Applications of Machine Learning in Agrometeorological Forecasting and Modeling: A Short Review from the Journal of Agrometeorology

Agrometeorology, the science of applying meteorological data to enhance agricultural productivity, stands at the critical intersection of climate science, agriculture, and global food security.In an era of increasing climate variability, the ability to accurately forecast weather patterns and model their impact on agricultural systems is more crucial than ever.Recently, the field has witnessed a transformative shift with the integration of machine learning (ML), a powerful set of computational tools capable of identifying complex patterns and making predictions from vast datasets.This influx of ML applications promises to deliver more precise, timely, and actionable insights for farmers, policymakers, and researchers.The purpose of this literature review is to synthesize and analyze recent research on the application of machine learning in agrometeorology.Drawing exclusively from studies published in the Journal of Agrometeorology during 2025, this short review identifies the core domains where ML is being applied, evaluates the diversity of modeling techniques being deployed, and highlights emerging methodological trends.This document is structured to provide a clear and comprehensive overview of the current landscape.I will begin by examining core applications in on-farm crop production and management, including yield forecasting and pest forewarning.The review will then explore the use of ML for modeling fundamental environmental parameters such as precipitation and evaporation.Following this, we will discuss advanced applications in hazard prediction before synthesizing the key methodological trends observed across the literature.Finally, we will conclude with a summary of findings and propose potential directions for future research.This exploration begins with the most direct application of ML in agriculture: improving on-farm decision-making for crop management. Core Applications in Crop Production and ManagementPredictive analytics are of paramount strategic importance for modern on-farm decision-making, enabling producers to move from reactive responses to proactive planning.Machine learning models that provide accurate forecasts for crop yield and timely warnings for pest outbreaks directly impact agricultural productivity by optimizing resource allocation, reducing input costs, and mitigating potential losses.Recent research highlights a significant focus on deploying ML to solve these fundamental agricultural challenges. Crop Yield Forecasting and Prediction:A primary objective in applied agrometeorology is the accurate forecasting of crop yields.Several recent studies demonstrate the application of machine learning to predict yields for a variety of staple crops.Research by Jhajharia (2025) focuses on wheat, while studies by Patel & Bunkar (2025) and Singh et al., (2025) address soybean and sorghum, respectively.Similarly, Chand & Ranjan (2025) have applied these techniques to sugarcane, and Rao & Krishnan (2025) have focused on rice.The common goal across these efforts is to leverage computational models to anticipate production outcomes, providing valuable information for regional food security assessments and market planning.A significant aspect of this research is the reliance on diverse data sources to train the predictive models.The work by Jhajharia (2025) explicitly notes the use of both climatic and satellite data as inputs, underscoring the power of integrating multiple data streams for robust forecasting.Furthermore, there is a clear trend toward methodological sophistication.While some studies refer generally to "machine learning techniques," others, such as those by Patel & Bunkar (2025) and Rao & Krishnan (2025),