Nematode survival rate is an important indicator for assessing the effectiveness of nematicidal reagents.Currently,nematode counting relies heavily on manual identification under a microscope,which is time-consuming,inaccurate and labor-intensive,etc.The use of a convolutional neural network to achieve intelligent identification and counting of nematodes is a crucial method to solve the above problems.We proposes an improved YOLOv7 neural network model with three improvements:adding ECA attention mechanism modules to the main network;optimizing the loss function of the original model by EIoU and replacing the original activation function with the Mish activation function.Comparative experimental tests reveal that the mAP of the improved YOLOv7 model reaches 95.3%,which is 12.3 and 6.2 percentage points higher than that of the classical target detection algorithms,such as SSD and Faster-RCNN,and 0.6,2.4 and 1.5 percentage points higher than that of SSD,Faster-RCNN and other classic target detection algorithms in terms of the accuracy rate,the recall and the F1 factor,respectively.Additionally,the model reduces redundancy,enhances multiscale feature extraction,and improves detection and regression precision for overlapping nematodes.In order to implement the research,the improved model was deployed to the server,and an application system for nematode survival status detection was developed using Vue,SpringBoot and other technologies,these provide the convenient and efficient nematode identification and counting service for researchers.
维普
万方数据