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Electronics, Close-Range Sensors and Artificial Intelligence in Forestry

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Palabras clave – provistas por la editorial

forest fire detection; deep learning; ensemble learning; Yolov5; EfficientDet; EfficientNet; big data; automation; artificial intelligence; multi-modality; acceleration; classification; events; performance; motor-manual felling; willow; Romania; region detection of forest fire; grading of forest fire; weakly supervised loss; fine segmentation; region-refining segmentation; lightweight Faster R-CNN; ultrasound sensors; road scanner; terrestrial laser scanning; TLS; forest road maintenance; forest road monitoring; crowned road surface; digital twinning; climate smart; LiDAR; digitalization; forest loss; land-cover change; machine learning; spatial heterogeneity; random forest model; geographically weighted regression; aboveground biomass; estimation; remote sensing; Sentinel-2; Iran; multiple regression; artificial neural network; k-nearest neighbor; random forest; canopy; drone; leaf; leaves; foliar; samples; sampling; Aerial robotics; UAS; UAV; IoT; forest ecology; accessibility; wood; diameter; length; close-range sensing; Augmented Reality; comparison; accuracy; effectiveness; potential; forestry 4.0; wood technology; sawmilling; productivity; prediction; long-term; tree ring; forestry detection; resistance sensor; micro-drilling resistance method; signal processing; Signal-to-Noise Ratio (SNR); n/a

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Suiza