Show simple item record

dc.contributor.editorBorz, Stelian Alexandru
dc.contributor.editorProto, Andrea R.
dc.contributor.editorKeefe, Robert
dc.contributor.editorNita, Mihai
dc.date.accessioned2023-01-05T12:35:05Z
dc.date.available2023-01-05T12:35:05Z
dc.date.issued2022
dc.identifierONIX_20230105_9783036561721_66
dc.identifier.urihttps://directory.doabooks.org/handle/20.500.12854/95837
dc.description.abstractThe use of electronics, close-range sensing, and artificial intelligence has changed the management paradigm in many contemporary industries in which Big Data analytics by automated processes has become the backbone of decision making and improvement. Acknowledging the integration of electronics, devices, sensors, and intelligent algorithms in much of the equipment used in forest operations, as well as their use in various forestry-related applications, it is apparent that many disciplines within forestry and forest science still rely on data collected traditionally, which is resource-intensive. In turn, this brings limitations in characterizing the specific behaviors of forest product systems and wood supply chains, and often prevents the development of solutions for improvement or inferring the laws behind the operation and management of such systems. Undoubtedly, many solutions still need to be developed in the future to provide the technology required for the effective management of forests. In this regard, the Special Issue entitled “Electronics, Close-Range Sensors and Artificial Intelligence in Forestry” highlights many examples of how technological improvements can be brought to forestry and to other related fields of science and practice.
dc.languageEnglish
dc.subject.classificationthema EDItEUR::G Reference, Information and Interdisciplinary subjects::GP Research and information: generalen_US
dc.subject.classificationthema EDItEUR::P Mathematics and Science::PS Biology, life sciencesen_US
dc.subject.classificationthema EDItEUR::K Economics, Finance, Business and Management::KN Industry and industrial studies::KNA Agribusiness and primary industries::KNAL Forestry industryen_US
dc.subject.otherforest fire detection
dc.subject.otherdeep learning
dc.subject.otherensemble learning
dc.subject.otherYolov5
dc.subject.otherEfficientDet
dc.subject.otherEfficientNet
dc.subject.otherbig data
dc.subject.otherautomation
dc.subject.otherartificial intelligence
dc.subject.othermulti-modality
dc.subject.otheracceleration
dc.subject.otherclassification
dc.subject.otherevents
dc.subject.otherperformance
dc.subject.othermotor-manual felling
dc.subject.otherwillow
dc.subject.otherRomania
dc.subject.otherregion detection of forest fire
dc.subject.othergrading of forest fire
dc.subject.otherweakly supervised loss
dc.subject.otherfine segmentation
dc.subject.otherregion-refining segmentation
dc.subject.otherlightweight Faster R-CNN
dc.subject.otherultrasound sensors
dc.subject.otherroad scanner
dc.subject.otherterrestrial laser scanning
dc.subject.otherTLS
dc.subject.otherforest road maintenance
dc.subject.otherforest road monitoring
dc.subject.othercrowned road surface
dc.subject.otherdigital twinning
dc.subject.otherclimate smart
dc.subject.otherLiDAR
dc.subject.otherdigitalization
dc.subject.otherforest loss
dc.subject.otherland-cover change
dc.subject.othermachine learning
dc.subject.otherspatial heterogeneity
dc.subject.otherrandom forest model
dc.subject.othergeographically weighted regression
dc.subject.otheraboveground biomass
dc.subject.otherestimation
dc.subject.otherremote sensing
dc.subject.otherSentinel-2
dc.subject.otherIran
dc.subject.othermultiple regression
dc.subject.otherartificial neural network
dc.subject.otherk-nearest neighbor
dc.subject.otherrandom forest
dc.subject.othercanopy
dc.subject.otherdrone
dc.subject.otherleaf
dc.subject.otherleaves
dc.subject.otherfoliar
dc.subject.othersamples
dc.subject.othersampling
dc.subject.otherAerial robotics
dc.subject.otherUAS
dc.subject.otherUAV
dc.subject.otherIoT
dc.subject.otherforest ecology
dc.subject.otheraccessibility
dc.subject.otherwood
dc.subject.otherdiameter
dc.subject.otherlength
dc.subject.otherclose-range sensing
dc.subject.otherAugmented Reality
dc.subject.othercomparison
dc.subject.otheraccuracy
dc.subject.othereffectiveness
dc.subject.otherpotential
dc.subject.otherforestry 4.0
dc.subject.otherwood technology
dc.subject.othersawmilling
dc.subject.otherproductivity
dc.subject.otherprediction
dc.subject.otherlong-term
dc.subject.othertree ring
dc.subject.otherforestry detection
dc.subject.otherresistance sensor
dc.subject.othermicro-drilling resistance method
dc.subject.othersignal processing
dc.subject.otherSignal-to-Noise Ratio (SNR)
dc.subject.othern/a
dc.titleElectronics, Close-Range Sensors and Artificial Intelligence in Forestry
dc.typebook
oapen.identifier.doi10.3390/books978-3-0365-6171-4
oapen.relation.isPublishedBy46cabcaa-dd94-4bfe-87b4-55023c1b36d0
oapen.relation.isbn9783036561721
oapen.relation.isbn9783036561714
oapen.pages248
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/