Titlebook: Smart Big Data in Digital Agriculture Applications; Acquisition, Advance Haoyu Niu,YangQuan Chen Book 2024 The Editor(s) (if applicable) an

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Venules

A Low-Cost Soil Moisture Monitoring Method by Using Walabot and Machine Learning Algorithmsisture estimation. The chapter concludes with a summary, underlining the potential of this low-cost method for widespread soil moisture monitoring. References are provided for further exploration of the methodologies and technologies discussed in this chapter.

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Conclusion and Future Researchn in a smart way. Likewise, the authors proposed the smart big data acquisition platforms, such as small UAVs and Edge-AI sensors. Smart big data cleans and transforms the data into useful information that is valuable and relevant to crops and trees growing status.

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BAIT

Why Do Big Data and Machine Learning Entail the Fractional Dynamics?ough an exploration of fractional calculus (FC) and fractional-order thinking (FOT), shedding light on their relevance in understanding the intricate dynamics of complex systems. The chapter delves into the concept of complexity and inverse power laws (IPLs), establishing a connection between heavy-

CYT

Small Unmanned Aerial Vehicles (UAVs) and Remote Sensing Payloadsisticated payloads they carry. The exploration begins with an in-depth examination of the UAV platform, elucidating the key features that make it an invaluable tool for aerial data collection. Within the context of lightweight sensors, the chapter outlines the capabilities of various sensors, includ

MIME

Curmudgeon

The Unmanned Ground Vehicles (UGVs) for Digital Agriculture role as data acquisition platforms. The chapter begins with an introduction, framing the discussion within the broader context of leveraging UGVs to enhance agricultural practices. An in-depth exploration follows, highlighting the UGV’s significance as a versatile and accessible data acquisition to

CODE

Fundamentals of Big Data, Machine Learning, and Computer Vision Workflowbegins with an introduction, setting the stage for a comprehensive tutorial that elucidates the workflow’s fundamentals. The chapter unfolds with a step-by-step tutorial focused on the classification of cotton water stress using Convolutional Neural Networks (CNNs).

后天習(xí)得

A Low-Cost Proximate Sensing Method for Early Detection of Nematodes in Walnut Using Machine Learnine learning algorithms. The chapter commences with an introduction, highlighting the significance of early detection in managing nematode infestations and introducing the approach’s cost-effective nature. The Materials and Methods section outlines the study area, emphasizing the use of reflectance me