ADVANCED DEEP LEARNING METHODS TO ACCURATELY DETECT AND CLASSIFY PLANT DISEASE
Abstract
Plant diseases are a big challenge in the agricultural productivity, food security and livelihood of farmers around the world. Conventional disease detection techniques where human judgment plays a major role are usually lengthy, inaccurate and unavailable in the rural communities. In recent years, machine learning, and deep learning have also made it possible to develop automated plant disease detection systems that are able to identify and classify plant diseases correctly with the help of leaf images. This paper discusses how computational methods can be used to diagnose the diseases in plants through the analysis of the visual symptoms, including the discoloration of leaves, spots, and textural features. We used convolutional neural networks (CNNs) to train a model using a set of images of plant leaves labelled with different diseases to identify the disease of different crop species in the dataset. The suggested system is highly accurate and scalable, and it provides a practical application of the early detection of the disease and a prompt intervention. This type of technology can help a great deal in precision agriculture, lessen the waste of pesticides, and enhance real-time monitoring of the health of crops.
