Smart Farming in Bangladesh: Mobile Application for Tomato Leaf Disease Detection Using a Hybrid VGG16-CNN Model
Article Sidebar
Main Article Content
Abstract: Tomato cultivation (Solanum lycopersicum L.) is highly significant due to its considerable economic value, high consumer demand, and critical role in supporting the livelihoods of farmers in Bangladesh. However, the majority of Bangladeshi farmers rely on traditional, manual methods for detecting tomato leaf diseases, relying on visual inspection and personal experience. Limited resources and a lack of awareness about advanced technologies further hinder the adoption of efficient disease detection methods. Computer vision, a cutting-edge technology, enables the automated identification and classification of tomato leaf diseases, holding significant promise for improving agricultural productivity and farmers’ livelihoods. This study focuses on developing a robust disease detection framework involving image acquisition, preprocessing, and feature extraction using a VGG16-CNN hybrid model, integrated with smartphone applications for real-time detection. To address the limitations faced by local farmers and plant enthusiasts unfamiliar with such technology, a diverse dataset of approximately 16,824 images was created, comprising field images and online sources. The proposed method leverages VGG16 for feature extraction, achieving enhanced performance through additional fine-tuned layers that form a hybrid model. This approach delivers an accuracy of 98%, with an F1 score of 98%. These findings highlight the potential of the proposed system to significantly mitigate the impacts of tomato leaf diseases, thereby improving tomato cultivation and production outcomes.
Downloads
Downloads
References
IndexBox, “Bangladesh’s tomato market report 2024 - prices, size, fore-cast, and companies,” https://www.indexbox.io/store/bangladesh-tomato-market-report-analysis-and-forecast-to-2020/, accessed: 2024-12-23.
Fresh Plaza, “Tomato farmers bangladesh see bumper production and fair prices,” https://www.freshplaza.com/ europe/article/9589231/tomato- farmers-bangladesh-see-bumper-production-and-fair-prices/, accessed: 2024-12-23.
Bangladesh Agro-Meteorological Information Service (BAMIS),“Tomato,” https://www.bamis.gov.bd/en/ crops/ view/ 13/,accessed:2024-2-23.
L. Depenbusch, T. Sequeros, P. Schreinemachers, M. Sharif, K. Man-namparambath, N. Uddin, and P. Hanson, “Tomato pests and diseases in bangladesh and india: farmers’ management and potential economic gains from insect resistant varieties and integrated pest management,” International Journal of Pest Management, pp. 1–15, 2023. DOI: https://doi.org/10.1080/09670874.2023.2252760
S. P. Mohanty, D. P. Hughes, and M. Salath´e, “Using deep learning for mage-based plant disease detection,” Frontiers in plant science, vol. 7, p. 1419, 2016. DOI: https://doi.org/10.3389/fpls.2016.01419
K. P. Ferentinos, “Deep learning models for plant disease detection and diagnosis,” Computers and electronics in agriculture, vol. 145, pp. 311–318, 2018. DOI: https://doi.org/10.1016/j.compag.2018.01.009
A. Sofiane, B. Mostefa, and B. Soumia, “Deep learning model based on vgg16 for tomato leaf diseases detection and categorization,” in 2024 2nd International Conference on Electrical Engineering and Automatic Control(ICEEAC). IEEE, 2024, pp. 1–6. DOI: https://doi.org/10.1109/ICEEAC61226.2024.10576347
M. Agarwal, A. Singh, S. Arjaria, A. Sinha, and S. Gupta, “Toled: Tomato leaf disease detection using convolution neural network,” Pro-cedia Computer Science, vol. 167, pp. 293–301, 2020. DOI: https://doi.org/10.1016/j.procs.2020.03.225
S. AM, P. JOE IR, S. Venkatraman, P. Kumar S et al., “Improved tomato leaf disease classification through adaptive ensemble models with exponential moving average fusion and enhanced weighted gradient optimization,” Frontiers in Plant Science, vol. 15, p. 1382416, 2024. DOI: https://doi.org/10.3389/fpls.2024.1382416
T. Ramcharan, O. Nolan, C. Y. Lai, N. Prabhu, R. Krishnamurthy, A. G.Richter, D. Jyothish, H. K.Kanthimathinathan, S. B. Welch, S. Hackett et al., “Paediatric inflammatory multisystem syndrome: temporally associated with sars-cov-2 (pims-ts): cardiac features, management and short-term outcomes at a uk tertiary paediatric hospital,” Pediatric cardiology, vol. 41, pp. 1391–1401, 2020. DOI: https://doi.org/10.1007/s00246-020-02391-2
A. K. Rangarajan, R. Purushothaman, and A. Ramesh, “Tomato crop disease classification using pre-trained deep learning algorithm,” Proce-dia computer science, vol. 133, pp. 1040–1047, 2018. DOI: https://doi.org/10.1016/j.procs.2018.07.070
E. D. N. Iffaty, P. Sukmasetya, M. R. A. Yudianto et al., “Transfer learning vgg16 for image classification of tomato leaf disease,” in Proceedings Universitas Muhammadiyah Yogyakarta Undergraduate Conference, vol. 3, no. 2, 2023, pp. 35–46.
D. Hughes, M. Salath´e et al., “An open access repository of images on plant health to enable the development of mobile disease diagnostics,”arXiv preprint arXiv:1511.08060, 2015.
A. Debnath, M. M. Hasan, M. Raihan, N. Samrat, M. M. Alsulami, M. Masud, and A. K. Bairagi, “A smartphone-based detection system for tomato leaf disease using efficientnetv2b2 and its explainability with artificial intelligence (ai),” Sensors, vol. 23, no. 21, p. 8685, 2023. DOI: https://doi.org/10.3390/s23218685
A. Sobur, M. H. Kabir, M. Z. Hossain, A. Hossain, and I. C. Rana, “En-hancing tomato leaf disease detection in varied climates: A comparative study of advanced deep learning models with a novel hybrid approach,” International Journal of Creative Research Thoughts— IJCRT, vol. 12, no. 2, 2024.
K. Bhalerao, “Tomato leaf dataset,” 2021, ac-cessed: December 27, 2024. [Online]. Available: https://www.kaggle.com/datasets/kaustubhb999/tomatoleaf
M. Saravanan and K. S, “Tomato leaf dataset for plant disease detection,” 2020, accessed: December 27, 2024. [Online]. Available: https://data.mendeley.com/datasets/zfv4jj7855/1
Geeks for Geeks, “Vgg-16 cnn model,” 2024, accessed: 2024-12-28.[Online]. Available: https://www.geeksforgeeks.org/vgg-16-cnn-model/
Data Camp, “Introduction to convolutional neural networks (cnns),” 2024, accessed: 2024-12-28. [Online]. Avail-able: https://www.datacamp.com/tutorial/introduction-to-convolutional- neural-networks-cnns
K. Simonyan and A. Zisserman, “Very deep convolutional networks for large-scale image recognition,” arXiv preprint arXiv: 1409.1556, 2014.[Online]. Available: https://arxiv.org/abs/1409.1556
This work is licensed under a Creative Commons Attribution 4.0 International License.
All articles published in our journal are licensed under CC-BY 4.0, which permits authors to retain copyright of their work. This license allows for unrestricted use, sharing, and reproduction of the articles, provided that proper credit is given to the original authors and the source.