基于改进Faster R-CNN的苹果叶部病害识别方法

针对苹果叶片图像中小尺度病斑和复杂背景带来的病斑目标难以精确定位和识别的问题,以苹果的斑点落叶病、黑星病、灰斑病、雪松锈病和花叶病为研究对象,提出一种基于改进Faster R-CNN的苹果叶片病害识别方法.先通过数据增广操作对训练集数据进行扩充以增强模型鲁棒性,再通过对增广训练集图像进行训练来得到一个可靠的病害识别模型...

Apple disease identification using improved Faster R-CNN

To analyze apple diseased leaf images,it is difficult to locate and identify these diseased leaves with small scale lesion and complex background in the actual application scenarios.In this study,five apple leaf diseases,i.e.,alternaria leaf spot,apple scab,gray spot,cedar rust and mosaic were investigated,and an improved Faster R-CNN based apple diseased leaf detection method was proposed.Firstly,the training set data was expanded through the data augmentation operation(including rotating,random brightness enhancement,random chromaticity enhancement,random contrast enhancement and sharpening)to enhance the robustness of the model.Then the augmented training set images were trained through the improved Faster R-CNN to make the detection model more reliable.For the improved Faster R-CNN model,the attention separation mechanism ResNest(split-attention networks)was adopted as the backbone to make the model focusing on the more useful information to enhance the feature extraction ability according to the feature representation through the weighted combination.To enhance the robustness of the feature information and improve the generalization ability,a feature pyramid network(FPN)was added to fuse multi-scale features,which effectively used the deep and shallow features of the network.Meanwhile,the cascade mechanism was adopted to optimize the generation mechanism of the suggestion box,so that the detection box location was more accurate.The mAP(mean average precision)of the improved model reached 86.2%,which is 8.7%higher than that of the previous Faster R-CNN model.The accuracy of the model reached 98.3%and the average detection time of the model was 0.092 s,which can effectively identify apple leaf lesions.The experimental results showed that the improved Faster R-CNN model could accurately and quickly identify small target lesions of apple leaves and lesions under complex background,and improve the accuracy of model recognition.The images in the data set included picked leaves and non-picked leaves,so this method could realize the nondestructive identification of apple leaf diseases,which provided a scientific basis for the early detection and prevention of apple diseases.