Research On Algorithm Of Rice Diseases Identification And Leaf Age Detection Based On Machine Learning

With the rapid development of agricultural information intelligence,the traditional mode of relying on the weather has evolved into a new agricultural mode which is monitored and automatically controlled by intelligent devices.How to process these data collected by these intelligent devices,especially the image data,extract the useful information,and combine it with the actual production control measures,so as to promote the efficient and rapid operation of agricultural production,which is of great significance.In this paper,image processing and machine learning are applied to the identification of rice leaf disease and detection of rice leaf age.This method is more efficient and accurate than traditional manual identification and diagnosis methods.The main contents include:Firstly,the basic theories of machine learning algorithms and image processing techniques are introduced,which provide theoretical guidance and technical scheme for the research of the identification of rice leaf disease and detection of rice leaf age.Then,in the research on identification algorithm of rice leaf disease,some machine learning methods are used to identify rice diseases automatically.This paper mainly focuses on three common rice leaf diseases: rice blast,bacterial leaf blight and bacterial leaf streak.The specific contents include: Firstly,a series of preprocessing operations are carried out on the rice disease images,and the rice disease spots are segmented,so the corresponding image set of rice disease is established.Then,according to the pathological appearance of different disease spots,characteristic parameters from various aspects are extracted,and the extracted characteristic parameters are optimized by principal component analysis.After that,BP neural network and support vector machine(SVM)algorithm are used to establish models respectively,and the optimized features are classified and recognized.The BP neural network with higher recognition accuracy is selected as the best classification model.Finally,improvements are proposed for the best classification model.The genetic algorithm is used to optimize the initial value of weight and threshold in BP algorithm.The experimental results show that the GA-BP algorithm is feasible and improves the accuracy of disease identification.Then,in the research on detection algorithm of rice leaf age,some image processing techniques are used to detect rice leaf age automatically.Firstly,the traditional image processing method is used to extract the rice leaf veins.Under the condition that the traditional algorithm has a poor effect to extract the veins,a new method based on cluster mean value judgment is proposed to extract the rice leaf veins.The algorithm improved after can extract the rice the main vein successfully.By validating on the diseased rice leaves and the disease-free rice leaves,the algorithm performs both well.Then,combined with the leaf vein deflection method,the detection algorithm of rice leaf age is realized through practical test.Finally,combined the identification results of rice disease with the detection results of rice leaf age,based on the technology of rice leaf identification,the technology of leaf age diagnosis is taken as the core,according to different diseases,the corresponding management measures are taken timely for the paddy field according to different leaf age stages to conduct intelligent control of rice in cold regions.