Soil conservation and information technologies: A literature review

Abstract

The evolution of real-time data technologies has significantly transformed several sectors, including agriculture. Advances in sensors, transducers, and artificial intelligence (AI) have driven automation and optimization in agricultural production processes, enabling detailed analyses for soil conservation. However, intensive land use and climate change represent critical challenges, threatening biodiversity and water resource quality. Image processing and spatial data analysis tools support informed decision-making in precision agriculture. This study conducted a systematic review on the SCOPUS platform, emphasizing AI technologies applied to soil management, coverage, and classification. The optimal combination of search terms, including “Agriculture”, “Deep Learning”, and “Soil”, yielded 909 publications. We selected 190 publications for detailed analysis. The review underscored the importance of remote sensing in developing indexes and predictive models, despite existing limitations in the scale of analysis. The growing application of neural network algorithms to recognize soil and plant structures reflects advancements in Information and Communication Technologies (ICT). Since 2020, there has been a notable increase in AI-driven approaches to soil conservation, highlighting a shift toward sustainable and regenerative management practices.