This new volume, Soil, Agriculture, and Ecosystem Modeling, explores and demonstrates soil, agriculture, and ecosystem modeling using artificial intelligence technologies for fostering smart sustainable agricultural practices. The volume takes into account the mechanisms of climate change as well as the challenges and hazards related to soil health, providing insight into long-term and sophisticated sustainable agriculture, crop protection and management, soil carbon sequestration, and ecology preservation.
The authors believe that soil and ecosystem modeling is crucial for managing and comprehending ecological processes and should be required for all studies focusing on agriculture systems, environmental management, environmental sciences, and ecology. Offering an array of modeling strategies that include applications of machine learning, deep learning, and other AI methods, the book provides examples of agriculture and ecological modeling problems along with assignments (with answers). Each chapter ends with various tasks, but the greatest ones require students to create their own models based on issues they have posed and are particularly interested in solving.
The book discusses how the use of machine learning strategies for computer vision algorithms is helping to improve productivity in agriculture by fostering the development of more accurate systems. The combination of computer vision and machine learning (ML) aids in the diagnosis of plant diseases and the monitoring of agricultural conditions, and assessment of environmental risks, both of which are important in preventing the loss of yield and quality as well as sustenance for the human population.
The volume covers the fundamental concepts of soil agriculture and ecosystem modeling, "how to go modeling," and an overview of the numerous model types used for ecological modeling. The creation and use of the many model types in agriculture modeling are covered in depth in the book: environmental models, pathological models, agronomic models, and structurally dynamic models.
This volume will be valuable to both established and budding agricultural researchers, soil scientists, and environmental scientists, as well as for students and faculty.
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