Loading...
N. Seetha is an Assistant Professor in the Department of Computer Science and Engineering at Velalar College of Engineering and Technology, Erode, Tamil Nadu, India. She is involved in teaching and academic activities in computer science and engineering. Her contribution focuses on computational intelligence and multimodal AI applications in smart agriculture.
Agriculture is essential for worldwide food security, economic advancement, and ecological sustainability. Crop yields are progressively influenced by phytopathologies, pest invasions, soil deterioration, and suboptimal resource management. Traditional agricultural monitoring techniques frequently rely on regular field assessments and visual inspections, which may fail to detect developing issues promptly for effective action. Intelligent agriculture tackles these issues by incorporating IoT sensors, remote sensing technologies, computer vision, artificial intelligence, and automated farming machinery. This chapter explores the principles of intelligent farming, diverse data collection, evaluation of crop vitality, identification of diseases, monitoring of environmental stressors, optimisation of irrigation, and immediate agricultural decision-making assistance. The suggested paradigm establishes a theoretical basis for creating scalable, data-centric agricultural systems that facilitate early intervention, optimise resource use, enhance crop management, and promote sustainable food production.