Integrating AI and Remote Sensing for Advanced Soil Mapping

Abstract

The work is the first of its kind about the integration of machine learning algorithms and remote sensing in artificial intelligence (AI) to digital soil mapping which is a breakthrough in soil science. The application of these techniques like random forests, support machines and neural networks and massive amounts of data have made it possible to get the precision of soil property estimates that were inaccessible to conventional methods. In our approach based on remote sensing and a mixture of supervised and unsupervised learning, more accurate and fine-resolution soil maps are obtained that are required in accuracy agriculture and environmental initiatives on the one hand and land-use planning on the other. The results reveal that there is a great enhancement of the accuracy of the maps that can assist in developing useful information regarding sustainable agricultural activities and improve crop productivity and risk evaluation of the circumstance. Nevertheless, these barriers as the absence of data in remote places and high computational expenses provide the prospects of research and enhancement of this model in the future. One of the strongest aspects of the paper is especially the way in which the highly developed AI technologies can be effectively applied to measure the aggregate soil characteristics in spacious areas and under various conditions. The findings are not just academically relevant, but they bring out meaningful issues that need to be explored. Policy makers took part in the process of formulating policies to accommodate sustainable agriculture and landscape description.

Keywords: Artificial Intelligence (AI), Remote Sensing, Digital Soil Mapping, Machine Learning, Precision Agriculture, Geographic Information Systems (GIS).e