Forecasting Prices of Agricultural Commodities using Machine Learning for Global Food Security: Towards Sustainable Development Goal 2

Forecasting Prices of Agricultural Commodities using Machine Learning for Global Food Security: Towards Sustainable Development Goal 2
  IJETT-book-cover           
  
© 2023 by IJETT Journal
Volume-71 Issue-12
Year of Publication : 2023
Author : Anket Patil, Dhairya Shah, Abhishek Shah, Radhika Kotecha
DOI : 10.14445/22315381/IJETT-V71I12P226

How to Cite?

Anket Patil, Dhairya Shah, Abhishek Shah, Radhika Kotecha, "Forecasting Prices of Agricultural Commodities using Machine Learning for Global Food Security: Towards Sustainable Development Goal 2," International Journal of Engineering Trends and Technology, vol. 71, no. 12, pp. 277-291, 2023. Crossref, https://doi.org/10.14445/22315381/IJETT-V71I12P226

Abstract
Global food security is vital for promoting human health, upholding social well-being, and ultimately achieving the United Nations’ Sustainable Development Goal (SDG) 2: Zero Hunger. Conversely, it is influenced by a multitude of factors, with the dynamics of agricultural commodity prices playing a significant role. Recognizing the potential of Machine Learning in agricultural applications, this work delves into exploring the price dynamics of key agricultural commodities across various global producers. Through rigorous experimentation and performance comparison, this study analyses suitable Machine Learning methods and proposes a Hybrid SARIMA-LSTM (HySALS) to forecast global prices of agricultural commodities. The effectiveness of the proposed approach is evaluated using historical price data for five important commodities: Wheat, Millet, Sorghum, Maize, and Rice, both on a global average scale and with specific emphasis on developing nations that are either global leaders in the production of these crops or hold a significant production share within their own borders. The training data encompasses the years 2005 to 2017, while testing is conducted for the period from 2018 to 2022, followed by forecasting global prices for these commodities from 2023 to 2030. The insights derived from these forecasts are aimed to assist the decision-making processes of various stakeholders, from farmers to policymakers, thereby contributing to the efforts towards achieving global food security.

Keywords
Sustainable Development Goals, Global food security, Machine learning, Agricultural research, Price dynamics, Price forecasting.

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