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Optimizing Crop Recommendations with Improved Deep Belief Networks: A Multi model Approach
Authors
1 Professor, Department of Computer Science and Engineering, Rajarajeswari College of Engineering, Bangalore, Karnataka, India. 2 3 4 Department of Computer Science and Engineering, Rajarajeswari College of Engineering, Bangalore, Karnataka, India.
Article Information
- Published In
- Volume 6, Issue 6 (2025)
- Published Online
- November-December 2025
- Pages
- 78-82
- Article Type
- Original Article
- Check for updates
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References
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2. S. S. Kamble, A. Gunasekaran, and S. A. Gawankar, ‘‘Achieving sustainable performance in a data-driven agriculture supply chain: A review for research and applications,’’ Int. J. Prod. Econ., vol. 219,pp. 179–194, Jan. 2020.
3. F.-H. Tseng, H.-H. Cho, and H.-T. Wu, ‘‘Applying big data for intelligent agriculture-based crop selection analysis,’’ IEEE Access, vol. 7, pp. 116965–116974, 2019.
4. P. Jayashankar, W. J. Johnston, S. Nilakanta, and R. Burres, ‘‘Co-creation of value-in-use through big data technology-a B2B agricultural perspective,’’ J. Bus. Ind. Marketing, vol. 35, no. 3, pp. 508–523, Sep. 2019.
5. T. van Klompenburg, A. Kassahun, and C. Catal, ‘‘Crop yield prediction using machine learning: A systematic literature review,’’ Comput. Electron. Agricult., vol. 177, Oct. 2020, Art. no. 105709.
6. G. E. Hinton, ‘‘A practical guide to training restricted Boltzmann machines,’’ Momentum, vol. 9, no. 1, p. 926, Jan. 2010.
7. S. Jana, S. Jana, and A. Begum, ‘‘Design and analysis of pepper leaf disease detection using deep belief network,’’ Eur. J. Mol. Clin. Med., vol. 7, no. 9, pp. 1724–1731, Dec. 2020.
8. A. Kumar, S. Sarkar, and C. Pradhan, ‘‘Recommendation system for crop identification and pest control technique in agriculture,’’in Proc. Int. Conf. Commun. Signal Process. (ICCSP),Apr.2019,pp.0185–0189.
9. R. Llugsi, S. E. Yacoubi, A. Fontaine, and P. Lupera, ‘‘Comparison between Adam, AdaMax and Adam W optimizers to implement a weather forecast based on neural networks for the Andean city of Quito,’’ in Proc. IEEE 5th Ecuador Tech. Chapters Meeting(ETCM),Oct.2021,pp.1–6.
10. K. Shaukat, S. Luo, S. Chen, and D. Liu, ‘‘Cyber threat detection using machine learning techniques: A performance evaluation perspective,’’ in Proc. Int. Conf. Cyber War.
2. S. S. Kamble, A. Gunasekaran, and S. A. Gawankar, ‘‘Achieving sustainable performance in a data-driven agriculture supply chain: A review for research and applications,’’ Int. J. Prod. Econ., vol. 219,pp. 179–194, Jan. 2020.
3. F.-H. Tseng, H.-H. Cho, and H.-T. Wu, ‘‘Applying big data for intelligent agriculture-based crop selection analysis,’’ IEEE Access, vol. 7, pp. 116965–116974, 2019.
4. P. Jayashankar, W. J. Johnston, S. Nilakanta, and R. Burres, ‘‘Co-creation of value-in-use through big data technology-a B2B agricultural perspective,’’ J. Bus. Ind. Marketing, vol. 35, no. 3, pp. 508–523, Sep. 2019.
5. T. van Klompenburg, A. Kassahun, and C. Catal, ‘‘Crop yield prediction using machine learning: A systematic literature review,’’ Comput. Electron. Agricult., vol. 177, Oct. 2020, Art. no. 105709.
6. G. E. Hinton, ‘‘A practical guide to training restricted Boltzmann machines,’’ Momentum, vol. 9, no. 1, p. 926, Jan. 2010.
7. S. Jana, S. Jana, and A. Begum, ‘‘Design and analysis of pepper leaf disease detection using deep belief network,’’ Eur. J. Mol. Clin. Med., vol. 7, no. 9, pp. 1724–1731, Dec. 2020.
8. A. Kumar, S. Sarkar, and C. Pradhan, ‘‘Recommendation system for crop identification and pest control technique in agriculture,’’in Proc. Int. Conf. Commun. Signal Process. (ICCSP),Apr.2019,pp.0185–0189.
9. R. Llugsi, S. E. Yacoubi, A. Fontaine, and P. Lupera, ‘‘Comparison between Adam, AdaMax and Adam W optimizers to implement a weather forecast based on neural networks for the Andean city of Quito,’’ in Proc. IEEE 5th Ecuador Tech. Chapters Meeting(ETCM),Oct.2021,pp.1–6.
10. K. Shaukat, S. Luo, S. Chen, and D. Liu, ‘‘Cyber threat detection using machine learning techniques: A performance evaluation perspective,’’ in Proc. Int. Conf. Cyber War.
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