Application and Prospects of Artificial Intelligence (AI)-Based Technologies in Soil Science

Abstract

Artificial intelligence (AI) can assist soil health studies in agriculture by collecting information, monitoring weather patterns, and recommending the use of pesticides and fertilizers. Farmers may increase productivity and profitability at the same time by using farm management software to make smarter decisions at every stage of the crop-cultivation process. AI is used in soil research to build comprehensive soil health maps and forecast changes by combining data from IoT sensors, GIS, and soil samples. By providing data-driven, localised recommendations, AI-driven models can improve digital soil map resolution, predict soil parameters more accurately, and assist precision agriculture decision-making processes. Promising paths for expanding AI in agriculture are provided by developments in 5G technology, edge computing, and sensor miniaturisation. Machine learning (ML) has completely changed soil science over the last 20 years, transforming soil system assessment, modelling, and management. By combining soil metrics, meteorological data, and other environmental elements for accurate and effective fertility estimate, recent developments in machine learning (ML) and artificial intelligence (AI) present intriguing options.