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

The transformer-based data fusion techniques in agricultural implementation of PA, also known as smart farming, relies remote sensing (RS), with a particular focus on soil on the ability to collect, process, and analyse spatial and analysis. Utilizing a systematic, data-driven approach, we temporal data to optimize field management practices demonstrate that transformers have significantly (Cisternas et al., 2020; Pyingkodi et al., 2022). Despite its outperformed conventional deep learning and machine enormous potential, the adoption of PA remains below learning methods since 2022, achieving prediction expectations due to factors such as high initial investment performance between 92% and 97%. The review is costs, the complexity of IT, and the need for specialized specifically focused on soil analysis, due to the importance knowledge (Cisternas et al., 2020). of soil condition in optimizing crop productivity and Remote sensing (RS) has seen rapid advancements and ensuring sustainable farming practices. Transformer-based widespread adoption in PA, offering high-resolution data models have shown remarkable capabilities in handling for applications ranging from crop monitoring to irrigation complex multivariate soil data, improving the accuracy of management (Sishodia et al., 2020). Remote sensing has soil moisture prediction, soil element analysis, and other proven to be an effective tool for capturing and monitoring soil-related applications. This systematic review primarily the spectral and temporal properties of the land surface focuses on 1) analysing research trends and patterns in the influenced by human activities at different spatial and literature, both chronologically and technically, and 2) temporal scales (Bégué et al., 2018).