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CottonNet-MHA: a multi-head attention-based deep learning framework for cotton disease detection
Mostaque Md Morshedur Hassan1, Asmita Ray2, Munsifa Firdaus Khan Barbhuyan3
1Department of Computer Science and Engineering, Eudoxia Research University, New Castle (City of Newark), Delaware, DE, United States.
Frontiers in Plant Science
|December 8, 2025
Summary
This study introduces CottonNet-MHA, a deep learning model for accurate cotton crop disease detection. It enhances feature learning and interpretability, outperforming traditional models for improved agricultural yields.
Area of Science:
- Agricultural Science
- Computer Science
- Plant Pathology
Background:
- Cotton is a vital cash crop in India, threatened by diseases and pests.
- Accurate disease identification is crucial for maintaining crop health and yield.
- Current methods for disease detection can be time-consuming and less accurate.
Purpose of the Study:
- To develop a deep learning framework for automated cotton crop disease identification.
- To enhance disease detection accuracy and model interpretability.
- To create a practical tool for real-world agricultural applications.
Main Methods:
- Developed CottonNet-MHA, a novel deep learning framework utilizing multi-head attention mechanisms.
- Evaluated performance against benchmark models like VGG16, VGG19, InceptionV3, Xception, and MobileNet.
- Employed Gradient-weighted Class Activation Mapping (Grad-CAM) for model interpretability and visualization.
- Deployed the trained model via a web-based application for practical use.
Main Results:
- CottonNet-MHA demonstrated superior accuracy and efficiency in detecting cotton diseases compared to benchmark models.
- Multi-head attention mechanisms improved feature learning and highlighted diseased regions.
- Grad-CAM visualization confirmed the model's ability to focus on affected areas, enhancing trust.
- The web application facilitated real-world deployment and live disease monitoring.
Conclusions:
- CottonNet-MHA offers an automated, accurate, and interpretable solution for cotton disease diagnosis.
- The framework's success suggests potential for adaptation to other crop disease detection systems.
- The developed web platform supports practical implementation in agricultural settings, aiding farmers.
