A Cutting Edge Deep Learning Models for Paddy Leaf Disease Detection and Classification

The world’s main grain crop is rice (Oryza sativa). It provides the majority of the world’s population with nourishment and energy. Numerous rice leaf diseases are a major threat to the world’s agricultural output because they deteriorate crop quality in addition to lowering crop yield. Traditional methods of classifying and managing diseases mostly rely on laborious and subjective manual examination procedures. However, recent advancements in computer vision methods have made it possible to construct automated systems that can recognize illnesses from picture and video data. The paper emphasizes the potential application of cutting-edge models like Detectron2 to address issues with agricultural leaf disease detection. Together with other cutting-edge models, Detectron2 provides a promising means of enhancing the accuracy and effectiveness of disease diagnosis and control techniques. The use of automated disease detection systems holds the potential to reduce subjectivity in the evaluation of illnesses, optimize the allocation of resources in agriculture, and reduce dependence on human labour. These systems are able to continually learn from and adapt to changing disease patterns by incorporating complex algorithms and machine learning techniques, ensuring robust and stable agricultural operations.