Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Faculty Publications Book Chapter Faculty Publications- Book Chapter Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Creator An entity primarily responsible for making the resource Pandey, Ananya Title A name given to the resource AI-Driven Innovations in Soil Management, Crop Productivity, and Autonomous Agricultural Vehicles Date A point or period of time associated with an event in the lifecycle of the resource 01-01-2025 Source A related resource from which the described resource is derived Global Societal Resilience Through Political, Environmental, and Cultural Upheaval;pp.1-22 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.4018/979-8-3373-5651-8.ch001" target="_blank" rel="noreferrer noopener">https://doi.org/10.4018/979-8-3373-5651-8.ch001</a> <br /><br /><a href="https://www.scopus.com/pages/publications/105026146312?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105026146312?origin=resultslist</a> Coverage The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant Pandey A., Christ University, Bangalore, India Description An account of the resource The incorporation of Artificial Intelligence (AI) into agriculture is escalating significant transformations in essential areas like soil management, crop yield enhancement, and self-operating farming vehicles. This chapter examines the ways in which AI-driven tools-such as machine learning algorithms, remote sensing technologies, robotics, and autonomous systems-enhance decision-making processes, optimize resource utilization, and contribute to environmental sustainability. With AI, it is possible to carry out accurate diagnostics of soil health, predictive irrigation and fertilization, early detection of diseases, and real-time monitoring of crops. Moreover, AI-enabled self-driving vehicles are transforming the world of mechanized agriculture by providing efficient planting, spraying, and harvesting. This chapter offers a detailed examination of these innovations, assesses their practical uses, and deliberates on the consequences for farmers, researchers, and policymakers. It also underscores the ethical, infrastructural, and socio-economic challenges linked to the use of AI in agriculture. 2026, IGI Global Scientific Publishing. All rights reserved. Publisher An entity responsible for making the resource available IGI Global Relation A related resource ISBN: 979-833735653-2; 979-833735651-8; Language A language of the resource English Type The nature or genre of the resource Book chapter Rights Information about rights held in and over the resource Restricted Access; Hardcopy may be available in the library Format The file format, physical medium, or dimensions of the resource online