Enhancing Rice Plant Disease Detection: A Classification Approach with Transfer Learning

Abstract

Rice is the dominant crop of Bangladesh. Yet, every year a large number of crops are affected by the disease. Conventional detection of rice plant disease in the lab is timeconsuming and involves a lot of time. It leads to a decrease in production and economic loss for farmers. Therefore, image-based disease classification is a rising research discipline. In this paper, we've taken an approach to classify the nine frequent rice diseases and normal rice plant leaves by image. With transfer learning and model finetuning, we classified a total of ten classes. We are using Convolution Neural Network (CNN) and Deep Learning (DL), a subfield of Artificial Intelligence, for doing this kind of classification with automatism by training an over ten-thousand image list. We've achieved 98.47 percent validation accuracy with EfficientNetB3 of total ten class where nine of them are disease class and remaining one is normal. After identifying the bestperforming model, we converted it using TensorFlow Lite model maker for deployment in a mobile application. This app enables real-time disease detection by allowing users to capture or select an image, which the model then classifies instantly. This paper includes methodology and how we achieved validation accuracy along with previous literature on this. Discussion on hyperparameter tuning and utilization of different categories of pretrained models which were trained on 'ImageNet' are present.