Application of Machine Learning in Smart Agriculture is the first book to present a multidisciplinary look at how technology can not only improve agricultural output, but the economic efficiency of that output as well. Through a global lens, the book approaches the subject from a technical perspective, providing important knowledge and insights for effective and efficient implementation and utilization of machine learning. As artificial intelligence techniques are being used to increase yield through optimal planting, fertilizing, irrigation, and harvesting, these are only part of the complex picture which must also take into account the economic investment and its optimized return. The performance of machine learning models improves over time as the various mathematical and statistical models are proven. Presented in three parts, Application of Machine Learning in Smart Agriculture looks at the fundamentals of smart agriculture; the economics of the technology in the agricultural marketplace; and a diverse representation of the tools and techniques currently available, and in development. This book is an important resource for advanced level students and professionals working with artificial intelligence, internet of things, technology and agricultural economics.

Assessing Progress toward Sustainability: Frameworks, Tools, and Case Studies provides practical frameworks for measuring progress toward sustainability in various areas of production, consumption, services and urban development as they relate to environmental impact. A variety of policies/strategies or frameworks are available at national and international levels. This book presents an integrated approach to sustainability progress measurement by considering both the frameworks and methodological developments of various tools, as well as their implementation in assessing the sustainability of processes, products and services through a global perspective. Combining methods and their application, the book covers a variety of topics, including lifecycle assessment, risk assessment, nexus thinking, and connection to SDGs. Organized clearly into three main sections --Frameworks, Tools, and Case Studies--this book can serve as a practical resource for researchers and practitioners alike in environmental science, sustainability, environmental management and environmental engineering.

Advances in Agronomy, Volume 172, the latest release in this leading reference on the topic, contains a variety of updates and highlights new advances in the field, with each chapter written by an international board of authors.

The evolution of deep learning models, combined with with advances in the Internet of Things and sensor technology, has gained more importance for weather forecasting, plant disease detection, underground water detection, soil quality, crop condition monitoring, and many other issues in the field of agriculture. agriculture. Deep Learning for Sustainable Agriculture discusses topics such as the impactful role of deep learning during the analysis of sustainable agriculture data and how deep learning can help farmers make better decisions. It also considers the latest deep learning techniques for effective agriculture data management, as well as the standards established by international organizations in related fields. The book provides advanced students and professionals in agricultural science and engineering, geography, and geospatial technology science with an in-depth explanation of the relationship between agricultural inference and the decision-support amenities offered by an advanced mathematical evolutionary algorithm.

Advanced Organic Waste Management: Sustainable Practices and Approaches provides an integrated holistic approach to the challenges associated with organic waste management, particularly related to sustainability, lifecycle assessment, emerging regulations, and novel approaches for resource and energy recovery. In addition to traditional techniques, such as anaerobic digestion, composting, innovative and emerging techniques of waste recycling like hydrothermal carbonization and vermicomposting are included. The book combines the fundamentals and practices of sustainable organic waste management with successful case studies from developed and developing countries, highlighting practical applications and challenges. Sections cover global organic waste generation, encompassing sources and types, composition and characteristics, focus on technical aspects related to various resource recovery techniques like composting and vermicomposting, cover various waste-to-energy technologies, illustrate various environmental management tools for organic waste, present innovative organic waste management practices and strategies complemented by detailed case studies, introduce the circular bioeconomy approach, and more.