Research on Strawberry Disease and Pest Detection Based on Enhanced YOLOv5s Algorithm
Strawberry growth is susceptible to a variety of diseases and pests.In order to quickly and accu-rately detect the diseases and pests suffered by strawberry plants during the growth process,this paper collects image information during the growth process of strawberry,applies YOLOv5s algorithm for analysis and process-ing,and integrates CBAM attention mechanism into YOLOv5s model.In order to enhance the sensibility and ex-pressiveness of the model,SIOU loss function is selected to replace GIOU loss function to accelerate the conver-gence of the model.The results show that the accuracy of the improved algorithm model reaches 95.2％,the re-call rate increases to 97.2％,and the average accuracy increases to 98.5％.To some extent,it can satisfy the detection of strawberry pests and diseases.
pest and disease detectionYOLOv5sCBAMSIOU loss function
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