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- Vol. 5 Issue 3
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Smart Farmers Equipment Rental and Pest Classification System: an Integrated Web Platform Using Machine Learning and Deep Learning
Authors
1 Assistant Professor, Department of Information Science and Engineering, Bapuji Institute of Engineering and Technology, Davangere, Karnataka, India. 2 3 4 5 Bachelor of Engineering, Information Science and Engineering, Bapuji Institute of Engineering and Technology, Davangere, Karnataka, India.
Article Information
- Published In
- Volume 5, Issue 3 (2026)
- Published Online
- September-December 2026
- Pages
- 469-475
- Article Type
- Original Article
- Check for updates
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References
1. S. P. Mohanty, D. P. Hughes and M. Salathé, “Using deep learning for image-based plant disease detection,” Frontiers in Plant Science, vol. 7, p. 1419, 2016.
2. Y. LeCun, Y. Bengio and G. Hinton, “Deep learning,” Nature, vol. 521, no. 7553, pp. 436–444, 2015.
3. A. Krizhevsky, I. Sutskever and G. E. Hinton, “ImageNet classification with deep convolutional neural networks,” Advances in Neural Information Processing Systems, vol. 25, 2012.
4. K. G. Liakos, P. Busato, D. Moshou, S. Pearson and D. Bochtis, “Machine learning in agriculture: A review,” Sensors, vol. 18, no. 8, p. 2674, 2018.
5. F. Pedregosa et al., “Scikit-learn: Machine learning in Python,” Journal of Machine Learning Research, vol. 12, pp. 2825–2830, 2011.
6. M. Abadi et al., “TensorFlow: A system for large-scale machine learning,” in Proc. 12th USENIX Symposium on Operating Systems Design and Implementation (OSDI), 2016, pp. 265–283.
7. N. M. Trendov, S. Varas and M. Zeng, Digital Technologies in Agriculture and Rural Areas — Briefing Paper. Rome: Food and Agriculture Organization of the United Nations, 2019.
8. F. Chollet, Deep Learning with Python. Shelter Island, NY: Manning Publications Co., 2018.
9. Pallets Projects, “Flask Web Framework — Official Documentation.”
10. Government of India, Ministry of Agriculture and Farmers Welfare, State of Indian Agriculture 2022–23. New Delhi: Department of Agriculture and Cooperation, 2023.
2. Y. LeCun, Y. Bengio and G. Hinton, “Deep learning,” Nature, vol. 521, no. 7553, pp. 436–444, 2015.
3. A. Krizhevsky, I. Sutskever and G. E. Hinton, “ImageNet classification with deep convolutional neural networks,” Advances in Neural Information Processing Systems, vol. 25, 2012.
4. K. G. Liakos, P. Busato, D. Moshou, S. Pearson and D. Bochtis, “Machine learning in agriculture: A review,” Sensors, vol. 18, no. 8, p. 2674, 2018.
5. F. Pedregosa et al., “Scikit-learn: Machine learning in Python,” Journal of Machine Learning Research, vol. 12, pp. 2825–2830, 2011.
6. M. Abadi et al., “TensorFlow: A system for large-scale machine learning,” in Proc. 12th USENIX Symposium on Operating Systems Design and Implementation (OSDI), 2016, pp. 265–283.
7. N. M. Trendov, S. Varas and M. Zeng, Digital Technologies in Agriculture and Rural Areas — Briefing Paper. Rome: Food and Agriculture Organization of the United Nations, 2019.
8. F. Chollet, Deep Learning with Python. Shelter Island, NY: Manning Publications Co., 2018.
9. Pallets Projects, “Flask Web Framework — Official Documentation.”
10. Government of India, Ministry of Agriculture and Farmers Welfare, State of Indian Agriculture 2022–23. New Delhi: Department of Agriculture and Cooperation, 2023.
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