STUDY ON VARIOUS MACHINE LEARNING TECHNIQUES FOR PLANT DISEASE DETECTIONS IN AGRICULTURAL SECTOR

Abstract

Around 70% of Indian population are directly or indirectly depends upon agriculture. Till date the farming sector is flourishing on outworn technical techniques. Failure in detection of disease in early stage or improper diagnosis of plant disease can cause huge loss in terms of production, product quality, time and money. In such case detections of plant illnesses and pests at early stage plays a crucial role in terms of production yield [21]. Thus early detection of disease helps in avoiding financial loss to the farmers and agriculture industry. Earlier it’s was very unfair practice to detect disease and pest manually which requires expertize, high labor and ample amount of time. Here digital image processing and machine learning models plays an important role and providing immense help to agriculturists. To get maximum benefits to farmer’s, researchers from various field are applying the image processing techniques to monitor and diagnose the plant illnesses in its numerous stages and here the machine learning algorithms are used to give a solution for the various problems [4]. Thus aforesaid techniques can be used for monitoring, diagnosing plant illnesses, and providing proper treatment within proper time span. In this paper, the neutral is to training and evaluate the numerous development of machine learning concepts studied and recycled by altered researcher in agricultural sector [7].