Soybean yield prediction leveraging advanced ensemble machine learning models

Authors

  • RAM MANOHAR PATEL Institute of Computer Science, Vikram University, Ujjain, Madhya Pradesh, India
  • KAMAL BUNKAR Institute of Computer Science, Vikram University, Ujjain- 456010, Madhya Pradesh, India https://orcid.org/0000-0003-2774-1864

DOI:

https://doi.org/10.54386/jam.v27i2.2971

Keywords:

Soybean, Yield prediction, Machine learning, Deep learning, Madhya Pradesh

References

Arvind, K.S., Vashisth, A., Krishanan, P., and Das, B. (2022). Wheat yield prediction based on weather parameters using multiple linear, neural network and penalised regression models. J. Agrometeorol., 24 (1): 18-25. https://doi.org/10.54386/jam.v24i1.1002

Bhagat, K.P., Bal, S.K., Singh, Y., Potekar, S., Saha, S., Ratnakumar, P., Wakchaure, G.C., and Minhas, P.S. (2017). Effect of reduced PAR on growth and photosynthetic efficiency of soybean genotypes. J. Agrometeorol., 19(1):1-9. https://doi.org/10.54386/jam.v19i1.734

Dakhore, K.K., Kadam, Y.E., Kadam, D.R., Mane, R.B., Kapse, P.S., and Bal, S.K. (2024). Crop-weather relationship of soybean in Marathwada region of Maharashtra. J. Agrometeorol., 26(2):163-167. https://doi.org/10.54386/jam.v26i2.2438

Dhinakaran, S., and Thangavel, C. (2024). Weather based paddy yield prediction using machine learning regression algorithms. J. Agrometeorol., 26(3):344-348. https://doi.org/10.54386/jam.v26i3.2598

DES (2024). Directorate of Economics and Statistics (DES), Ministry of Agriculture and Farmers Welfare, Government of India. https://eands.dacnet.nic.in

FAI. (2024). Fertilizer Association of India (FCI). Fertilizer Statistics (1990–2022). New Delhi: Fertilizer Association of India. Accessed from the ICAR–National Soybean Research Institute Library, Indore.

ICRISAT (2024). District-level crop production statistics for India (1966–2022). International Crops Research Institute for the Semi-Arid Tropics. https://data.icrisat.org/dld/

IMD. (2024). India Meteorological Department (IMD), Pune, Daily Gridded Rainfall Data (0.25°×0.25°) for India (1990–2022).

NASA (2024). National Aeronautics and Space Administration (NASA), Prediction Of Worldwide Energy Resources (POWER) Project, NASA POWER agro-climatology data for 1990–2022. NASA Langley Research Center. https://power.larc.nasa.gov

Patel, R.M., Sharma, P., and Sharma, A.N. (2019). Prediction of girdle beetle (Oberiopsis brevis) infestation through pest-weather model in soybean. J. Entom. Zool.Stud.,7(4):718-723

Setiya, P., Nain, A.S., and Satpathi, A. (2024). Comparative analysis of SMLR, ANN, Elastic net and LASSO based models for rice crop yield prediction in Uttarakhand. Mausam, 75(1):191-196.

Sharma, P., Dadheech, P., Aneja, N., and Aneja, S. (2023). Predicting agriculture yields based on machine learning using regression and deep learning. IEEE Access, 11:11255-111264.

Sridhara, S., Manoj, K.N., Gopakkali, P., Kashyap, G.R., Das, B., Singh, K.K., and Srivastava, A.K. (2023). Evaluation of machine learning approaches for prediction of pigeon pea yield based on weather parameters in India. Int. J. Biometeorol., 67(1):165-180.

Sridhara, S., Soumya, B.R., and Kashyap, G.R. (2024). Multistage sugarcane yield prediction using machine learning algorithms. J. Agrometeorol., 26 (1):37–44. https://doi.org/10.54386/jam.v26i1.2411

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Published

01-06-2025

How to Cite

PATEL, R. M., & BUNKAR, K. (2025). Soybean yield prediction leveraging advanced ensemble machine learning models. Journal of Agrometeorology, 27(2), 227–229. https://doi.org/10.54386/jam.v27i2.2971

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Short Communication

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