Enhancing Disease Detection With Weight Initialization and Residual Connections Using LeafNet for Groundnut Leaf Diseases

Abstract

Groundnut is a crucial crop on a global scale and is facing considerable decrease in yield due to leaf diseases. This makes it essential for the prompt identification of these diseases to ensure continuous agricultural productivity. In this research article, LeafNet, a novel architecture, is introduced for detecting six major classifications of groundnut leaf, including diseases such as Early Rust, Early Leaf Spot, Nutrition Deficiency, Rust, Late Leaf Spot, as well as Healthy Leaf, from a dataset comprising 10,361 images. The performance of the proposed architecture is evaluated through a variety of subjective and objective assessment techniques to validate its efficiency. The robust performance of LeafNet can be attributed to utilizing residual networks and weight initialization techniques within its ensembling process. The proposed architecture undergoes comparison with various neural network architectures introduced in state-of-the-art techniques, demonstrating an exceptional accuracy. The proposed architecture achieved a test accuracy of 97.225%, precision of 97.365%, recall of 97.225%, F1-score of 97.225%, and MCC of 96.700%. To assess the adaptability and generalizability of the proposed architecture, it is evaluated with various leaf disease datasets along with groundnut leaf diseases. This research makes a substantial contribution to agricultural science by presenting a robust deep learning-based architecture for precise identification of groundnut leaf diseases. The potential impact of this method on agricultural practices is significant, facilitating timely disease management and reinforcing agricultural sustainability.

Keywords

• LeafNet
• groundnut disease detection
• deep learning
• image processing