LEADER 04287nam 22006975 450 001 9911066099803321 005 20260214121052.0 010 $a981-9543-30-4 024 7 $a10.1007/978-981-95-4330-4 035 $a(CKB)45332266700041 035 $a(MiAaPQ)EBC32549914 035 $a(Au-PeEL)EBL32549914 035 $a(DE-He213)978-981-95-4330-4 035 $a(OCoLC)1573179574 035 $a(EXLCZ)9945332266700041 100 $a20260214d2026 u| 0 101 0 $aeng 135 $aur||||||||||| 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aTechnological Progress on Variable Rate Fertilization /$fedited by Yinyan Shi, Xiaochan Wang, Man Chen, Lei Wang, Xuekai Huang, Zhao Zhang 205 $a1st ed. 2026. 210 1$aSingapore :$cSpringer Nature Singapore :$cImprint: Springer,$d2026. 215 $a1 online resource (278 pages) 225 1 $aSmart Agriculture,$x2731-3484 ;$v17 311 08$a981-9543-29-0 327 $aForeword by Prof. Zhao Zhang -- Preface -- Progress and development on biological information of crop phenotype research applied to real-time variable-rate fertilization -- Precision Variable-Rate Fertilization for Rice-Wheat Cropping Using Outer-Grooved Wheel Mechanism Based on Multi-Layer Perceptron Model -- Design and Modeling of the Centrifugal Variable-Rate Fertilizer Spreader -- Mechanical properties of fertilizer particles and crop straw under discrete element modeling -- Performance Analysis and Optimization of Centrifugal Variable-Rate Fertilizer Spreaders -- Performance Evaluation and Lag Compensation of Actuators in Variable-Rate Fertilizer Spreaders -- Research on Fertilizer Discharge Flow Rate Detection System -- Field Evaluation and Efficiency Analysis of Centrifugal Variable-Rate Fertilization in Precision Agriculture. 330 $aThis book features interdisciplinary of agricultural engineering and precision agriculture, focusing on real-time variable-rate fertilization (VRF) based on crop phenotypic biological information. It systematically presents cutting-edge research on intelligent sensing, inversion modeling, actuator optimization, and practical system validation. Key scientific methods include hybrid neural network models (such as LFA-PSO-MLP), discrete element modeling (DEM), and deep learning-based flow detection (YOLOv5s-seg). These approaches are illustrated with clear diagrams, data tables, and experimental results, linking theoretical insights with practical engineering applications. The book introduces innovative designs like centrifugal VRF spreaders and phenotypic sensing systems, validated through field trials to improve fertilization accuracy, reduce input waste, and enhance sustainability. It offers a comprehensive technology framework integrating ?crop?sensor?decision?actuator? into a closed-loop control system. For researchers and practitioners in smart farming, crop sensing, and equipment development, this book serves as a valuable reference bridging theory and field applications. Target readers include graduate and undergraduate students in agricultural engineering, precision agriculture researchers, intelligent equipment developers, and agricultural extension professionals. 410 0$aSmart Agriculture,$x2731-3484 ;$v17 606 $aAgriculture 606 $aMaterials 606 $aDetectors 606 $aMachine learning 606 $aAutomation 606 $aEngineering$xData processing 606 $aAgriculture 606 $aSensors and biosensors 606 $aMachine Learning 606 $aAutomation 606 $aData Engineering 615 0$aAgriculture. 615 0$aMaterials. 615 0$aDetectors. 615 0$aMachine learning. 615 0$aAutomation. 615 0$aEngineering$xData processing. 615 14$aAgriculture. 615 24$aSensors and biosensors. 615 24$aMachine Learning. 615 24$aAutomation. 615 24$aData Engineering. 676 $a630 700 $aShi$b Yinyan$01780260 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9911066099803321 996 $aTechnological Progress on Variable Rate Fertilization$94547077 997 $aUNINA