Artificial intelligence and soil conservation: An overview

Abstract

Soil conservation has evolved from traditional techniques such as contour ploughing, terracing, and crop rotation to the adoption of advanced technologies like remote sensing, GIS, and precision agriculture. Integrating AI marks a transformative phase in soil management, offering data-driven solutions for soil health assessment, monitoring degradation, and predicting fertility. AI-powered platforms utilize ML algorithms and image processing techniques to interpret satellite imagery and data from IoT sensors, enhancing the precision of soil diagnostics. According to the FAO, global soil degradation affects over 33% of land resources, and AI offers significant potential to mitigate such threats. AI-enabled models have achieved up to 92% accuracy in predicting soil organic carbon levels and 85% efficiency in mapping soil moisture patterns. Moreover, AI-driven DSS aid site-specific planning through VRT, adaptive tillage, and irrigation management, improving input use efficiency by 20-25%. These innovations also support policymakers with real-time dashboards and compliance tracking. Despite infrastructural and ethical challenges, the future of AI in soil conservation is promising. High-quality, interdisciplinary research, policy support, and stakeholder collaboration can foster sustainable and resilient soil ecosystems globally.