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| Link zu diesem Datensatz | https://d-nb.info/1322012261 |
| Titel | Smart Big Data in Digital Agriculture Applications : Acquisition, Advanced Analytics, and Plant Physiology-informed Artificial Intelligence / by Haoyu Niu, YangQuan Chen |
| Person(en) |
Niu, Haoyu (Verfasser) Chen, Yangquan (Verfasser) |
| Organisation(en) | SpringerLink (Online service) (Sonstige) |
| Ausgabe | 1st ed. 2024 |
| Verlag | Cham : Springer Nature Switzerland, Imprint: Springer |
| Zeitliche Einordnung | Erscheinungsdatum: 2024 |
| Umfang/Format | Online-Ressource, XVIII, 239 p. 1 illus. : online resource. |
| Andere Ausgabe(n) |
Printed edition:: ISBN: 978-3-031-52644-2 Printed edition:: ISBN: 978-3-031-52646-6 Printed edition:: ISBN: 978-3-031-52647-3 |
| Inhalt | Part I Why Big Data Is Not Smart Yet? -- 1. Introduction -- 2. Why Do Big Data and Machine Learning Entail the Fractional Dynamics? -- Part II Smart Big Data Acquisition Platforms -- 3. Small Unmanned Aerial Vehicles (UAVs) and Remote Sensing Payloads -- 4. The Edge-AI Sensors and Internet of Living Things (IoLT) -- 5. The Unmanned Ground Vehicles (UGVs) for Digital Agriculture -- Part III Advanced Big Data Analytics, Plant Physiology-informed Machine Learning, and Fractional-order Thinking -- 6. Fundamentals of Big Data, Machine Learning, and Computer VisionWorkflow -- 7. A Low-cost Proximate Sensing Method for Early Detection of Nematodes inWalnut Using Machine Learning Algorithms -- 8. Tree-level Evapotranspiration Estimation of Pomegranate Trees Using Lysimeter and UAV Multispectral Imagery -- 9. Individual Tree-level Water Status Inference Using High-resolution UAV Thermal Imagery and Complexity-informed Machine Learning -- 10. Scale-aware Pomegranate Yield Prediction Using UAV Imagery and Machine Learning -- Part IV Towards Smart Big Data in Digital Agriculture -- 11. Intelligent Bugs Mapping and Wiping (iBMW): An Affordable Robot-Driven Robot for Farmers -- 12. A Non-invasive Stem Water Potential Monitoring Method Using Proximate Sensor and Machine Learning Classification Algorithms -- 13. A Low-cost Soil Moisture Monitoring Method by Using Walabot and Machine Learning Algorithms -- 14. Conclusions and Future Research |
| Persistent Identifier |
URN: urn:nbn:de:101:1-2024022903091791045505 DOI: 10.1007/978-3-031-52645-9 |
| URL | https://doi.org/10.1007/978-3-031-52645-9 |
| ISBN/Einband/Preis | 978-3-031-52645-9 |
| Sprache(n) | Englisch (eng) |
| Beziehungen | Agriculture Automation and Control |
| DDC-Notation | 630.2 (maschinell ermittelte DDC-Kurznotation) |
| Sachgruppe(n) | 630 Landwirtschaft, Veterinärmedizin |
| Online-Zugriff | Archivobjekt öffnen |
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