Apple Leaf Image Segmentation And Disease Detection Based On Deep Learning

With the continuous development of artificial intelligence,deep learning technology has been widely used in agriculture.In this paper,the apple leaf image is taken as the research object,and the deep learning technology is used to segment the healthy apple leaf image quickly,and the geometric parameters of the apple leaf are determined accurately.At the same time,the image of the apple leaf with disease is automatically detected with high accuracy.Since apple leaves are the main photosynthetic organs of apple trees,the growth and development of apple leaves can directly reflect the growth of apple trees.Therefore,apple leaf image segmentation and disease detection based on deep learning has high research significance and practical value.Aiming at the segmentation problem of apple leaf image,this paper proposed a segmentation method of healthy apple leaf image based on Bi Se Net model.Firstly,174 kinds of 8 184 apple leaf images were annotated,and a data set of apple leaf image annotation with sufficient data was established.Secondly,Bi Se Net real-time semantic segmentation network model was used to rapidly segment apple leaf image,and guided filtering technology was used to extract edge details.Finally,guided filtering binarization images combined with deep learning segmentation results are used to eliminate redundant ruler parts and outliers.Experimental results show that the accuracy and intersection ratio of the proposed method are over 98%,which can obtain complete segmentation results.In addition,geometric parameters are extracted in this paper,which can provide effective data for monitoring the growth of apple trees.Aiming at the target detection problem of apple leaf disease,this paper proposed an improved Faster R-CNN model,which took Res Next-101 as the main trunk network.By enlarging the network width,the robustness of the network and the diversity of extracted features were improved,and the feature pyramid network structure was added to make the detection accuracy of small targets higher.The improved Faster R-CNN model proposed in this paper was verified to have strong generalization ability by using the images of black rot apple leaves in open data sets.