Show simple item record

dc.contributor.editorChang, Jiyul
dc.contributor.editorFuentes, Sigfredo
dc.date.accessioned2023-04-05T12:49:46Z
dc.date.available2023-04-05T12:49:46Z
dc.date.issued2023
dc.identifierONIX_20230405_9783036566146_37
dc.identifier.urihttps://directory.doabooks.org/handle/20.500.12854/98758
dc.description.abstractWhen adopting remote sensing techniques in precision agriculture, there are two main areas to consider: data acquisition and data analysis methodologies. Imagery and remote sensor data collected using different platforms provide a variety of information volumes and formats. For example, recent research in precision agriculture has used multispectral images from different platforms, such as satellites, airborne, and, most recently, drones. These images have been used for various analyses, from the detection of pests and diseases, growth, and water status of crops to yield estimations. However, accurately detecting specific biotic or abiotic stresses requires a narrow range of spectral information to be analyzed for each application. In data analysis, the volume and complexity of data formats obtained using the latest technologies in remote sensing (e.g., a cube of data for hyperspectral imagery) demands complex data processing systems and data analysis using multiple inputs to estimate specific categorical or numerical targets. New and emerging methodologies within artificial intelligence, such as machine learning and deep learning, have enabled us to deal with these increasing data volumes and the analysis complexity.
dc.languageEnglish
dc.subject.classificationthema EDItEUR::T Technology, Engineering, Agriculture, Industrial processes::TB Technology: general issuesen_US
dc.subject.classificationthema EDItEUR::T Technology, Engineering, Agriculture, Industrial processes::TB Technology: general issues::TBX History of engineering and technologyen_US
dc.subject.othervineyard
dc.subject.otherpesticide application
dc.subject.othervariable rate application
dc.subject.otherunmanned aerial vehicle
dc.subject.othersatellite
dc.subject.othernanosatellite
dc.subject.othermonsoon crops
dc.subject.otherleaf area index
dc.subject.otherleaf chlorophyll concentration
dc.subject.othercrop water content
dc.subject.othermultispectral
dc.subject.otherhyperspectral
dc.subject.otherdeep learning
dc.subject.otherforage dry matter yield
dc.subject.otherhigh-throughput phenotyping
dc.subject.otherBrazilian pasture
dc.subject.othernitrogen indicator
dc.subject.othernitrogen nutrition diagnosis
dc.subject.otheroptical sensor
dc.subject.otherspectral index
dc.subject.otherUAV
dc.subject.otherwheat lodging
dc.subject.otherlightweight
dc.subject.otherdigital surface model (DSM)
dc.subject.otherwinter wheat
dc.subject.otherfractional order differential
dc.subject.othercontinuous wavelet transform
dc.subject.otheroptimal subset regression
dc.subject.othersupport vector machine
dc.subject.otherwheat powdery mildew
dc.subject.othermachine learning
dc.subject.otherinformation fusion
dc.subject.otherremote sensing monitoring
dc.subject.otherhyperspectral imaging
dc.subject.otherdimensionality reduction
dc.subject.otherLDA
dc.subject.otherPLS
dc.subject.otherPCA
dc.subject.otherRandomForest
dc.subject.otherReliefF
dc.subject.otherXGB
dc.subject.otherMeloidogyne
dc.subject.otherSolanum tuberosum
dc.subject.othersoil salinity sensitive parameter
dc.subject.otherrandom forest
dc.subject.otheroptimal retrieval model
dc.subject.otherremote sensing
dc.subject.otherhigh throughput phenotyping
dc.subject.otherUAV/drone
dc.subject.otherbiomass estimation
dc.subject.otheroats
dc.subject.otherwheat
dc.subject.otheryield prediction
dc.subject.otherrandom forests
dc.subject.othersatellite imagery
dc.subject.otherNormalized Difference Vegetation Index (NDVI)
dc.subject.othern/a
dc.titleMethodologies Used in Remote Sensing Data Analysis and Remote Sensors for Precision Agriculture
dc.typebook
oapen.identifier.doi10.3390/books978-3-0365-6615-3
oapen.relation.isPublishedBy46cabcaa-dd94-4bfe-87b4-55023c1b36d0
oapen.relation.isbn9783036566146
oapen.relation.isbn9783036566153
oapen.pages226
oapen.place.publicationBasel


Files in this item

FilesSizeFormatView

There are no files associated with this item.

This item appears in the following Collection(s)

Show simple item record

https://creativecommons.org/licenses/by/4.0/
Except where otherwise noted, this item's license is described as https://creativecommons.org/licenses/by/4.0/