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AI-Driven Decision-Making and Optimization in Modern Agriculture Sectors
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Author(s): D. Joel Jebadurai (St. Joseph's College of Engineering, Chennai, India), Mary V. V. Sheela (Aarupadai Veedu Institute of Technology, Vinayaka Mission's Research Foundation, India), L. Rajeshkumar (St.Joseph's College of Engineering, India), M. Soundarya (Sathyabama Institute of Science and Technology, India), Rathi Meena (Dr. Umayal Ramanathan College for Women, India), Thirupathi Manickam (Christ University, India), Arul Vethamanikam G. Hudson (Ayya Nadar Janaki Ammal College, India), K. Dheenadhayalan (Mepco Schlenk Engineering College, India)and M. Manikandan (SRM University, India)
Copyright: 2024
Pages: 20
Source title:
Using Traditional Design Methods to Enhance AI-Driven Decision Making
Source Author(s)/Editor(s): Tien V. T. Nguyen (Industrial University of Ho Chi Minh City, Vietnam)and Nhut T. M. Vo (National Kaohsiung University of Science and Technology, Taiwan)
DOI: 10.4018/979-8-3693-0639-0.ch012
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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.
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