Advances in Machine Learning for Crop Yield Prediction: A Comprehensive Review of Techniques, Trends, and Challenges

Addressing food security amidst climate change and population growth requires precise crop yield prediction models that surpass the limitations of traditional approaches. In this field, machine learning has become a potent instrument, utilizing comprehensive data from soil metrics, climatic variables, and crop characteristics to enhance forecasting accuracy. This review examines the application of ML algorithms such as Artificial Neural Networks, Random Forests, and Gradient Boosting Decision Trees for predicting crop productivity. Out of 200 articles initially reviewed from major databases, 47 were rigorously selected based on relevance and methodological rigor. The findings provide an in-depth analysis of ML’s efficacy in predicting yields across diverse agricultural environments, identifying trends, key challenges, and limitations related to data heterogeneity, regional specificity, and interpretability. This paper aims to guide researchers in advancing ML-driven yield prediction, particularly in integrating IoT technologies and improving model generalizability, hence encouraging precision farming and sustainable agriculture.