Kishore Chandra Swain – författare

Feeding a growing global population with a changing climate, shrinking arable land, and increasingly strained water resources is a challenge with no single solution. Machine Learning and AI Technology in Agricultural Applications recasts agriculture as fundamentally a problem of data acquisition, integration, and analysis. Drawing on concrete methods and case studies, the book shows how advanced technologies turn scattered sensor readings, satellite imagery, and field records into decision-making tools that enable more precise, more resilient, and more sustainable farming practices.The chapters cover the full agricultural cycle: crop and weather indicators feeding models that predict yield before harvest; satellites and drones replacing manual field-monitoring surveys across the growing season; and image-based algorithms supporting targeted interventions, from detecting a diseased plant to directing a sprayer to apply treatment only where it is required. The same reliance on remote sensing and predictive modeling carries into aquaculture and water management, where AI and ML are used to estimate groundwater recharge, track fish growth, and monitor water quality. A dedicated set of chapters also examines the economic dimensions of this shift, assessing the viability, market impact, and costs of adopting these innovations, and clarifying where they are most likely to reshape how these sectors operate.Together, these technical perspectives make the book a valuable resource for students building a foundation in this field, as well as for researchers and practitioners looking to apply its findings and insights to their own work.Bridges theory and practice by distilling complex analytical approaches into practical, implementation-ready workflows.Surveys established ML models - such as random forests, support vector machines, and decision trees - alongside newer deep learning architectures, helping readers match the right tool to the task at hand.Demonstrates the value of real- or near-real-time monitoring, from continuous field analytics to automated control systems that respond to conditions as they change.