Advancements in maize yield estimation : a comprehensive review of methods and models

Abstract

Accurate and timely estimation of maize yield is crucial for ensuring food security, optimizing resource utilization, and informing agricultural policy. However, current yield estimation methods often encounter significant limitations, such as low spatial resolution, dependence on sparse ground-truth data, poor model generalizability across diverse agroecological zones, and challenges in integrating heterogeneous data sources. Although numerous techniques have been developed, ranging from traditional field-based measurements to advanced remote sensing and machine learning methods, a comprehensive synthesis that critically evaluates these approaches and explores their convergence is still lacking. This review addresses this gap by providing a systematic overview of recent advances in maize yield estimation, with a focus on remote sensing technologies, machine learning algorithms, and hybrid crop modeling frameworks. It examines the strengths and limitations of various methodologies, including UAV- and satellite-based imaging, hyperspectral and LiDAR sensing, regression and ensemble learning, and long-read sequencing. Additionally, the review explores the role of emerging technologies such as IoT, cloud computing, and blockchain in enhancing data collection, processing, and traceability. By identifying key challenges such as environmental variability, data scarcity, and model interpretability, and highlighting opportunities for methodological integration, this review offers a roadmap for future research and development. It argues that the convergence of digital agriculture tools and robust modeling strategies holds significant promise for improving maize yield estimation accuracy, scalability, and applicability. These advancements have far-reaching implications for sustainable agriculture, climate resilience, and global food security.