This book aims to address emerging challenges in the field of agriculture and natural resource management using the principles and applications of data science (DS). The book is organized in three sections, and it has fourteen chapters dealing with specialized areas. The chapters are written by experts sharing their experiences very lucidly through case studies, suitable illustrations and tables. The contents have been designed to fulfil the needs of geospatial, data science, agricultural, natural resources and environmental sciences of traditional universities, agricultural universities, technological universities, research institutes and academic colleges worldwide. It will help the planners, policymakers and extension scientists in planning and sustainable management of agriculture and natural resources. The authors believe that with its uniqueness the book is one of the important efforts in the contemporary cyber-physical systems.

Part I: Introduction; Part II: Valuation of ecosystem services and biophysical indicators of NRM impacts; Part III: Methodological advances for a comprehensive impact assessment; Part IV: NRM impact assessment in practice.

Indigenous Knowledge (IK) reviews cutting-edge research and links theory with practice to further our understanding of this important approach's contribution to natural resource management. It addresses IK's potential in solving issues such as coping with change, ensuring global food supply for a growing population, reversing environmental degradation and promoting sustainable practices. It is increasingly recognised that IK, which has featured centrally in resource management for millennia, should play a significant part in today's programmes that seek to increase land productivity and food security while ensuring environmental conservation. An invaluable resource for researchers and postgraduate students in environmental science and natural resources management, this book is also an informative read for development practitioners and undergraduates in agriculture, forestry, geography, anthropology and environmental studies.

This book focuses on the latest developments in the fields of visual AI, image processing and computer vision. It shows research in basic techniques like image pre-processing, feature extraction, and enhancement, along with applications in biometrics, healthcare, neuroscience and forensics. The book highlights algorithms, processes, novel architectures and results underlying machine intelligence with detailed execution flow of models.

If you’re an experienced programmer interested in crunching data, this book will get you started with machine learning—a toolkit of algorithms that enables computers to train themselves to automate useful tasks. Authors Drew Conway and John Myles White help you understand machine learning and statistics tools through a series of hands-on case studies, instead of a traditional math-heavy presentation. Each chapter focuses on a specific problem in machine learning, such as classification, prediction, optimization, and recommendation. Using the R programming language, you’ll learn how to analyze sample datasets and write simple machine learning algorithms. Machine Learning for Hackers is ideal for programmers from any background, including business, government, and academic research. Develop a naïve Bayesian classifier to determine if an email is spam, based only on its text Use linear regression to predict the number of page views for the top 1,000 websites Learn optimization techniques by attempting to break a simple letter cipher Compare and contrast U.S. Senators statistically, based on their voting records Build a “whom to follow” recommendation system from Twitter data

Beyond the Biophysical provides a broad overview of agriculture and natural resource management (NRM) scholarship and practice that lies beyond the biophysical, emphasizing instead epistemological, cultural, and political foundations of NRM. The volume is oriented toward professionals with expertise in agriculture and natural resource management scholarship and practice, but who lack exposure to the conceptual and methodological underpinnings of critical theory, the anthropology of development, ecological anthropology, and other relevant scholarship. It therefore follows common standards of academic rigour, but minimizes the use of jargon, integrates detailed case studies with conceptual syntheses, and attempts to move from critique to concrete recommendations for scholarship and practice. The volume seeks to foster a more nuanced and responsible engagement with local communities and the natural world among NRM scholars and practitioners.

This book presents the latest findings in the areas of digital ecosystem for innovation in agriculture. The book is organized into two sections with thirteen chapters dealing with specialized areas. It provides the reader with an overview of the frameworks and technologies involved in the digitalization of agriculture, as well as the data processing methods, decision-making processes, and innovative services/applications for enabling digital transformations in agriculture. The chapters are written by experts sharing their experiences in lucid language through case studies, suitable illustrations, and tables. The contents have been designed to fulfill the needs of geospatial, data science, agricultural, and environmental sciences of universities, agricultural universities, technological universities, research institutes, and academic colleges worldwide. It helps the planners, policymakers, and extension scientists plan and sustainably manage agriculture and natural resources.

This handbook provides a holistic overview of different aspects of energy management in agriculture with an orientation to address the sustainable development goals. It covers possible applications not only from a technical point of view, but also from economic, financial, social, regulatory, and political viewpoints. Agriculture is one of the most imperative sectors that contribute to the economy across different agro-ecologies of the universe with energy inputs in each stage of production, from making and applying chemicals to fueling tractors that lay seeds and harvest crops to electricity for animal housing facilities. The majority of agricultural research has focused on the use of input, production, and productivity, whereas rational energy budgeting and use remain an overlooked and likely underestimated segment, ignored so far while formulating agro-ecosystem framework. Energy management study is a new frontier of agriculture and is challenging duе to complex enterprises, spatial-temporal variability, exposure to pollution, and the predominant effect of the anthropogenic factor on ecology and environment. But it is worth taking the challenge considering the important prerequisite role of energy for sustainable development which has been evidenced from increasing research in recent times. Of recent origin, there are critical, in-depth studies around the globe assessing the capture and flow of energy in the ecosystem, which will help to develop a conceptual framework to incorporate this vital resource in the agriculture management template. This book is a state-of-the-art resource for a broad group of readers including a diversity of stakeholders and professionals in universities, public energy institutions, farmers and farming industry, public health and other relevant institutions, and the broader public as well.