Precision Soil Quality Analysis Using Transformer-based Data Fusion Strategies: A Systematic Review

• URL: http://arxiv.org/abs/2410.18353v1
• Date: Thu, 24 Oct 2024 01:26:21 GMT
• Title: Precision Soil Quality Analysis Using Transformer-based Data Fusion Strategies: A Systematic Review
• Authors: Mahdi Saki, Rasool Keshavarz, Daniel Franklin, Mehran Abolhasan, Justin Lipman, Negin Shariati,
• Abstract summary: This review explores the most recent advancements in transformer-based data fusion techniques in agricultural remote sensing (RS) We demonstrate that transformers have significantly outperformed conventional deep learning and machine learning methods since 2022. The review is specifically focused on soil analysis, due to the importance of soil condition in optimizing crop productivity and ensuring sustainable farming practices.
• Score: 6.184871136700834
• License:
• Abstract: This review explores the most recent advancements in transformer-based data fusion techniques in agricultural remote sensing (RS), with a particular focus on soil analysis. Utilizing a systematic, data-driven approach, we demonstrate that transformers have significantly outperformed conventional deep learning and machine learning methods since 2022, achieving prediction performance between 92% and 97%. The review is specifically focused on soil analysis, due to the importance of soil condition in optimizing crop productivity and ensuring sustainable farming practices. Transformer-based models have shown remarkable capabilities in handling complex multivariate soil data, improving the accuracy of soil moisture prediction, soil element analysis, and other soil-related applications. This systematic review primarily focuses on 1) analysing research trends and patterns in the literature, both chronologically and technically, and 2) conducting a comparative analysis of data fusion approaches, considering factors such as data types, techniques, and RS applications. Finally, we propose a roadmap for implementing data fusion methods in agricultural RS.

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