Identifying the agricultural Diseases using image processing and Soft computing techniques

Abstract

Agriculture is the backbone of Indian economy and livelihood of Indians relies on Agriculture. In recent days, farmers face many issues because of the diseases that affect the crops. Due to the affected n, yearly productivity of farmers is lower. Manual monitoring of plant disease tends to require enormous amount of work and also require excessive processing time. The rudimentary purpose of this research work is to detect the plant disease rapidly and accurately and aid farmers. The method employed in this thesis helps in mapping the diseases that occur in rice, banana and sugarcane and also it detects the plant infection at the earlier stage. Hence, this thesis is concerned with development of image processing technology and soft computing for smart agriculture applications, with a particular focus on applications in new design for the detection of diseases in rice, banana and sugarcane plants. The important analysis techniques employed involves image acquisition, segmentation, feature extractions, classification and Neuro-Fuzzy modelling. By utilizing this robust detection expert system, the exact location of the infected crop can be identified and also the agricultural diseases at an early stage is identified in rice, banana and sugarcane stems and crops are protected. Input images are collected from the agricultural fields and research institutions. Computer vision techniques act as an effective means to distinguish between healthy and unhealthy crops. newlineFirstly, the pre-processing method is used to eliminate the noise from images and then a comprehensive study is conducted using image-based plant feature extractions techniques. Three primary parameters namely colour, texture and shape are discussed. newline