Feature recognition algorithm in intelligent planting and preparation technology of single-bud segment sugarcane

In order to avoid the uneven phenomenon of sugarcane planting, such as seed missing and reseeding, the computer vision technology was applied to the intelligent identification of sugarcane varieties with single-bud segment, and the design idea of rapid detection of sugarcane planting distribution was proposed in this study. With sugarcane species with single-bud segment as the research object, the sugarcane species distribution image was acquired, and LabelImg was used for image annotation and format conversion to build the YOLOv5s target detection model. On the basis of depth-separable convolution, SE module is spliced to obtain the weights of extracted features and extract key features of input feature map. By adding regularization to constrain the BN layer coefficient, sparse regularization is carried out on the BN layer to reduce the network input size and improve the model training speed. On this basis, 600 rounds of iterative training were carried out to complete the target recognition of sugarcane species characteristics in single-bud segment. The results showed that the recognition accuracy, mAP value, and Recall value of YOLOv5s single-bud segment target detection model are 98.95%, 98.89%, and 98.69%, and the loss value converges in advance between 0-0.02. The results showed that YOLOv5s could effectively detect and identify sugarcane seeds with single-bud segment during field planting, which lays a foundation for promoting precise and intelligent sugarcane planting.