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IoT-Enabled Crop Yield Prediction Using Machine Learning Techniques
Authors
1 3 4 5 Department of Artificial Intelligence and Machine Learning, Sasi Institute of Technology and Engineering Tadepalligudem, Andhra Pradesh, India. 2 Associate Professor, Department of Artificial Intelligence and Machine Learning, Sasi Institute of Technology and Engineering Tadepalligudem, Andhra Pradesh, India.
Article Information
- Organised By
- Madanapalle Institute of Technology & Science (MITS)
- Conference Date
- 9 & 10 March 2026
- Pages
- 135-139
- Article Type
- Research Article
- Check for updates
-
References
1. J. Karthikeyan and A. Murugan, “Enhancing crop yield prediction using machine and deep learning models,” SEEJPH, vol. 24, pp.
A. Islam et al., “Crop yield prediction using IoT and ensemble machine learning,” in Proc. ICCIT, 2023, pp. 1–6.
2. P. Delfani et al., “Integrative approaches in agriculture using IoT and AI,” Precision Agriculture, vol. 25, no. 4, pp. 2589
3. V. Ramesh and P. Kumaresan, “Machine learning and deep learning for crop yield prediction: A review,” Nat. Env. Poll. Tech., vol. 23, no. 4, pp. 2071
4. M. K. Siam, Sushil J. Jangirala, and S. Senthil Kumar, “IoT-based smart device for crop yield prediction,”
5. F. M. Talaat, “Crop yield prediction algorithm using IoT and active learn- ing,” Neural Computing and Applications, vol. 35, pp. 17281–17292
6. J. Gupta et al., “Machine learning approach for crop yield prediction and recommendation,” MIT IJCSIT, vol. 7, no. 1, pp.
A. Kamilaris and F. Prenafeta-Boldu´, “Deep learning in agriculture,” Computers and Electronics in Agriculture, vol. 147, pp
7. S. Shinde and S. Shah, “Review of machine learning in crop yield prediction,” Int. J. Eng. Res., vol. 9, pp. 110–115, 2020.
8. R. Feng et al., “Crop yield prediction using Random Forest,” IEEE Access, vol. 7, pp. 180326–180335, 2019.
A. Kumar et al., “Machine learning-based crop prediction model,” Int. J. Comput. Sci., vol. 8, no. 2, pp. 34–40, 2019.
9. L. Chlingaryan et al., “Machine learning in agriculture: A review,” Sensors, vol. 18, no. 12, pp. 1–29, 2018.
10. S. Sarker et al., “Predicting crop yield using climate data,” Agricultural Systems, vol. 184, p. 102903, 2020.
11. P. Lal et al., “Weather-based yield prediction using ML,” Environmental Modelling & Software, vol. 115, pp. 102–112, 2019.
12. R. Ravi et al., “Smart agriculture using IoT and ML,” Procedia Com- puter Science, vol. 171, pp. 1789–1796, 2020.
13. K. Patil and M. Shirkhedkar, “Agricultural prediction using ML tech- niques,” Int. J. Sci. Tech., vol. 6, pp. 22–28, 2018.
A. Ali and M. Chaudhary, “Crop yield forecasting using ensemble learning,” IEEE Access, vol. 8, pp. 191287–191297, 2020.
14. S. Zhou et al., “Evaluation of ML models for agricultural prediction,”Computers in Agriculture, vol. 3, pp. 45–56, 2017.
15. M. Misra et al., “IoT-based smart farming systems,” IEEE Internet of Things Journal, vol. 7, no. 4, pp. 2758–2768, 2020.
A. Islam et al., “Crop yield prediction using IoT and ensemble machine learning,” in Proc. ICCIT, 2023, pp. 1–6.
2. P. Delfani et al., “Integrative approaches in agriculture using IoT and AI,” Precision Agriculture, vol. 25, no. 4, pp. 2589
3. V. Ramesh and P. Kumaresan, “Machine learning and deep learning for crop yield prediction: A review,” Nat. Env. Poll. Tech., vol. 23, no. 4, pp. 2071
4. M. K. Siam, Sushil J. Jangirala, and S. Senthil Kumar, “IoT-based smart device for crop yield prediction,”
5. F. M. Talaat, “Crop yield prediction algorithm using IoT and active learn- ing,” Neural Computing and Applications, vol. 35, pp. 17281–17292
6. J. Gupta et al., “Machine learning approach for crop yield prediction and recommendation,” MIT IJCSIT, vol. 7, no. 1, pp.
A. Kamilaris and F. Prenafeta-Boldu´, “Deep learning in agriculture,” Computers and Electronics in Agriculture, vol. 147, pp
7. S. Shinde and S. Shah, “Review of machine learning in crop yield prediction,” Int. J. Eng. Res., vol. 9, pp. 110–115, 2020.
8. R. Feng et al., “Crop yield prediction using Random Forest,” IEEE Access, vol. 7, pp. 180326–180335, 2019.
A. Kumar et al., “Machine learning-based crop prediction model,” Int. J. Comput. Sci., vol. 8, no. 2, pp. 34–40, 2019.
9. L. Chlingaryan et al., “Machine learning in agriculture: A review,” Sensors, vol. 18, no. 12, pp. 1–29, 2018.
10. S. Sarker et al., “Predicting crop yield using climate data,” Agricultural Systems, vol. 184, p. 102903, 2020.
11. P. Lal et al., “Weather-based yield prediction using ML,” Environmental Modelling & Software, vol. 115, pp. 102–112, 2019.
12. R. Ravi et al., “Smart agriculture using IoT and ML,” Procedia Com- puter Science, vol. 171, pp. 1789–1796, 2020.
13. K. Patil and M. Shirkhedkar, “Agricultural prediction using ML tech- niques,” Int. J. Sci. Tech., vol. 6, pp. 22–28, 2018.
A. Ali and M. Chaudhary, “Crop yield forecasting using ensemble learning,” IEEE Access, vol. 8, pp. 191287–191297, 2020.
14. S. Zhou et al., “Evaluation of ML models for agricultural prediction,”Computers in Agriculture, vol. 3, pp. 45–56, 2017.
15. M. Misra et al., “IoT-based smart farming systems,” IEEE Internet of Things Journal, vol. 7, no. 4, pp. 2758–2768, 2020.
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