July 30, 2025

An Integrated Diagnostic Model for Identifying and Diagnosing Groundnut Leaf Diseases

Key Points

The hybrid models ResNet50 and VGG16 achieve high accuracy rates of 98.72% and 98.22% for disease identification in groundnut leaves.
Image evaluation includes both infected and uninfected groundnut leaf images categorized into six distinct folders.
Adopting deep learning methods, particularly convolutional neural networks, enhances accuracy in diagnosing leaf diseases for timely crop management.
The findings support integrating advanced technologies in agricultural practices to mitigate losses due to plant diseases.

Abstract

Groundnut (Arachis hypogaea L.), also known as peanut, serves as a significant source of edible oil and protein, making it essential for the agricultural economy and food security. However, groundnut plants are susceptible to various diseases, notably leaf diseases. Early and accurate identification of these diseases is crucial for implementing timely management strategies to mitigate losses. In recent years, advancements in deep learning especially convolutional neural networks (CNNs), have revolutionized image recognition tasks, including plant disease identification. This study employed the use of ResNet50 and VGG16 Convolutional Neural Networks (CNNs) with Bayesian Optimization (BO). The dataset comprises both infected and uninfected groundnut leaf images, categorized into six folders based on their status. Performance evaluation demonstrates that ResNet50 and VGG16 model’s ability to accurately identify groundnut leaves diseases. Results indicate that the hybrid models ResNet50 and VGG16 effectively captures and distinguishes between infected and uninfected groundnut leaves disease with a high degree of accuracy of 98.72% and 98.22% respectively.

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