05664nam a22005295i 4500001001800000003000900018005001700027007001500044008004100059020003700100024003500137050001100172072001600183072002300199072001500222100008000237245020000317250001800517264006900535300004500604336002600649337002600675338003600701347002400737490005100761505145400812520225402266650002404520650002204544650002704566650003104593650002404624650002204648650003204670650003104702700008404733710003404817773002604851776003604877776003604913776003604949830005804985856004605043912001405089912001405103999001705117978-3-031-52645-9DE-He21320251030111450.0cr nn 008mamaa240228s2024    sz |    s    |||| 0|eng d  a97830315264599978-3-031-52645-97 a10.1007/978-3-031-52645-92doi 4aS1-972 7aTVB2bicssc 7aTEC0030002bisacsh 7aTVB2thema1 aNiu, Haoyu.eauthor.4aut4http://id.loc.gov/vocabulary/relators/aut97197410aSmart Big Data in Digital Agriculture Applicationsh[electronic resource] :bAcquisition, Advanced Analytics, and Plant Physiology-informed Artificial Intelligence /cby Haoyu Niu, YangQuan Chen.  a1st ed. 2024. 1aCham :bSpringer Nature Switzerland :bImprint: Springer,c2024.  aXVIII, 239 p. 1 illus.bonline resource.  atextbtxt2rdacontent  acomputerbc2rdamedia  aonline resourcebcr2rdacarrier  atext filebPDF2rda1 aAgriculture Automation and Control,x2731-35060 aPart 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.  aIn 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 on small-unmanned aerial vehicles (UAVs), unmanned ground vehicles (UGVs), edge-AI sensors and the profound impact they have on digital agriculture, particularly in the context of heterogeneous crops, such as walnuts, pomegranates, cotton, etc. For example, lightweight sensors mounted on UAVs, including multispectral and thermal infrared cameras, serve as invaluable tools for capturing high-resolution images. Their enhanced temporal and spatial resolutions, coupled with cost effectiveness and near-real-time data acquisition, position UAVs as an optimal platform for mapping and monitoring crop variability in vast expanses. This combination of data acquisition platforms and advanced analytics generates substantial datasets, necessitating a deep understanding of fractional-order thinking, which is imperative due to the inherent “complexity” and consequent variability within the agricultural process. Much optimism is vested in the field of artificial intelligence, such as machine learning (ML) and computer vision (CV), where the efficient utilization of big data to make it “smart” is of paramount importance in agricultural research. Central to this learning process lies the intricate relationship between plant physiology and optimization methods. The key to the learning process is the plant physiology and optimization method. Crafting an efficient optimization method raises three pivotal questions: 1.) What represents the best approach to optimization? 2.) How can we achieve a more optimal optimization? 3.) Is it possible to demand “more optimal machine learning,” exemplified by deep learning, while minimizing the need for extensive labeled data for digital agriculture? This book details the foundations of the plant physiology-informed machine learning (PPIML) and the principle of tail matching (POTM) framework. It is the 9th title of the "Agriculture Automation and Control" book series published by Springer. 0aAgriculture.968079 0aPlant physiology. 0aQuantitative research. 0aEngineering design.96431614aAgriculture.96807924aPlant Physiology.24aData Analysis and Big Data.24aEngineering Design.9643161 aChen, YangQuan.eauthor.4aut4http://id.loc.gov/vocabulary/relators/aut9719752 aSpringerLink (Online service)0 tSpringer Nature eBook08iPrinted edition:z978303152644208iPrinted edition:z978303152646608iPrinted edition:z9783031526473 0aAgriculture Automation and Control,x2731-350697197640uhttps://doi.org/10.1007/978-3-031-52645-9  aZDB-2-SBL  aZDB-2-SXB  c57960d57960