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| 001 | 978-981-97-7762-4 | ||
| 003 | DE-He213 | ||
| 005 | 20251030114628.0 | ||
| 007 | cr nn 008mamaa | ||
| 008 | 241219s2024 si | s |||| 0|eng d | ||
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_a10.1007/978-981-97-7762-4 _2doi |
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_aHarnessing Data Science for Sustainable Agriculture and Natural Resource Management _h[electronic resource] / _cedited by Mehul S. Raval, Sanjay Chaudhary, J. Adinarayana, Wei Guo. |
| 250 | _a1st ed. 2024. | ||
| 264 | 1 |
_aSingapore : _bSpringer Nature Singapore : _bImprint: Springer, _c2024. |
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| 300 |
_aXXI, 355 p. 131 illus., 113 illus. in color. _bonline resource. |
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| 336 |
_atext _btxt _2rdacontent |
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| 337 |
_acomputer _bc _2rdamedia |
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| 338 |
_aonline resource _bcr _2rdacarrier |
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| 347 |
_atext file _bPDF _2rda |
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| 490 | 1 |
_aStudies in Big Data, _x2197-6511 ; _v161 |
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| 505 | 0 | _aIntroduction to Data Science in Agriculture and Natural Resource Management -- Defining Problems and Identifying Opportunities in Agriculture and Natural Resources -- Preprocessing of Agricultural and Natural Resource Data -- A Robust big data handling solution for RGB image data set by indoor UAV based phenotyping system -- Mapping Aboveground Biomass and Soil Organic Carbon Density in India- A geospatial-analytic framework for Integrating multi-year remote sensing, large field surveys, and machine learning -- Statistical Modeling in Agriculture: From Foundational Concepts to Modern Applications -- EasyIDP v2.0: An Intermediate Data Processing Package for Photogrammetry-Based Plant Phenotyping -- Deep Learning: A Catalyst for Sustainable Agriculture Transformation -- Deep Learning and Reinforcement Learning Methods for Advancing Sustainable Agricultural and Natural Resource Management -- A Review on AI and Remote Sensing-Based Regenerative Agriculture Assessment -- Model Evaluation and Selection: Ensuring Robust and Accurate Predictions of Crop Yields in Agriculture -- Evaluation of hybrid biodegradable sensor node for monitoring soil moisture -- Multi-modal AI for Ultra-precision Agriculture -- Future Perspectives: Emerging Technologies and Ethical Considerations in Data Science for Agriculture and Natural Resources. | |
| 520 | _aThis book is a comprehensive resource that handles the issues of sustainable agriculture and natural resource management, aligned with the United Nations' Sustainable Development Goals (SDGs). The book is organized into five sections, Understanding the Problem, Data Collection and Cleaning, Exploratory Data Analysis and Visualization, Model Building, and Model Deployment. Each section covers a critical aspect of data science in this context and addresses specific SDGs 2—zero hunger, 6—clean water and sanitation, 12—responsible consumption and production, 13—climate action, and 15—Life on land. The organized sections are arranged to seamlessly follow the data science pipeline and provide practical guidance from problem understanding to its model deployment and stakeholder management. The book is useful for researchers, students, practitioners, and policymakers. | ||
| 650 | 0 |
_aComputational intelligence. _964052 |
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| 650 | 0 |
_aArtificial intelligence _xData processing. |
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| 650 | 0 |
_aSustainability. _957661 |
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| 650 | 1 | 4 |
_aComputational Intelligence. _964052 |
| 650 | 2 | 4 | _aData Science. |
| 650 | 2 | 4 |
_aSustainability. _957661 |
| 700 | 1 |
_aRaval, Mehul S. _eeditor. _0(orcid)0000-0002-3895-1448 _1https://orcid.org/0000-0002-3895-1448 _4edt _4http://id.loc.gov/vocabulary/relators/edt _989213 |
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| 700 | 1 |
_aChaudhary, Sanjay. _eeditor. _0(orcid)0000-0001-9127-360X _1https://orcid.org/0000-0001-9127-360X _4edt _4http://id.loc.gov/vocabulary/relators/edt _989214 |
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| 700 | 1 |
_aAdinarayana, J. _eeditor. _0(orcid)0000-0002-8753-9919 _1https://orcid.org/0000-0002-8753-9919 _4edt _4http://id.loc.gov/vocabulary/relators/edt _989215 |
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| 700 | 1 |
_aGuo, Wei. _eeditor. _0(orcid)0000-0002-3017-5464 _1https://orcid.org/0000-0002-3017-5464 _4edt _4http://id.loc.gov/vocabulary/relators/edt _970447 |
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| 710 | 2 | _aSpringerLink (Online service) | |
| 773 | 0 | _tSpringer Nature eBook | |
| 776 | 0 | 8 |
_iPrinted edition: _z9789819777617 |
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_iPrinted edition: _z9789819777631 |
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_iPrinted edition: _z9789819777648 |
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_aStudies in Big Data, _x2197-6511 ; _v161 _968724 |
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| 856 | 4 | 0 | _uhttps://doi.org/10.1007/978-981-97-7762-4 |
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