Tomato diseased leaf detection model based on improved YOLOv5 in nat-ural environment
Aiming at the complex background and dense occlusion of tomato leaves in natural environment,an im-proved YOLOv5 model was proposed for real-time detection of tomato leaf diseases in natural environment.Firstly,the RepVGG module was used to replace the convolutional layer of the backbone network in YOLOv5,which improved the fea-ture extraction capability of the backbone network,reduced the memory occupation of the model and accelerated the reason-ing speed of the model.Secondly,the attention mechanism module CBAM was introduced into C3 module in neck part to improve the detection accuracy of tomato diseased leaves and the recognition rate of shielded targets in the complex back-ground.Finally,a new loss function SIoU was introduced to accelerate the convergence speed of the model and reduce the loss value of the model.The research results showed that compared with the original YOLOv5 model,the average precision of the improved model increased by three percentage points,and the average accuracy was as high as 98.9%,indicating that the improved model was more advantageous in the detection of tomato diseased leaves in natural environment.
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