05350nam 2201441z- 450 991063998500332120231214133043.03-0365-6171-4(CKB)5470000001633504(oapen)https://directory.doabooks.org/handle/20.500.12854/95837(EXLCZ)99547000000163350420202301d2022 |y 0engurmn|---annantxtrdacontentcrdamediacrrdacarrierElectronics, Close-Range Sensors and Artificial Intelligence in ForestryBaselMDPI - Multidisciplinary Digital Publishing Institute20221 electronic resource (248 p.)3-0365-6172-2 The 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.Research & information: generalbicsscBiology, life sciencesbicsscForestry & related industriesbicsscforest fire detectiondeep learningensemble learningYolov5EfficientDetEfficientNetbig dataautomationartificial intelligencemulti-modalityaccelerationclassificationeventsperformancemotor-manual fellingwillowRomaniaregion detection of forest firegrading of forest fireweakly supervised lossfine segmentationregion-refining segmentationlightweight Faster R-CNNultrasound sensorsroad scannerterrestrial laser scanningTLSforest road maintenanceforest road monitoringcrowned road surfacedigital twinningclimate smartLiDARdigitalizationforest lossland-cover changemachine learningspatial heterogeneityrandom forest modelgeographically weighted regressionaboveground biomassestimationremote sensingSentinel-2Iranmultiple regressionartificial neural networkk-nearest neighborrandom forestcanopydroneleafleavesfoliarsamplessamplingAerial roboticsUASUAVIoTforest ecologyaccessibilitywooddiameterlengthclose-range sensingAugmented Realitycomparisonaccuracyeffectivenesspotentialforestry 4.0wood technologysawmillingproductivitypredictionlong-termtree ringforestry detectionresistance sensormicro-drilling resistance methodsignal processingSignal-to-Noise Ratio (SNR)Research & information: generalBiology, life sciencesForestry & related industriesBorz Stelian Alexandruedt1279878Proto Andrea RedtKeefe RobertedtNita MihaiedtBorz Stelian AlexandruothProto Andrea RothKeefe RobertothNita MihaiothBOOK9910639985003321Electronics, Close-Range Sensors and Artificial Intelligence in Forestry3016103UNINA