Novel Disease detection for paddy crop using CNN with Transfer Learning

Plant disease is a significant factor that affects plant growth and yield leading to a loss in money and time. A professional farmer can identify the disease type with his experience. Still, a new one has to rely on an agricultural expert, which takes more time and risks disease spreading to remaining crops. We have chosen the paddy crop among various plant types as it is the widely cultivated plant type in the South-Asian subcontinent. We have primarily determined three common disease types, namely Leaf blight, Brown spot, and Blast. Our solution is to identify the disease type from paddy leaf images using a Convolutional neural network(CNN). Our dataset has 1100 images, of which 700 are taken for training and 400 for testing. We use the TensorFlow Keras model for image pre-processing and classification, in which the image will be resized and converted to RGB. We operate transfer learning methods for classification as it takes less time compared to other algorithms. Finally, the disease type is displayed to the user.