Efficient Categorization and Prediction of Rice Leaf Diseases using Machine Learning and Inception V3 with Transfer Learning - M. Jothika (International Journal of Intelligent Systems and Applications in Engineering, 2024)
Rice serves as a crucial dietary staple for nearly half of the world’s populace; however, the identification of foliar diseases poses a considerable obstacle to agricultural output. This investigation introduces a proficient methodology for the classification and forecasting of rice foliar diseases by employing machine learning techniques in conjunction with the Inception V3 architecture via transfer learning. Our strategy leverages the capabilities of deep learning while concurrently reducing computational requirements, rendering it appropriate for implementation in practical agricultural settings. To fortify the model, it augmented an existing dataset pertaining to rice leaf diseases by amalgamating two separate datasets and incorporating Ninety-five meticulously annotated images sourced from publicly accessible platforms were utilized, thus creating a more robust training dataset. The model achieves astonishing performance metrics, boasting an impressive accuracy of 99.81%, a precision score of 0.99828, a recall rate of 0.99826, and an F1-score of 0.99827 has been achieved, exceeding a multitude of advanced methodologies. In addition, it has developed an extensive crop health monitoring system tailored specifically for agricultural practitioners, accompanied by an open API for the automated classification of newly acquired data samples. This initiative aims to improve the management of rice cultivation and furnish vital resources for the agricultural research community.