DETECTION OF PLANT DISEASE USING DEEP LEARNING TECHNIQUES

Deep Learning(DL) is one of the parts of machine learning methods based on artificial intelligence network with the representation of learning. Deep learning techniques help to process and analyze big data available around us through several applications in various fields related to the subject. The concept of plant disease is the scientific study of plants where the disease in plants is caused by pathogens and other environmental conditions. In order to identify the disease and curing them in the initial stage is the better option. Automatic and perfect identification of plant disease is important in the aspects of food security, managing disease, and predicting it. DL method helps in detecting the disease's severe-ness. With the leaf of apple plant with rot images in the Plant-Village dataset are explained by botanists with 4 stages of severeness. The deep learning convolutional neural network are trained for analysing the severe-ness in the plant disease. This research tries to analyze the transfer learning method with the help of a deep model and trained networks from scratch. The deep VGG16 model under the training of transfer learning was found to have an accuracy of 90 percent on the test. The DL method will have a huge significance in disease control in plants in the field of modern agriculture.