Chiranjit Singha – författare

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.Connects core concepts of AI and ML to real-world application practices in agricultureExplores applications in crop scouting and optimizing variable rate crop inputDemonstrates the value in real or near real-time crop monitoring, yield estimation, and predictionHighlights SAR/optical data applications and analysis in agriculture

This book bridges the gap between research, practice, and policy by offering practical insights and evidence-based solutions. It provides a comprehensive guide to addressing the pressing challenges posed by climate change and extensive anthropogenic activities on the health of global delta ecosystems. Through in-depth analysis, the book examines delta ecosystem resiliency, sustainable practices, and strategies for restoring and safeguarding natural resources. The book underscores the critical importance of studying rivers for their resilience, emphasizing the biophysical aspects of riverine landscapes and their interactions with ecosystems. It highlights how urbanization, groundwater extraction, agricultural intensification, channel regulation, and upstream activities like water withdrawals, diversion, and sediment trapping combine with coastal hazards - such as sea level rise, salinity intrusion, and hurricanes - to jeopardize delta social-ecological systems. Modern geospatial technologies play a prominent role, with discussions on data mining, analysis, modeling, assessment, and visualization techniques. The book explores appropriate management strategies and cutting-edge approaches to delta health management, leveraging open-source software, statistical programming, modeling platforms, and artificial intelligence. These strategies aim to address the multifaceted challenges of delta management while contributing to multiple Sustainable Development Goals (SDGs), including SDG2, SDG6, SDG13, SDG14, SDG15, and SDG17. Organized into thematic chapters, the book combines theoretical frameworks, empirical research, and actionable recommendations. It integrates data from diverse delta regions to provide a holistic perspective, enhanced by diagrams, charts, maps, and case studies for improved understanding and practical application. This approach ensures the book serves as both a foundational reference and an inspirational guide for fostering resilient and sustainable delta ecosystems capable of withstanding climate uncertainties and human-induced pressures. The book is a valuable resource for researchers, policymakers, administrators, educators, local communities, farmers, and practitioners. It offers innovative practices and adaptive strategies to promote sustainable delta ecosystem resilience in the face of climate challenges and extensive anthropogenic activities.