Identification and Classification of Leaf Diseases using Deep Learning

Abstract

For agriculture to be sustainable, it is essential to monitor a plant's health and look for diseases. It is quite challenging to manually monitor plant diseases. To improve the agriculture industry and the prosperity of our nation, plant disease must be effectively identified. Several diseases cause the plant's leaves to die. Farmers have a harder time seeing these diseases because it's difficult to care for those plants without knowing about them. The method described in this paper uses Tensor Flow to identify illnesses in plant leaf pictures. CNN training is used on the model proposed to automatically diagnose disease using the object detection API tensor flow. This data is acquired from multiple sources. In order to treat the sickness, the proposed effort will also identify the causes and manifestations of the illness. In the work proposed, to identify plant diseases, sophisticated deep machine learning models based on specific CNN topologies were developed using photos of healthy or diseased plants' leaves. In comparison to existing models, CNN's numerous layers offer a great rating and a 96 percent accuracy level.