Description: Machine Learning and AI Technology in Agricultural Applications offers a comprehensive overview of how artificial intelligence and machine learning are transforming the agricultural industry. By delving into image processing and advanced data analysis, the book demonstrates how technology addresses modern agricultural challenges, including climate change, urbanization, and increasing global populations. It emphasizes the importance of integrating sensors and data collection methods to generate vast pools of information, which can be efficiently analyzed through AI-driven solutions. The text lays a strong foundation for understanding the role of technological innovation in supporting sustainable and secure food production.
Beyond introducing core machine learning models such as random forest, support vector machines, logistic regression, and decision trees, the book highlights the centralization of critical agricultural data in the cloud. This resource benefits both students and seasoned agricultural scientists, providing practical insights for optimizing crop yields, monitoring soil and weather conditions, and managing resources like fertilizers and pesticides. The book also explores the rapid analysis of complex datasets, empowering users to make informed, timely decisions in real-world agricultural scenarios.Brief description: Prof. Swain received his Masters and Ph.D. from Asian Institute of Technology(AIT), Thailand. His postdoctoral experience includes Denmark (2 years) and Dalhousie University, Canada (1 year). He has published six books and nearly 100 journal and conference proceedings papers. He have sound computer programming knowledge in C, C++, MatLab, AutoCAD, Arcview, ArcGIS, ENVI, SNAP/GEE. Four students have been awarded Ph.D. under his supervision. His major area of research has been precision agriculture, computer vision, machine learning, flood and drought monitoring etc.