed potato automatic cutting machine due to its low false recognition rate of bud eye, strong anti-interference ability and high stability.