AI-Driven Decision-Making and Optimization in Modern Agriculture Sectors

Jebadurai, D. Joel and V. Sheela, Mary V. and Rajeshkumar, L. and Soundarya, M. and Rathi Meena and Manickam, Thirupathi and G. Hudson, Arul Vethamanikam and Dheenadhayalan, K. and Manikandan, M. (2024) AI-Driven Decision-Making and Optimization in Modern Agriculture Sectors. Springer. pp. 269-288. ISSN 2475-6814

Full text not available from this repository.

Abstract

AI-driven decision-making tools have emerged as a novel technology poised to replace traditional agricultural practices. In this chapter, AI's pivotal role in steering the agricultural sector towards sustainability is highlighted, primarily through the utilization of AI techniques such as robotics, deep learning, the internet of things, image processing, and more. This chapter offers insights into the application of AI techniques in various functional areas of agriculture, including weed management, crop management, and soil management. Additionally, it underlines both the challenges and advantages presented by AI-driven applications in agriculture. In conclusion, the potential of AI in agriculture is vast, but it faces various impediments that, when properly identified and addressed, can expand its scope. This chapter serves as a valuable resource for government authorities, policymakers, and scientists seeking to explore the untapped potential of AI's significance in agriculture. © 2024 Elsevier B.V., All rights reserved.

Item Type: Article
Subjects: Computer Science > Artificial Intelligence
Divisions: Medicine > Vinayaka Mission's Kirupananda Variyar Medical College and Hospital, Salem
Depositing User: Unnamed user with email techsupport@mosys.org
Last Modified: 27 Nov 2025 06:35
URI: https://vmuir.mosys.org/id/eprint/1684

Actions (login required)

View Item
View Item