AN INTEGRATED ENHANCED DEEP LEARNING MODEL DETECTING AND CLASSIFYING CROP DISEASES IN THE EARLY STAGES TO MAXIMIZE THE YIELD

Agriculture sector is the most promising sector which plays crucial role in Economy data of every country. There are numerous challenges are being faced by the farmers. Hence, researchers and academicians are trying to overcome these challenges by introducing new techniques, machine learning and AI models. Among the many challenges faced by farmers, leaf diseases stand as formidable adversaries, persistently diminishing their harvests. In most cases, farmers’ harvests are diminished by leaf diseases which directly affect the crop yield. Researchers and academicians are developing a generalized model applicable to all the crops. In the initial stage we have focused on wheat crop. Wheat is cultivated at large and is consumed by maximum people to its status as a staple food. Similarly in the next subsequent development of generalized model we will focus on many other crops. Wheat plant diseases affect the yield in the recent decades. Decreased crop yield directly affects the economic growth of the country and have social impact on farming. Recent technological advancements using artificial intelligence (AI), machine learning (ML) and deep learning (DL) have made it easier to identify diseases on leaves in their early stages. With the help of recent technologies it became possible to develop ‘autonomous’ systems keeping eye on the ‘diseases’ in the early stages. In this approach, we have used an open dataset from Plant Village which has more than images of 5000 plant leaf diseases divided into 04 types of leaf diseases along with the normal leaf. The proposed system is a combination of CNN and Mask RCNN. In CNN method we have successfully identified the type of disease for the input image whereas Mask RCNN masks the exact area covered by the disease of the leaf. Proposed integrated deep learning model applies the convolutional neural networks (CNN) for better accuracy (97 % accuracy), classification, and identifying the disease affected region. In the proposed work, model integrates 02 different methods CNN and Mask RCNN.