Predicting Prices of Cash Crop using Machine Learning
Authors
-
Department of Industrial Engineering and Management, B.M.S College of Engineering, Bengaluru ,IN
Vaidehi Bhaskara -
Department of Industrial Engineering and Management, B.M.S College of Engineering, Bengaluru ,INK. T. Ramesh
-
Department of Operations and IT, ICFAI Business School, Hyderabad ,INSayan Chakraborty
DOI:
https://doi.org/10.18311/jmmf/2023/34497Keywords:
Agriculture, Cash crop, Price prediction, Machine learning, RegressionAbstract
More than half of the Indian population depends on agriculture as a source of livelihood. But, India’s marginal farmers especially, earn meagre amounts from their harvested yields. This may be attributed partly due to relatively smaller land holdings, and partly due to minimal access to resources that aid with informative price forecasts. In order to alleviate the stress caused by the lack of sound financial planning, this research proposes the utilization of machine learning to predict commodity prices. The solution obtained through such a model would assist farmers in predicting the price and associated estimates can be made with respect to yield, sowing patterns and suitable recommendations for sales. The solution developed in this research is a result of a thorough exploration of the literature in this domain, identification of verified secondary sources for data collection, and proposes a methodology to design a machine learning model that predicts prices for seasonal cash crops specific to the markets of Karnataka. Cotton has been used as the crop of focus in this study. ARIMA and Bayesian ridge regression have been used for predictive analytics, and the results obtained indicate a high correlation between the predicted and actual.
Downloads
Metrics
Downloads
Published
How to Cite
Issue
Section
License
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
References
Agriculture in India: Industry Overview, Market Size, Role in Development... IBEF. (n.d.). India Brand Equity Foundation. Retrieved June 18. 2022.
Anubha, Tripathi, K., Kumar, K., & Khandelwal, G. (2021): Onion Price Prediction for the Market of Kayamkulam. Data Analytics and Management, 77– 85.
Basso, B., & Liu, L. (2019): Seasonal crop yield forecast: Methods, applications, and accuracies. Advances in Agronomy, 154, 201–255.
Cenas, P. V. (2017): Forecast of Agricultural Crop Price using Time Series and Kalman Filter Method. Asia Pacific Journal of Multidisciplinary Research, 5(4). 2018.
Chen, Z., Goh, H. S., Sin, K. L., Lim, K., Chung, N. K. H., & Liew, X. Y. (2021): Automated Agriculture Commodity Price Prediction System with Machine Learning Techniques. Advances in Science, Technology and Engineering Systems Journal, 6(4), 376-384.
Ghutake, I., Verma, R., Chaudhari, R., & Amarsinh, V. (2021): An intelligent Crop Price Prediction using suitable Machine Learning Algorithm. ITM Web of Conferences. https://doi.org/10.1051/itmconf/ 20214003040.
Israeli Trade and Economic Mission in India. (2020, May 11): Small and Marginal farmers in India – Difficulties and Solutions. India - Israel Trade & Economic Office, Embassy of Israel. Retrieved June 18, 2022, https://itrade.gov.il/india/2016/08/29/smallandmarginal- farmers-in-india-difficulties-andsolutions/
Jain, A., Marvaniya, S., Godbole, S., & Munigala, V. (2020): A Framework for Crop Price Forecasting in Emerging Economies by Analyzing the Quality of Time-series Data. arXiv. Org. https://doi.org/ 10.48550/arXiv.2009.04171
K., M., Swamy P.S., D., B.R., J., & N.N., N. (2013): Resource Use Efficiency of Bt Cotton and Non-Bt Cotton in Haveri District of Karnataka. International Journal of Agriculture and Food Science Technology, 4(3), 253–258.
Kalichkin, V. K., Alsova, O. K., & Yu Maksimovich, K. (2021): Application of the decision tree method for predicting the yield of spring wheat. IOP Conference Series: Earth and Environmental Science, 839(3). https://doi.org/10.1088/1755-1315/839/ 3/032042
Kanwal, S. (2022, February 14): Agriculture in India - statistics & facts. Statista. Retrieved June 18, 2022, from https://www.statista.com/ topics/4868/ agricultural-sector-inindia/#dossierKeyfigures
Kumar, G. R. (2017, January 24): Focusing on major problems of marginal farmers. The Hans India. Retrieved June 18, 2022, from https:// www.thehansindia.com/posts/index/Hans/2017-01-23/ Focusing-on-majorproblems-of-marginal-farmers/ 275349?infinitescroll=1
Ma, W., Nowocin, K., Marathe, N., & Chen, G. H. (2019): An interpretable produce price forecasting system for small and marginal farmers in India using collaborative filtering and adaptive nearest neighbours. Proceedings of the Tenth International Conference on Information and Communication Technologies and Development, 6, 1–11. https:// doi.org/10.1145/3287098.3287100
Mgale, Y. J., Yan, Y., & Timothy, S. (2021): A Comparative Study of ARIMA and Holt-Winters Exponential Smoothing Models for Rice Price Forecasting in Tanzania. OALib, 08(05), 1–9.
NITI, Integrated Research and Action for Development. (2007, October): Extension of MSP: Fiscal and Welfare Implications. A Study for the Planning Commission. Retrieved June 18, 2022, from https://www.niti.gov.in/planningcommission.gov.in/ docs/reports/sereport/ser/ser_msp.pdf
Sabu, K.M. and Kumar, T.M. (2020): Predictive analytics in Agriculture: Forecasting prices of Arecanuts in Kerala. Procedia Computer Science, 171, 699–708.
Global Hunger Index Scores by 2021 GHI Rank. (n.d.). Global Hunger Index (GHI) – Peer Reviewed Annual Publication Designed to Comprehensively Measure and Track Hunger at the Global, Regional, and Country Levels. Retrieved June 18, 2022
India at a glance FAO in India Food and Agriculture Organization of the United Nations.
Tutorial point (n.d). Scikit Learn - Bayesian Ridge Regression. Scikit Learn. https:// www.tutorialspoint.com/scikit_learn/scikit_learn_ bayesian_ridge_regression.htm
VIT University, Vellore, Tamil Nadu, India. (2020): Crop Value Forecasting using Decision Tree Regressor and Models. European Journal of Molecular & Clinical Medicine, 07(02).
Vohra, A., Pandey, N., & Khatri, S. (2019): Decision Making Support System for Prediction of Prices in Agricultural Commodity. 2019 Amity International Conference on Artificial Intelligence (AICAI). https:/ /doi.org/10.1109/aicai.2019.8701273
What is Marginal Farmer, IGI Global. (n.d.). IGI Global. Retrieved June 18, 2022, from https://www.igiglobal. com/dictionary/marginal-farmer/68592
Scikit learn - Bayesian Ridge regression. Tutorials Point. (n.d.). Retrieved June 30, 2022, https:// www.tutorialspoint.com/scikit_learn/scikit_learn_ bayesian_ridge_regression.htm
Lasso vs Ridge vs elastic net: ML. GeeksforGeeks. (2022, February 11). Retrieved June 30, 2022, from https://www.geeksforgeeks.org/lasso-vs-ridge-vselastic- net-ml/
Wikipedia contributors. (2006, November 22). Kharif crop. Wikipedia. https://en.wikipedia.org/wiki/ Kharif_crop 30, 2022, from https://www. geeksforgeeks.org/lasso-vs-ridge-vs-elastic-net-ml/
Wikipedia contributors. (2006, November 22). Kharif crop. Wikipedia. https://en.wikipedia.org/wiki/ Kharif_crop
