Comparative analysis of wheat yield prediction through artificial intelligence, simulation modelling and statistical analysis in Central Punjab

Authors

  • K. K GILL Department of Climate Change and Agricultural Meteorology, Punjab Agricultural University, Ludhiana-141004, Punjab
  • KAVITA BHATT Department of Climate Change and Agricultural Meteorology, Punjab Agricultural University, Ludhiana-141004, Punjab
  • AKANSHA Department of Climate Change and Agricultural Meteorology, Punjab Agricultural University, Ludhiana-141004, Punjab
  • BALJEET KAUR Department of Climate Change and Agricultural Meteorology, Punjab Agricultural University, Ludhiana-141004, Punjab
  • S. S. SANDHU Department of Climate Change and Agricultural Meteorology, Punjab Agricultural University, Ludhiana-141004, Punjab

DOI:

https://doi.org/10.54386/jam.v26i3.2445

References

Ajith, S., Manoj, K. D., Deb, S.G., Pradip, B., Subhendu, B., Shyamal, K., and Ragini, H.R. (2023). Comparative evaluation of penalized regression models with multiple linear regression for predicting rapeseed-mustard yield: Weather-indices based approach. J. Agrometeorol., 25(3): 432–439. https://doi.org/10.54386/jam.v25i3.2185

Gill, K. K., Sandhu, S. S., Divya and Mishra, S. K. (2018). Pre-harvest wheat yield prediction using CERES-wheat model for Ludhiana district, Punjab, India. J. Agrometeorol., 20(4): 319-21. https://doi.org/10.54386/jam.v20i4.574

Gomez, D., Salvador, P., Sanz, J., and Casanova, J. L. (2021). Modelling wheat yield with antecedent information, satellite and climate data using machine learning methods in Mexico. Agric. For. Meteorol., 300, 1-8.

Ghosh, K., R. Balasubramanian, S. Bandopadhyay, N. Chattopadhyay, K.K. Singh, & L. S. Rathore. (2014). Development of crop yield forecast models under FASAL- a case study of kharif rice in West Bengal. J. Agrometeorol., 16(1): 1–8. https://doi.org/10.54386/jam.v16i1.1479

Krithikha, S. S., and Velammal B. (2022). A comprehensive approach on predicting the crop yield using hybrid machine learning algorithms. J. Agrometeorol., 24(2);179–185. https://doi.org/10.54386/jam.v24i2.1561

Kumar, S., Attri, S. D., and Singh, K. K. (2019). Comparison of LASSO and stepwise regression technique for wheat yield prediction. J. Agrometeorol., 21(2): 188-92. https://doi.org/10.54386/jam.v21i2.231

Pandey, S. N., and Sinha, B. K. (2006). Plant physiology (4th ed.). Vikas Publishing House Pvt. Ltd, New Delhi (pp. 485-487).

Ritchie, J. T., Godwin, D. C., and Singh, U. (1988). Soil and weather inputs for the IBSNAT crop models. In Proceedings of the IBSNAT symposium: Decision support system for Agrotechnology Transfer Part I (pp. 31-45). University of Hawaii, Hoolulu, Hawaii, USA.

Singh, M. C., Pal, V., Singh, S. P. and Satpute S. (2021). Wheat yield prediction in relation to climatic parameters using statistical model for Ludhiana district of central Punjab. J. Agrometeorol., 23(1):122-26. https://doi.org/10.54386/jam.v23i1.97

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Published

01-09-2024

How to Cite

GILL, K. K., BHATT, K., AKANSHA, BALJEET KAUR, & S. S. SANDHU. (2024). Comparative analysis of wheat yield prediction through artificial intelligence, simulation modelling and statistical analysis in Central Punjab. Journal of Agrometeorology, 26(3), 377–379. https://doi.org/10.54386/jam.v26i3.2445

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