Skip to main navigation menu Skip to main content Skip to site footer
Plant Science Today (PST)

Research Articles

Early Access

Predicting small millets productivity based on machine learning models: A comprehensive study

DOI
https://doi.org/10.14719/pst.7933
Submitted
26 February 2025
Published
24-04-2025
Versions

Abstract

People in rural and urban areas prefer small millet-based food products and they are more popular in markets. Before the green revolution, little millets were widely cultivated for food and fodder, but dietary preferences changed and they were no longer widely planted. Machine learning techniques have been applied in agriculture recently to analyze and predict crop yields. A major concern for farmers during the growing season is estimating their expected yield. In this study, data on area and production for small millets in Tamil Nadu are collected over 50 years. The machine learning models are used to predict productivity with the available small millets dataset. Four different machine learning models are used to estimate small millets productivity. With an accuracy of 95.46 %, a mean absolute error (MAE) of 0.0709, a root mean square error (RMSE) of 0.014 and an R-square value of 0.94, the Random Forest regressor performed better than the other models. The current research helps the farmers in mitigating potential losses, as their financial stability and productivity output are closely related. Additionally, the study provides valuable insights for better planning and implementation. Furthermore, the Random Forest regressor offers insightful information to help farmers maximize their farming techniques and make well-informed judgments.

References

  1. Zhang L, Lei L, Yan D. Comparison of two regression models for predicting crop yield. In: IEEE International Symposium on Geoscience and Remote Sensing (IGARSS). IEEE; 2010. p. 1521-1524. https://doi.org/10.1109/IGARSS.2010.5652764
  2. Sanchez AG, Solis JF, Bustamante WO. Attribute selection impact on linear and nonlinear regression models for crop yield prediction. Sci World J. 2014;2014:1-10. https://doi.org/10.1155/2014/509429
  3. Corrales DC, Corrales JC, Figueroa-Casas A. Towards detecting crop diseases and pest by supervised learning. Ing Univ J. 2015;19(1):207-88. https://doi.org/10.11144/Javeriana.iyu19-1.tdcd
  4. Luminto H. Weather analysis to predict rice cultivation time using multiple linear regression to escalate farmer's exchange rate. In: International Conference on Advanced Informatics, Concepts, Theory, and Applications (ICAICTA). Denpasar, Indonesia; 2017. p. 16-18. https://doi.org/10.1109/ICAICTA.2017.8090974
  5. Harimurti R, Yamasari Y, Asto B. Predicting student's psychomotor domain on the vocational senior high school using linear regression. In: International Conference on Information and Communications Technology (ICOIACT). IEEE; 2018. p. 448-453. https://doi.org/10.1109/ICOIACT.2018.8350768
  6. Wei D, Xing M, Zhang J, Zhang C, Cao H. Applied research of multiple linear regression in the information quantification of Chinese medicine bone-setting manipulation. In: IEEE International Conference on Bioinformatics and Biomedicine (BIBM). IEEE; 2018. p. 1912-1916. https://doi.org/10.1109/BIBM.2018.8621086
  7. Gopalakrishnan T, Choudhary R, Prasad S. Prediction of sales value in online shopping using linear regression. In: International Conference on Computing Communication and Automation (ICCCA). IEEE; 2018. p. 1-6. https://doi.org/10.1109/CCAA.2018.8777620
  8. Vats V, Kumar A, Kumar N. Efficient crop yield prediction using machine learning algorithms. Int Res J Eng Technol. 2018;5(6):1-16.
  9. Lim HI. A linear regression approach to modelling software characteristics for classifying similar software. In: IEEE 43rd Annual Computer Software and Applications Conference (COMPSAC). IEEE; 2019. p. 942-943. https://doi.org/10.1109/COMPSAC.2019.00152
  10. Sakthivel V, Kumaravel K, Suresh S, Prakash P, Suresh Kumar B. Crop yield optimization and prediction by machine learning algorithms. Int J Sci Res Eng Manag. 2022;6(6).
  11. Rawat L, Karnatak AK, Bisht TS, Kukreti A. Minor millets: profile and ethnobotanical scenario. Millets and millet technology. 2021:51-80. https://doi.org/10.1007/978-981-16-0676-2_3
  12. Shah P, Joshi R, Tripathy NP, Mehta N. What are the drivers of millet-based food consumption in India? A Theory of Consumption Values (TCV) perspective. Journal of International Food & Agribusiness Marketing. 2024:31:1-24. https://doi.org/10.1080/08974438.2024.2398245
  13. Mehmood S, Iraqui S, Mohan M, Ahlawat YK, Sharma N, Sharma AJ, et al. Millet products in the market: Preparation and commercialization. 2024.
  14. Narasimma VP, Muthusamy K, Natarajan B, Vasudevan B, Appavu S, Marimuthu R. Evaluation of machine learning models in comparison to forecast yield for small millets. FEB-Fresenius Environmental Bulletin. 2024:1558.
  15. Kuradusenge M, Hitimana E, Hanyurwimfura D, Rukundo P, Mtonga K, Mukasine A, et al. Crop yield prediction using machine learning models: case of Irish potato and maize. Agriculture. 2023:13:225. https://doi.org/10.3390/agriculture13010225
  16. Venkatesa Palanichamy N, Kalpana M, Suresh A, Balamurugan V. Enhancing small millets productivity prediction through linear regression model using machine learning techniques. sensitizing the millet farming, consumption and nutritional security. 2024:62-66.
  17. Palanichamy V, Muthusamy K, Natarajan B, Vasudevan B, Appavu S, Mahalingam SK. A comparison study of machine learning models to predict the bajra (pearl millet) yield prediction in Tamil Nadu. FEB-Fresenius Environmental Bulletin. 2024:1598.
  18. Hu T, Zhang X, Khanal S, Wilson R, Leng G, Toman EM, et al. Climate change impacts on crop yields: A review of empirical findings, statistical crop models, and machine learning methods. Environmental Modelling & Software. 2024. 19:106119. https://doi.org/10.1016/j.envsoft.2024.106119
  19. Pant J, Pant RP, Singh MK, Singh DP, Pant H. Analysis of agricultural crop yield prediction using statistical techniques of machine learning. Materials Today: Proceedings. 2021;46:10922-6. https://doi.org/10.1016/j.matpr.2021.01.948
  20. Ansarifar J, Wang L, Archontoulis SV. An interaction regression model for crop yield prediction. Scientific reports. 2021;11(1):17754. https://doi.org/10.1038/s41598-021-97221-7
  21. Leng G, Hall JW. Predicting spatial and temporal variability in crop yields: an inter-comparison of machine learning, regression and process-based models. Environmental research letters. 2020. 20;15(4):044027. https://doi.org/10.1088/1748-9326/ab7b24

Downloads

Download data is not yet available.