An effective deep learning framework for diseases prediction to enrich paddy production

Paddy cultivation is frequently threatened by diseases that can drastically reduce yields and compromise crop quality. Conventional methods for disease management often fall short due to their reliance on manual inspection and limited data availability. Addressing this challenge in this proposed system, we introduce an innovative deep learning based framework for the early detection and prediction of paddy diseases, which combines a Convolutional Neural Networks with a Deep Neural Networks to enhance accuracy and is also employed for the early identification of leaf diseases through image data manipulation. Applying filtering and enhancement operations with paddy leaf images which facilitates more accurate disease prediction. This process involves techniques such as Wiener filtering to reduce noise and improve image clarity and also enhance the visibility of leaf disease symptoms. For effective implementation using the above mentioned methodologies, the extraction of more reliable features is possible through preprocessing techniques to attain improved accuracy. This proposed model is meticulously trained and validated using a diverse dataset encompassing images gathered from various paddy fields. Results illustrate that the proposed approach surpasses traditional methods, achieving high precision in both identifying and forecasting plant diseases. This advancement promises to revolutionize paddy cultivation practices by enabling proactive disease management and minimizing agricultural losses. By facilitating timely interventions, the framework supports sustainable agriculture, ensuring healthier yields and enhanced crop resilience against disease outbreaks.