001446828 000__ 03558cam\a2200589Ii\4500
001446828 001__ 1446828
001446828 003__ OCoLC
001446828 005__ 20230310004019.0
001446828 006__ m\\\\\o\\d\\\\\\\\
001446828 007__ cr\un\nnnunnun
001446828 008__ 220520s2022\\\\si\a\\\\o\\\\\000\0\eng\d
001446828 019__ $$a1319199980$$a1319222674
001446828 020__ $$a9789811920271$$q(electronic bk.)
001446828 020__ $$a9811920273$$q(electronic bk.)
001446828 020__ $$z9789811920264
001446828 020__ $$z9811920265
001446828 0247_ $$a10.1007/978-981-19-2027-1$$2doi
001446828 035__ $$aSP(OCoLC)1319076899
001446828 040__ $$aYDX$$beng$$erda$$epn$$cYDX$$dGW5XE$$dEBLCP$$dOCLCF$$dOCLCQ$$dN$T
001446828 049__ $$aISEA
001446828 050_4 $$aS494.5.A3
001446828 08204 $$a631.3$$223/eng/20220602
001446828 24500 $$aUnmanned aerial systems in precision agriculture :$$btechnological processes and applications /$$cZhao Zhang, Hu Liu, Ce Yang, Yiannis Ampatzidis, Jianfeng Zhou, Yu Jiang, editors.
001446828 264_1 $$aSingapore :$$bSpringer,$$c[2022]
001446828 264_4 $$c©2022
001446828 300__ $$a1 online resource :$$billustrations (chiefly color).
001446828 336__ $$atext$$btxt$$2rdacontent
001446828 337__ $$acomputer$$bc$$2rdamedia
001446828 338__ $$aonline resource$$bcr$$2rdacarrier
001446828 4901_ $$aSmart agriculture ;$$vvolume 2
001446828 5050_ $$aApplications of UAVs and machine learning in agriculture -- Robot Operating System Powered Data Acquisition for Unmanned Aircraft Systems in Digital Agriculture -- Unmanned aerial vehicle (UAV) applications in cotton production -- Time effect after initial wheat lodging on plot lodging ratio detection using UAV imagery and deep learning -- UAV mission height effects on wheat lodging ratio detection -- Wheat-Net: An Automatic Dense Wheat Spike Segmentation Method Based on An Optimized Hybrid Task Cascade Model -- UAV multispectral remote sensing for yellow rust mapping: opportunities and challenges -- Corn Goss's Wilt disease assessment based on UAV imagery.
001446828 506__ $$aAccess limited to authorized users.
001446828 520__ $$aThis book, consisting of 8 chapters, describes the state-of-the-art technological progress and applications of unmanned aerial vehicles (UAVs) in precision agriculture. It focuses on the UAV application in agriculture, such as crop disease detection, mid-season yield estimation, crop nutrient status, and high-throughput phenotyping. Different from individual papers focusing on a specific application, this book provides a holistic view for readers with a wide range of subjects. In addition to researchers in the areas of plant science, plant pathology, breeding, engineering, it is also intended for undergraduates and graduates who are interested in imaging processing, artificial intelligence in agriculture, precision agriculture, agricultural automation, and robotics.
001446828 588__ $$aOnline resource; title from PDF title page (SpringerLink, viewed June 2, 2022).
001446828 650_0 $$aPrecision farming$$xTechnological innovations.
001446828 650_0 $$aDrone aircraft.
001446828 650_0 $$aAeronautics in agriculture.
001446828 655_0 $$aElectronic books.
001446828 7001_ $$aZhang, Zhao,$$eeditor.
001446828 7001_ $$aLiu, Hu,$$eeditor.
001446828 7001_ $$aYang, Ce,$$eeditor.
001446828 7001_ $$aAmpatzidis, Yiannis,$$eeditor.
001446828 7001_ $$aZhou, Jianfeng,$$d1978-$$eeditor.
001446828 7001_ $$aJiang, Yu,$$eeditor.
001446828 77608 $$iPrint version:$$z9811920265$$z9789811920264$$w(OCoLC)1304815416
001446828 830_0 $$aSmart agriculture ;$$vvolume 2.
001446828 852__ $$bebk
001446828 85640 $$3Springer Nature$$uhttps://univsouthin.idm.oclc.org/login?url=https://link.springer.com/10.1007/978-981-19-2027-1$$zOnline Access$$91397441.1
001446828 909CO $$ooai:library.usi.edu:1446828$$pGLOBAL_SET
001446828 980__ $$aBIB
001446828 980__ $$aEBOOK
001446828 982__ $$aEbook
001446828 983__ $$aOnline
001446828 994__ $$a92$$bISE