An Advance Deep Learning Approach For Rice Leaf Disease Classification

Abstract

Feeding the people and preventing the spread of diseases among crops depend on the early diagnosis of diseases in agricultural crops. This paper employs transfer learning to automate the classification of rice leaf diseases, particularly those common in the area as bacterial leaf blight and brown spot. In order to assess the effectiveness of the aforementioned Deep Learning (DL) models, a dataset including 692 photos of brown spots, 700 photographs of healthy rice, and 700 images of bacterial leaf blight is employed in the vicinity of the Pabna Bangladesh Agriculture Development Corporation (BADC) & Jamalpur district paddy field. Data collection, sample preparation, labeling, image processing, selecting the best model, training, and testing are all included in the established technique. The models over fit and produced varying accuracy rates, with DenseNet201 and MobileNetV2 recording 98.89% and 98.57%, respectively, according to the findings analysis. The findings are discussed with a focus on model architecture and choice as crucial elements in the success of disease classification. The anticipated future efforts will focus on investigating imbalanced categorization and ensemble learning. To sum up, this study contributes pertinent knowledge to the advancement of automated disease identification in agriculture, which is important for global food security