PaddyNet: An Improved Deep Convolutional Neural Network for Automated Disease Identification on Visual Paddy Leaf Images

Abstract

Timely disease diagnosis in paddy is fundamental to preventing yield losses and ensuring an adequate supply of rice for a rapidly rising worldwide population. Recent advancements in deep learning have helped overcome the limitations of unsuper-vised learning methods. This paper proposes a novel PaddyNet model for enhanced accuracy in paddy leaf disease detection. The PaddyNet model, developed using 17 layers, captures and models patterns of different disease symptoms present in paddy leaf images. The effectiveness of the novel model is verified by applying a large dataset comprising 16,225 paddy leaf datasets across 13 classes, including a normal class and 12 disease classes. The performance results show that the new PaddyNet model classifies paddy leaf disease images effectively with 98.99% accuracy and a dropout value of 0.4.