Maize disease identification method based on improved YOLOv3
In order to improve the accuracy of maize disease leaf recognition model,an improved YOLOv3 maize disease recognition method was proposed.First of all,in order to obtain deeper maize disease characteristics,the YOLOv3 network architecture was modified to YOLOv3-M1 and YOLOv3-M2 by changing the proportion of shallow feature map and adding a fourth detection layer.Then,the improved K-means algorithm was used for clustering,and the obtained anchor frame tended to be the true boundary frame of the data set.Finally,a balance factor was added for each category,and the difficulty of samples in different categories was weighted to modify the loss function,so that the model could find the best point between the boundary box prediction and the category prediction,so that the algorithm could obtain the best detection effect.The test results show that the accuracy of the improved YOLOv3-M1 and YOLOv3-M2 models in the test set is as high as 95.63％and 97.59％,respectively.Compared with the YOLOv3 model,the recognition accuracy is increased by 4.15％and 6.28％,respectively,and the recognition accuracy is greatly improved in the corn data set.
maizedeep learningdisease recognitionYOLOv3 modelloss function
NETL
NSTL
万方数据
