Predictive Models Based on Artificial Intelligence to Estimate Crop Yield: A Literature Review

Abstract

This article presents a detailed review of methodologies for estimating crop yields in the context of growing global concern for food security and agricultural sustainability, with the main objective of analyzing, synthesizing, and comparing recent studies that apply artificial intelligence to yield prediction, identifying their strengths, limitations, and emerging trends. Approaches that integrate climatic variables, soil conditions, and agricultural management practices are examined. Artificial intelligence techniques, such as machine learning and neural networks, are effective at building robust predictive models. In several reviewed studies, these methods have achieved coefficients of determination (R2) greater than 0.85 and error reductions of 15% to 30% compared to traditional statistical approaches, confirming their high predictive potential. These models consider key elements such as temperature, precipitation, soil fertility, and agronomic decisions related to planting, crop choice, and fertilizer use. The article also discusses the challenges associated with model calibration and selection, given the complexity of agricultural systems and the variability of available data. The review covers studies published between 2016 and 2024, a period in which there has been a notable advance in the application of hybrid and deep learning approaches in the agricultural field. The importance of further research into hybrid approaches that integrate various techniques to improve prediction accuracy is highlighted. Finally, the strategic role of artificial intelligence in agricultural decision-making, in promoting sustainable practices, and in strengthening global food security is underlined.