Design And Implementation Of Segmentation And Recognition System For Persimmon Leaf Disease Image

China is the largest persimmon planting country in the world.But due to the occurrence of persimmon disease,resulting in persimmon branches withered,shoot coke,less results,the yield and quality of persimmon caused serious impact.The prevention and control of persimmon leaf diseases mainly take reasonable measures according to the type and degree of disease.The artificial identification of disease types and occurrence degree is prone to error and not timely.At present,the application of deep learning in agriculture has made good progress,the image segmentation method based on deep learning can accurately segment the lesion area and determine the degree of disease according to the lesion area.The image recognition method based on deep learning can quickly and accurately identify disease types.It provides strong technical support for artificial intelligence in the early prevention of persimmon leaf diseases.In this paper,the image segmentation and image recognition methods are studied,and the image segmentation and recognition system of persimmon leaf diseases based on deep learning is designed and implemented,which provides a solution for fruit farmers to achieve early disease prevention.The main research work of this paper is as follows:（1）Aiming at the problem that the size and shape of persimmon leaf disease spots change with the occurrence period and degree,the receptive field of convolutional neural network is fixed,and the segmentation ability of large or small lesions is poor,a UNet model based on self-attention mechanism and deformable convolution is proposed to realize the image segmentation of persimmon leaf disease.The deformable convolution is used to automatically adjust the characteristics of the convolution kernel according to the input sample to adapt to different sizes of diseases.At the same time,the self-attention mechanism is used to learn the relationship between each feature,obtain spatial information and context information,and reduce the loss of small target information caused by continuous downsampling.The experimental results show that the proposed model MPA and MIo U are 89.18 %and 83.58 %,respectively,and the segmentation effect is better than that of UNet.（2）In view of the problem that the size and location of lesion areas of persimmon leaf diseases are not fixed,this paper proposes a capsule network based on conditional convolution.The spatial vector of the capsule network can be used to represent the characteristics of the target’s attitude,position and direction,so as to solve the problem that the location of persimmon diseases is not fixed.At the same time,conditional convolution is introduced into the capsule network,and the characteristics of specific convolution kernels are customized for each input sample by conditional convolution,so as to improve the feature representation ability of the capsule network and adapt the capsule network to persimmon leaf diseases of different sizes.The experimental results show that the accuracy,precision,recall and F1 score of the proposed model are 88.44 %,88.86 %,88.44 % and 88.65 %,respectively,and the recognition effect is better than that of capsule network.（3）The image segmentation and recognition system of persimmon leaf diseases was designed and implemented.The system consists of disease image segmentation and recognition APP and disease information management system.The disease image segmentation and recognition APP includes image acquisition,image clipping,image preprocessing,disease image segmentation,disease image recognition,disease severity,disease prevention and planting question answering function.Disease information management system includes disease information management,planting Q ＆ A information management and user information management functions.The system aims to provide a convenient tool for fruit farmers to facilitate persimmon planting management and help fruit farmers achieve early disease control.