Disease Detection in Groundnut Crop Using Deep Learning Models

Early detection of the diseases is very vital for the well-being and improvement of the crop. Often, it is difficult to detect the disease of the crop upon observation with a naked eye. Professionals in identifying plant diseases are very less and their availability in each and every region is one of the primary difficulties that the agriculture sector is facing. So, the best solution for this problem is to replace a human with an advanced and well-trained model that can help in accurate detection of the plant disease. Several people have researched in this field. Yet, the results are not always promising and there is lot of scope for improvement. In this project, we made use of Groundnut crop for determining the performance of the disease detection model that we are developing. After referring to various papers, we eventually decided to work on three models- CNN, MobilenetV2 and InceptionResnetV2. Our ultimate aim is to determine the best model among these three for groundnut disease detection.