Mushroom maturity detection model based on improved YOLOv5
It is of great significance to accurately detect the maturity of mushroom for the promotion of intelligent cultivation of mushroom.In order to achieve accurate detection of mushroom maturity,a segmentation method for mushroom maturity detection based on improved YOLOv5 example was proposed.This algorithm added Squeeze and Excitation module(SENet)to C3 module in the backbone network to enhance the learning ability of specific characteristics of shiitake mushrooms.The two convolution modules in the neck network were replaced by Deformable Convnets v2(DCN v2),which made the network better adapt to the shape and position changes of the target,and improved the accuracy and robustness of the target detection.The experiment showed that the accuracy of the improved model was 91.7%,6.1%higher than that of the original model.The accuracy and reliability of the improved model were better than that of the original model,which provided technical support for the promotion of intelligent planting of mushroom.
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