Title: Robust cotton leaf disease classification using hybrid deep learning approach with multi-loss function

Authors: Sailaja Madhu; V. Ravisankar

Addresses: Department of CSE, GITAM Deemed to be University, Hyderabad 502329, India ' Department of CSE, GITAM Deemed to be University, Hyderabad 502329, India

Abstract: This paper presents an advanced deep learning framework for the early detection of cotton leaf diseases. Pre-processing is performed using an adaptive median filter to remove noise from input images, enhancing clarity and quality. The improved images are then processed by a hybrid model combining the CN2-SE module and a modified Inception V3 network. The CN2-SE module extracts features, while the modified Inception V3 network also extracts complementary features. These features are merged into a unified set and passed through a multi-layer perceptron (MLP) for classification, which employs a hybrid multi-loss function to ensure improved accuracy while preventing overfitting. Tailored to address both sustainability challenges and global cotton demands, this approach achieves an outstanding classification accuracy of 99.41%, with strong performance in sensitivity, specificity, and recall. By advancing precision agriculture, this research provides an effective solution for automating cotton crop management and enhancing productivity and sustainability in cotton farming.

Keywords: robust cotton leaf disease; CN2-SE; MLP; multi-layer perceptron with a multi-loss function; bacterial blight; leaf curl virus; various fungal infections.

DOI: 10.1504/IJSISE.2026.152509

International Journal of Signal and Imaging Systems Engineering, 2026 Vol.14 No.3, pp.147 - 164

Received: 20 Jun 2025
Accepted: 12 Nov 2025
Published online: 24 Mar 2026 *

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