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Time and Space Efficient Multi-Model Convolution Vision Transformer for Tomato Disease Detection from Leaf Images with Varied Backgrounds

Ankita Gangwar1, Vijaypal Singh Dhaka1, Geeta Rani2,*, Shrey Khandelwal1, Ester Zumpano3,4, Eugenio Vocaturo3,4

1 Department of Computer and Communication Engineering, Manipal University Jaipur, Jaipur, India
2 Department of IoT and Intelligent Systems, Manipal University Jaipur, Jaipur, India
3 Department of Computer Engineering, Modelling, Electronics and Systems (DIMES), University of Calabria, Rende (Cosenza), Italy
4 National Research Council, Institute of Nanotechnology (NANOTEC), Rende (Cosenza), Italy

* Corresponding Author: Geeta Rani. Email: email

Computers, Materials & Continua 2024, 79(1), 117-142. https://doi.org/10.32604/cmc.2024.048119

Abstract

A consumption of 46.9 million tons of processed tomatoes was reported in 2022 which is merely 20% of the total consumption. An increase of 3.3% in consumption is predicted from 2024 to 2032. Tomatoes are also rich in iron, potassium, antioxidant lycopene, vitamins A, C and K which are important for preventing cancer, and maintaining blood pressure and glucose levels. Thus, tomatoes are globally important due to their widespread usage and nutritional value. To face the high demand for tomatoes, it is mandatory to investigate the causes of crop loss and minimize them. Diseases are one of the major causes that adversely affect crop yield and degrade the quality of the tomato fruit. This leads to financial losses and affects the livelihood of farmers. Therefore, automatic disease detection at any stage of the tomato plant is a critical issue. Deep learning models introduced in the literature show promising results, but the models are difficult to implement on handheld devices such as mobile phones due to high computational costs and a large number of parameters. Also, most of the models proposed so far work efficiently for images with plain backgrounds where a clear demarcation exists between the background and leaf region. Moreover, the existing techniques lack in recognizing multiple diseases on the same leaf. To address these concerns, we introduce a customized deep learning-based convolution vision transformer model. The model achieves an accuracy of 93.51% for classifying tomato leaf images with plain as well as complex backgrounds into 13 categories. It requires a space storage of merely 5.8 MB which is 98.93%, 98.33%, and 92.64% less than state-of-the-art visual geometry group, vision transformers, and convolution vision transformer models, respectively. Its training time of 44 min is 51.12%, 74.12%, and 57.7% lower than the above-mentioned models. Thus, it can be deployed on (Internet of Things) IoT-enabled devices, drones, or mobile devices to assist farmers in the real-time monitoring of tomato crops. The periodic monitoring promotes timely action to prevent the spread of diseases and reduce crop loss.

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Cite This Article

APA Style
Gangwar, A., Dhaka, V.S., Rani, G., Khandelwal, S., Zumpano, E. et al. (2024). Time and space efficient multi-model convolution vision transformer for tomato disease detection from leaf images with varied backgrounds. Computers, Materials & Continua, 79(1), 117-142. https://doi.org/10.32604/cmc.2024.048119
Vancouver Style
Gangwar A, Dhaka VS, Rani G, Khandelwal S, Zumpano E, Vocaturo E. Time and space efficient multi-model convolution vision transformer for tomato disease detection from leaf images with varied backgrounds. Comput Mater Contin. 2024;79(1):117-142 https://doi.org/10.32604/cmc.2024.048119
IEEE Style
A. Gangwar, V.S. Dhaka, G. Rani, S. Khandelwal, E. Zumpano, and E. Vocaturo "Time and Space Efficient Multi-Model Convolution Vision Transformer for Tomato Disease Detection from Leaf Images with Varied Backgrounds," Comput. Mater. Contin., vol. 79, no. 1, pp. 117-142. 2024. https://doi.org/10.32604/cmc.2024.048119



cc This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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