Artificial Intelligence Techniques in Crop Yield Estimation Based on Sentinel-2 Data: A Comprehensive Survey

This review explores the integration of Artificial Intelligence (AI) with Sentinel-2 satellite data in the context of precision agriculture, specifically for crop yield estimation. The rapid advancements in remote sensing technology, particularly through Sentinel-2's high-resolution multispectral imagery, have transformed agricultural monitoring by providing critical data on plant health, soil moisture, and growth patterns. By leveraging Vegetation Indices (VIs) derived from these images, AI algorithms, including Machine Learning (ML) and Deep Learning (DL) models, can now predict crop yields with high accuracy. This paper reviews studies from the past five years that utilize Sentinel-2 and AI techniques to estimate yields for crops like wheat, maize, rice, and others. Various AI approaches are discussed, including Random Forests, Support Vector Machines (SVM), Convolutional Neural Networks (CNNs), and ensemble methods, all contributing to refined yield forecasts. The review identifies a notable gap in the standardization of methodologies, with researchers using different VIs and AI techniques for similar crops, leading to varied results. As such, this study emphasizes the need for comprehensive comparisons and more consistent methodologies in future research. The work underscores the significant role of Sentinel-2 and AI in advancing precision agriculture, offering valuable insights for future studies that aim to enhance sustainability and efficiency in crop management through advanced predictive models.