Machine Learning-Based Rice Fungal Disease Management

Machine Learning-Based Rice Fungal Disease Management

Authors- M.Tech. Scholar Richa Sharma, Assistant Professor Aarti

Abstract-Rice is one of the most important staple crops in the world, feeding billions of people every day. However, rice cultivation is threatened by various fungal diseases that cause significant losses in yield and quality. In recent years, machine learning (ML) techniques have been applied to develop decision support systems (DSS) for crop disease management. In this study, we present the development of an ML-based DSS for rice fungal disease management. The system was trained using a dataset of images and associated metadata of rice plants infected with various fungal diseases. The DSS was designed to provide real-time diagnosis and management recommendations based on the input image of a diseased rice plant. The accuracy of the DSS was evaluated using a test dataset of images of rice plants with known fungal infections. The results show that the ML-based DSS can accurately diagnose and provide management recommendations for various rice fungal diseases. The system has the potential to be used as a tool for rice farmers and agricultural extension workers to manage fungal diseases in rice crops.

 
DOI: /10.61463/ijset.vol.12.issue2.512