A Data-Driven Review of Remote Sensing-Based Data Fusion in Precision Agriculture from Foundational to Transformer-Based Techniques

TL;DR

This review analyzes recent data fusion and Transformer-based remote sensing techniques in precision agriculture, highlighting advancements, challenges, and strategic frameworks for improved agricultural monitoring and decision-making.

Contribution

It provides a comprehensive, data-driven comparison of traditional and Transformer-based data fusion methods, proposing best practices and a roadmap for future research in precision agriculture.

Findings

Transformers outperform traditional models in accuracy and data integration.

Transformer-based methods effectively model spatiotemporal dependencies.

The study offers a strategic framework for implementing data fusion in agriculture.

Abstract

This review explores recent advancements in data fusion techniques and Transformer-based remote sensing applications in precision agriculture. Using a systematic, data-driven approach, we analyze research trends from 1994 to 2024, identifying key developments in data fusion, remote sensing, and AI-driven agricultural monitoring. While traditional machine learning and deep learning approaches have demonstrated effectiveness in agricultural decision-making, challenges such as limited scalability, suboptimal feature extraction, and reliance on extensive labeled data persist. This study examines the comparative advantages of Transformer-based fusion methods, particularly their ability to model spatiotemporal dependencies and integrate heterogeneous datasets for applications in soil analysis, crop classification, yield prediction, and disease detection. A comparative analysis of multimodal data fusion approaches is conducted, evaluating data types, fusion techniques, and remote sensing platforms. We demonstrate how Transformers outperform conventional models by enhancing prediction accuracy, mitigating feature redundancy, and optimizing large-scale data integration. Furthermore, we propose a structured roadmap for implementing data fusion in agricultural remote sensing, outlining best practices for ground-truth data selection, platform integration, and fusion model design. By addressing key research gaps and providing a strategic framework, this review offers valuable insights for advancing precision agriculture through AI-driven data fusion techniques.