Electronics, Close-Range Sensors and Artificial Intelligence in Forestry

• Other Authors: Borz, Stelian Alexandru (Editor), Proto, Andrea R. (Editor), Keefe, Robert (Editor), Nita, Mihai (Editor)
• Format: Electronic Book Chapter
• Language: English
• Published: Basel MDPI - Multidisciplinary Digital Publishing Institute 2022
• Subjects: Research & information: general; Biology, life sciences; Forestry & related industries; 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
• Online Access: DOAB: download the publication; DOAB: description of the publication

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.