Sugarcane yield forecasting using machine learning techniques in Udham Singh Nagar district of Uttarakhand

Authors

  • NEHA CHAND Department of Agrometeorology, GB Pant University of Agriculture & Technology, Uttarakhand, India
  • RAJEEV RANJAN Department of Agrometeorology, GB Pant University of Agriculture & Technology, Uttarakhand, India

DOI:

https://doi.org/10.54386/jam.v27i2.2888

Keywords:

Yield forecasting, ANN (Artificial Neural Networks), Ridge Regression, lasso regression, Stepwise Multiple Linear Regression (SMLR), Elastic Net

References

Das, B., Nair, B., Reddy, V. K. and Venkatesh, P. (2018). Evaluation of multiple linear, neural network and penalised regression models for prediction of rice yield based on weather parameters for west coast of India. Int. J. Biometeorol., 62(10): 1809-1822.

Ghosh, K., Balasubramanian, R., Bandopadhyay, S., Chattopadhyay, N., Singh, K. K. and Rathore, L. S. (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

IISR (2023). Annual Report 2021–2022. ICAR—Indian Institute of Sugarcane Research, Lucknow.

Kuhn, M. (2008). Building predictive models in R using the caret package. J. Statist. Soft., 28: 1-26.

Kumar J, Devi M, Verma D, Malik DP, Sharma A (2021). Pre-harvest forecast of rice yield based on meteorological parameters using discriminant function analysis. J. Agric. Food Res., 5:100194.

Mishra, P., Al Khatib, A. M. G., Sardar, I., Mohammed, J., Karakaya, K., Dash, A. and Dubey, A. (2021). Modeling and forecasting of sugarcane production in India. Sugar Tech, 23(6):1317-1324.

Montgomery, D. C., Peck, E. A. and Vining, G. G. (2021). Introduction to linear regression analysis. John Wiley & Sons.

Piaskowski, J. L., Brown, D. and Campbell, K. G. (2016). Near‐infrared calibration of soluble stem carbohydrates for predicting drought tolerance in spring wheat. Agron. J., 108(1): 285-293.

Rashid, M., Bari, B. S., Yusup, Y., Kamaruddin, M. A. and Khan, N. (2021). A comprehensive review of crop yield prediction using machine learning approaches with special emphasis on palm oil yield prediction. IEEE Access, 9: 63406-63439.

Satpathi, A., Setiya, P., Das, B., Nain, A. S., Jha, P. K., Singh, S. and Singh, S. (2023). Comparative Analysis of Statistical and Machine Learning Techniques for Rice Yield Forecasting for Chhattisgarh, India. Sustainability, 15(3): 2786.

Shahhosseini, M., Hu, G. and Archontoulis, S. V. (2020). Forecasting corn yield with machine learning ensembles. Front. Plant Sci., 11: 1120.

Singh KN, Singh KK, Sudheer K, Sanjeev P, Bishal G. (2019). Forecasting crop yield through weather indices through LASSO. Indian J. Agric. Sci., 89(3):540–544.

Singh, R. S., Patel, C., Yadav, M. K. and Singh, K. K. (2014). Yield forecasting of rice and wheat crops for eastern Uttar Pradesh. J. Agrometeorol., 16(2): 199-202.

Setiya, P., Satpathi, A., Nain, A. S. and Das, B. (2022). Comparison of weather-based wheat yield forecasting models for different districts of Uttarakhand using statistical and machine learning Techniques. J. Agrometeorol., 24(3): 255–261. https://doi.org/10.54386/jam.v24i3.1571

Downloads

Published

01-06-2025

How to Cite

CHAND, N., & RANJAN, R. (2025). Sugarcane yield forecasting using machine learning techniques in Udham Singh Nagar district of Uttarakhand. Journal of Agrometeorology, 27(2), 233–235. https://doi.org/10.54386/jam.v27i2.2888

Issue

Section

Short Communication

Categories