Titlebook: Data Science in Agriculture and Natural Resource Management; G. P. Obi Reddy,Mehul S. Raval,Sanjay Chaudhary Book 2022 The Editor(s) (if a

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Lecture Notes in Computer Sciencecussed ongoing digital augmentation efforts for accelerating agroecological transition through sustainable intensification of rice fallows in India. Systematic characterization of these rice fallows can be sustainably intensified to grow short-duration legume crops, vegetables and oilseeds between t

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https://doi.org/10.1007/978-3-319-09042-9ital Soil Mapping (DSM), ML integrated with spectroscopic soil studies is gaining momentum among the scientific communities. These innovative approaches have the capabilities to solve global issues like desertification, ecological stability, carbon pool management and climate mitigation in a holisti

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Lecture Notes in Computer Sciencehese rasters. Total 361 segments were considered for training, and testing of RF model, of which 130 segments belonged to SAS, and remaining segments represented other features and normal soils. In the study, 70% of samples were used for training and 30% for testing. The accuracy of trained models o

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2197-6503 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..978-981-16-5849-5978-981-16-5847-1Series ISSN 2197-6503 Series E-ISSN 2197-6511

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Data Science in Agriculture and Natural Resource Management

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Data Science—Algorithms and Applications in Earth Observationus spatial data, and integration of these datasets with ground-based observational data significantly enhanced the capabilities in mapping, monitoring, and forecasting various earth system processes. ML algorithms have vast potential in monitoring earth resources to provide timely information for si

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Data Science: Principles and Concepts in Modeling Decision Trees by highlighting their computational efficiency and higher model interpretability in comparison with other earlier mentioned statistical learning techniques. This is followed by the introduction of basic terms and concepts necessary for understanding DT models and their associated statistical learni

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