Machine Learning for Sustainable Agriculture and Precision Farming: A Systematic Review of Predictive Models, Resource Optimization, and Implementation Barriers

Abstract

The imperative to increase global food production by fifty percent before 2050, coupled with the urgent need to reduce agriculture’s environmental footprint, has positioned machine learning as a transformative technology for sustainable farming systems. This paper presents a systematic review of machine learning applications in sustainable agriculture and precision farming, synthesizing peer-reviewed literature published between 2018 and 2026 across computer science, agronomy, and environmental science disciplines. Through analysis of 124 relevant studies identified via the PRISMA framework, we examine three primary domains of ML application: crop yield prediction, resource optimization (irrigation and nutrient management), and crop health monitoring. The findings reveal that ensemble methods, particularly random forest and gradient boosting architectures, consistently outperform single-model approaches for yield prediction, achieving R² values between 0.82 and 0.92 in validation studies. However, the literature demonstrates pronounced heterogeneity in data collection protocols, feature selection methods, and performance reporting standards, which substantially limits cross-study comparability and generalizability. Furthermore, implementation research reveals significant barriers related to data infrastructure, model interpretability, and smallholder farmer accessibility that remain underexplored. The paper advances a conceptual framework linking ML technical capabilities to sustainability outcomes and identifies critical research priorities for translating algorithmic advances into field-level impact.