AN EXTENSIVE STUDY ON CLASSIFICATION BASED PLANT DISEASE DETECTION SYSTEM

Abstract

Tree physiology and condition are connected to climate effects in the immediate environment since it strongly associated with the environment. Line up for detecting and identifying plant diseases has proved to be a trustworthy source of information for making farm decisions. Artificial intelligence techniques such as Deep learning and Convolution Neural Network (CNN) are earning momentum from the area due to powerful tool for detecting leaf diseases. Previously, manual observation was used to identify pests is complicated and prone to errors. Several plant diseases are invisible to the naked eye. The incidents of early disease are insignificant. To improve the quality of plant output and yield, it's critical to identify and handle disease symptoms early on. Farmers in India are also worried about diagnosis. At the same time, fearing pest attacks, the farmer sprays pesticides throughout the entire farm, potentially causing soil and plant damage. The aim is to get farmers to spray a small amount of pesticide at a specific target area where a pest is present or where an attack will occur in the future. The use features extraction and classification algorithms to classify tree leaf diseases and suggest pests to provide an alarm system.