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Home / Archives / Volume-6 / Issue-3 / Article-8

Volume - 6 | Issue - 3 | september 2024

Enhancing Paddy Leaf Disease Classification using CNN and MobileNetV2 Open Access
Rohan Tiwari  , Neha Vora  188
Pages: 324-340
Cite this article
Tiwari, Rohan, and Neha Vora. "Enhancing Paddy Leaf Disease Classification using CNN and MobileNetV2." Journal of Soft Computing Paradigm 6, no. 3 (2024): 324-340
Published
15 October, 2024
Abstract

Paddy farming, a cornerstone of global agriculture, faces significant threats from various diseases that affect the crop yield. This research presents a novel approach for detecting paddy leaf diseases using advanced deep learning techniques, specifically transfer learning with the MobileNetV2 architecture. The methodology involves the utilization of a comprehensive dataset consisting of paddy leaf images across multiple disease classes. Data augmentation was extensively employed to address the limitations posed by the dataset size. Both basic and advanced models were trained, with the advanced model achieving a remarkable validation accuracy of 97%. Additionally, Time-Test Augmentation (TTA) was applied to further enhance the model's performance. This research demonstrates the efficacy of deep learning techniques in agricultural disease detection and highlights potential improvements for future applications.

Keywords

Paddy leaf disease Convolutional Neural Networks Transfer learning Data augmentation MobileNetV2 Image classification

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