Obálka

Smart Big Data in Digital Agriculture Applications Acquisition, Advanced Analytics, and Plant Physiology-informed Artificial Intelligence /

In the dynamic realm of digital agriculture, the integration of big data acquisition platforms has sparked both curiosity and enthusiasm among researchers and agricultural practitioners. This book embarks on a journey to explore the intersection of artificial intelligence and agriculture, focusing o...

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Hlavní autoři: Niu, Haoyu (Autor), Chen, YangQuan (Autor)
Korporativní autor: SpringerLink (Online service)
Médium: Elektronický zdroj E-kniha
Jazyk:angličtina
Vydáno: Cham : Springer Nature Switzerland : Imprint: Springer, 2024.
Vydání:1st ed. 2024.
Edice:Agriculture Automation and Control,
Témata:
Agriculture.
Plant physiology.
Quantitative research.
Engineering design.
Plant Physiology.
Data Analysis and Big Data.
Engineering Design.
On-line přístup:https://doi.org/10.1007/978-3-031-52645-9
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Obsah:
  • 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.

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