LEADER 04513nam 22006855 450 001 9910726288303321 005 20251008163609.0 010 $a9789819905775$b(electronic bk.) 010 $z9789819905768 024 7 $a10.1007/978-981-99-0577-5 035 $a(MiAaPQ)EBC30546735 035 $a(Au-PeEL)EBL30546735 035 $a(OCoLC)1380359218 035 $a(DE-He213)978-981-99-0577-5 035 $a(BIP)087732576 035 $a(PPN)270615156 035 $a(CKB)26727078400041 035 $a(EXLCZ)9926727078400041 100 $a20230519d2023 u| 0 101 0 $aeng 135 $aurcnu|||||||| 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aDigital Ecosystem for Innovation in Agriculture /$fedited by Sanjay Chaudhary, Chandrashekhar M. Biradar, Srikrishnan Divakaran, Mehul S. Raval 205 $a1st ed. 2023. 210 1$aSingapore :$cSpringer Nature Singapore :$cImprint: Springer,$d2023. 215 $a1 online resource (280 pages) 225 1 $aStudies in Big Data,$x2197-6511 ;$v121 311 08$aPrint version: Chaudhary, Sanjay Digital Ecosystem for Innovation in Agriculture Singapore : Springer,c2023 9789819905768 327 $aA Brief Review of Tools to Promote Transdisciplinary Collaboration for Addressing Climate Change Challenges in Agriculture by Model Coupling -- Machine Learning and Deep Learning in Agriculture ? A review -- Need of orchestration platform to unlock the potential of remote sensing data -- An Algorithmic Framework for fusing images from satellites, Unmanned Aerial Vehicles (UAV), and Farm Internet of Things (IoT) Sensors -- Globally Scalable and Locally Adaptable Satellite Solutions for Agriculture -- A Theoretical Framework of Agricultural Knowledge Management Process in the Indian Agriculture Context -- Simple and innovative methods to estimate gross primary production and transpiration of crops: a review -- Role of Virtual Plants in Digital Agriculture -- Remote sensing for mango and rubber mapping and characterisation for carbon stock estimation? Case study of Malihabad tahsil (UP) and West Tripura District, India -- Impact of Vegetation Indices on Wheat Yield Prediction using Spatio-TemporalModeling -- Farm-wise estimation of crop water requirement of major crops using deep learning architecture -- Hyperspectral Remote Sensing for Agriculture Land Use and Land Cover Classification -- Computer Vision Approaches for Plant Phenotypic Parameter Determination. 330 $aThis book presents the latest findings in the areas of digital ecosystem for innovation in agriculture. The book is organized into two sections with thirteen chapters dealing with specialized areas. It provides the reader with an overview of the frameworks and technologies involved in the digitalization of agriculture, as well as the data processing methods, decision-making processes, and innovative services/applications for enabling digital transformations in agriculture. The chapters are written by experts sharing their experiences in lucid language through case studies, suitable illustrations, and tables. The contents have been designed to fulfill the needs of geospatial, data science, agricultural, and environmental sciences of universities, agricultural universities, technological universities, research institutes, and academic colleges worldwide. It helps the planners, policymakers, and extension scientists plan and sustainably manage agriculture and natural resources. 410 0$aStudies in Big Data,$x2197-6511 ;$v121 606 $aComputational intelligence 606 $aArtificial intelligence 606 $aAgriculture 606 $aBig data 606 $aComputational Intelligence 606 $aArtificial Intelligence 606 $aAgriculture 606 $aBig Data 615 0$aComputational intelligence. 615 0$aArtificial intelligence. 615 0$aAgriculture. 615 0$aBig data. 615 14$aComputational Intelligence. 615 24$aArtificial Intelligence. 615 24$aAgriculture. 615 24$aBig Data. 676 $a006.3 700 $aChaudhary$b Sanjay$01359386 701 $aBiradar$b Chandrashekhar M$01359387 701 $aDivakaran$b Srikrishnan$01359388 701 $aRaval$b Mehul S$01359389 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 912 $a9910726288303321 996 $aDigital Ecosystem for Innovation in Agriculture$93373806 997 $aUNINA