Detection of Tomato Leaf Diseases Using Deep Learning and Spatial Attention Mechanisms

Dhanya R, Dr. S. Mythili, X Rexeena and Dhishna Devadas
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Dhanya R: Research Scholar, Department of Computer Science, Karpagam Academy of Higher Education, Coimbatore-21, India.
Dr. S. Mythili: Professor and Head, Department of Computer Science, Karpagam Academy of Higher Education, Coimbatore-21, India.
X Rexeena: Assistant Professor, Department of Computer Science, CMS college of Engineering and Technology, Coimbatore-21, India.
Dhishna Devadas: Assistant Professor, Department of Computer Applications, Bharathamatha college of arts and science, Kozhnijampara, Palakkad.

International Journal of Research and Scientific Innovation, 2025, vol. 12, issue 7, 1144-1153

Abstract: Timely detection of plant diseases is vital for achieving sustainable agriculture and ensuring food security. This study proposes an innovative approach to detecting tomato leaf diseases, utilizing deep learning techniques enhanced with a spatial attention mechanism. Our method tackles the challenge of accurately identifying multiple diseases in complex leaf images taken under diverse environmental conditions. We present a convolutional neural network (CNN) architecture that integrates a spatial attention module, enabling the model to focus on the most relevant areas of each image. This spatial attention mechanism helps the model more effectively differentiate between healthy and diseased leaf regions. Trained on an extensive dataset of tomato leaf images—covering five common diseases as well as healthy samples—our model achieves a 99% accuracy in classifying disease types. The model also generates interpretable spatial attention maps, which highlight key leaf regions contributing to each diagnosis. This approach maintains robust performance across varying conditions, such as lighting, leaf orientations, and differing levels of disease severity. The high accuracy and interpretability of our model make it a powerful tool for automated disease diagnosis in tomato plants, with spatial attention maps enhancing explainability and assisting agronomists in validating results. Extensive experiments confirm the model’s strong generalization to real-world scenarios, marking a significant contribution to precision agriculture. Furthermore, this method has promising potential for adaptation to other crops, supporting more efficient and sustainable farming practices.

Date: 2025
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