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 The transformative potential of artificial intelligence in building sustainable agricultural systems: Innovations, challenges, and future directions 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 AI and Ecological Change for Sustainable Development;pp.161-182 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.4018/979-8-3373-0680-3.ch006" target="_blank" rel="noreferrer noopener">https://doi.org/10.4018/979-8-3373-0680-3.ch006</a> <br /><br /><a href="https://www.scopus.com/pages/publications/105010661216?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105010661216?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 Artificial Intelligence is revolutionizing agricultural practices by enhancing efficiency, productivity, and sustainability. Through precision agriculture, AI enables data-driven decision-making, optimizing resource use, crop management, and yield prediction. Innovations such as machine learning algorithms and IoT integration facilitate real-time monitoring of soil, weather patterns, and pest control. These technologies address critical challenges like food security and environmental conservation. However, the adoption of AI in agriculture faces obstacles, including high implementation costs, data privacy, and limited access to technology. Future directions involve advancing AI capabilities to support climate-resilient farming, promoting digital inclusion, and developing policy frameworks to address socioeconomic concerns. By leveraging AI, agriculture can transition toward sustainable practices, ensuring food security while minimizing environmental impact. In light of the above perspective, this chapter highlights AI's potential to transform agriculture amidst ongoing challenges. 2025, IGI Global Scientific Publishing. All rights reserved. Publisher An entity responsible for making the resource available IGI Global Relation A related resource ISBN: 979-833730682-7; 979-833730680-3; 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