Author(s) :

Khubaib Ahmed, Zainab Bintay Farooq, Manahal Noor, Muhammad Brahamdag Shabir kashani, Jameel Ahmed, Ayesha Bintay Farooq.

Volume/Issue :

Abstract :

The incorporation of Artificial Intelligence (AI) is revolutionizing soil science by improving our understanding, prediction and management of soil systems. Digital soil mapping (DSM), nutrient management, erosion evaluation, and soil health evaluation are all being advanced by machine learning (ML) techniques, including Random Forests, Support Vector Machines (SVM), and Deep Neural Networks (DNN). AI-driven models that use a variety of datasets, such as remote sensing (RS), topographical, spectral, and meteorological data, are currently assisting traditional methods that were previously time-consuming and limiting. These technologies make it possible to forecast important soil characteristics including pH, conductivity, and organic carbon with great accuracy and precision. when combined with RS and Geographic information system (GIS), AI supports sustainable agriculture by improving real-time monitoring of soil deterioration and water quality. AI is an essential addition to conventional soil science, enabling more accurate and expandable solutions.

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