PLANT DISEASE DETECTION USING CONVOLUTIONAL NEURAL NETWORK

When plants and crops are suffering from pests it affects the agricultural production of country. Usually, farmers or experts observe with eye for detection identification disease. But this method is often time processing, expensive inaccurate. Automatic using image processing techniques provide fast accurate results. This paper cares a replacement approach to development disease recognition model, supported leaf classification, by utilization deep convolutional networks. Advances in computer vision present chance expand enhance practice precise plant protection extend market applications within field precision agriculture. completely unique way training therefore methodology used facilitate straightforward system implementation practice. All essential steps required implementing model fully described throughout paper, starting gathering images make database, assessed experts, learning framework perform CNN training. may be new detecting diseases neural network trained finetuned suit accurately database leaves that was gathered independently diverse diseases. The advance novelty developed dwell its simplicity healthy background line other classes, enabling distinguish between diseased ones environment CNN. Plants source food on earth. Infections big threat, while foremost common diagnosis primarily performed examining body presence visual symptoms [1]. As an alternative traditionally time-consuming process, different research works plan find feasible approaches towards protecting plants. In recent years, growth technology has engendered several alternatives traditional arduous methods [2]. Deep very successful classification problems.