Artificial Intelligence Analysis Of Plant Diseases Based On Image Recognition

As a big agricultural country,the development of agriculture affects the people’s livelihood and national economic development.The identification of plant disease is the first guarantee of agricultural harvest.Traditional artificial identification and expert system do not meet the requirements of agricultural modernization because of the accuracy and respond speed.Deep learning is an important branch of the development of artificial intelligence,and has been applied to many fields such as image recognition,machine translation and so on.Detection and classification of crop diseases and insect pests through deep learning is a hot area in the development of modern agriculture.Based on the dataset published by AI Challenger,61 kinds of pests and diseases data were classified using neural networks.Firstly,the original data set is augmented by transformed images and images collected on the Internet,and the Alex Net-like network is used to verify the effectiveness of the augmentation.Secondly,a deeper neural network is used to train on the augmented data,and the classification of various types of diseases is analyzed.Because crop diseases are not easy to detect in the early stage,the number of samples is relatively small compared with the late-stage ones,which leads to the problem of imbalance in the number of samples.This problem is improved by data set sampling and model fusion,and the classification accuracy of the same disease at different stages of development is improved.Because the labeled data of pests and diseases are not easy to obtain in actual production activities,this paper uses GANs to train on a small number of samples in the above data sets in the way of semi-supervised learning.Compared with the original supervised learning method,it reduces the demand for labeled data.Using 50% labeled samples,we can get the accuracy close to 100% of supervised learning data.A feedback dynamic mediation GAN training method is proposed to improve the convergence speed and stability of the network.The loss function of GAN semi-supervised learning is improved,the degree of data imbalance caused by generated data is reduced,and the accuracy of the network is improved.