Agri Doctor

Abstract

The occurrence of plant diseases has a negative impact on agricultural pro- duction. If plant diseases are not discovered in time, food insecurity will increase. Early detection is the basis for effective prevention and control of plant diseases, and they play a vital role in the management and decision- making of agricultural production. In recent years, plant disease identification has been a crucial issue. Disease-infected plants usually show obvious marks or lesions on leaves, stems, flowers, or fruits. Generally, each disease or pest condition presents a unique vis- ible pattern that can be used to uniquely diagnose abnormalities. Usually, the leaves of plants are the primary source for identifying plant diseases, and most of the symptoms of diseases may begin to appear on the leaves. Farmers with less experience may misjudgment and use drugs blindly during the identification pro- cess. Quality and output will also bring environmental pollution, which will cause unnecessary economic losses. To counter these challenges, research into the use of image processing techniques for plant disease recognition has become a hot research topic. The general process of using traditional image recognition processing technol- ogy to identify plant diseases and use the K-means clustering method to segment the lesions regions, and combined the global color histogram (GCH) color coher- ence vector (CCV) local binary pattern (LBP), and completed local binary pat- tern (CLBP) was used to extract the color and texture features of brown spots, and three kinds of diseases were detected and identified based on improved support vector machine (SVM), and the classification accuracy reached 93percent.