Paddy Leaf Disease Detection using Deep Learning Methods

Computerised automated diagnosis of crops disease enables early detection and ensures the quality of crop. Technology advancements in these fields will reduce the loss and increase the overall productivity. Our research work motivated to build a deep learning classification model for paddy leaf disease detection. The model frame work consists of several pre-processing techniques such as denoising, data filtering, and selection of optimizer that best fits the model. Finally, a comparative study of the proposed model’s performance and efficiency was done with different deep learning models. Based on the analysis and observation, it was observed that the proposed model has given promising results for effective leaf disease detection.