Novel Deep Learning Model for Groundnut Leaf Diseases Classification

Abstract

Groundnut, an essential oilseed crop in India, is extremely vulnerable to a range of diseases that can have a considerable impact on its productivity and quality. Conventional disease detection methods typically involve a significant amount of manual work and necessitate specialized expertise, which limits their practicality for general application in agriculture. This work presents GroNet, which is a lightweight version of Inception-ResNet model for classifying groundnut leaf diseases. The model integrates the inception blocks from InceptionV3 with the skip connections from ResNet to improve feature extraction and address the issue of the disappearing gradient. The performance of the model was assessed using a widely recognized groundnut leaf dataset, comparing it to ResNet152, ResNet50, InceptionV3, and MobileNetV2. The experimental results indicate that GroNet outperforms the other examined models, achieving a superior performance with an accuracy that surpasses 99%. The accuracy of the GroNet highlights its potential as an essential tool for farmers and agricultural experts, facilitating prompt and accurate disease identification and therefore enhancing crop management and production.