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Electronics, Close-Range Sensors and Artificial Intelligence in Forestry
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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.
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Keywords
- aboveground biomass
- acceleration
- Accessibility
- accuracy
- aerial robotics
- artificial intelligence
- artificial neural network
- augmented reality
- Automation
- big data
- Biology, Life Sciences
- canopy
- Classification
- climate smart
- close-range sensing
- Comparison
- crowned road surface
- deep learning
- diameter
- digital twinning
- Digitalization
- drone
- Economics, finance, business & management
- effectiveness
- EfficientDet
- EfficientNet
- ensemble learning
- estimation
- events
- fine segmentation
- foliar
- Forest ecology
- forest fire detection
- forest loss
- forest road maintenance
- forest road monitoring
- Forestry & related industries
- forestry 4.0
- forestry detection
- geographically weighted regression
- grading of forest fire
- Industry & industrial studies
- IoT
- Iran
- K-nearest neighbor
- land-cover change
- leaf
- Leaves
- length
- LiDAR
- lightweight Faster R-CNN
- long-term
- Machine learning
- Mathematics & science
- micro-drilling resistance method
- motor-manual felling
- multi-modality
- multiple regression
- n/a
- Performance
- potential
- prediction
- Primary industries
- productivity
- random forest
- random forest model
- Reference, information & interdisciplinary subjects
- region detection of forest fire
- region-refining segmentation
- Remote sensing
- Research & information: general
- resistance sensor
- road scanner
- Romania
- Samples
- Sampling
- sawmilling
- Sentinel-2
- Signal processing
- signal-to-noise ratio (SNR)
- spatial heterogeneity
- terrestrial laser scanning
- TLS
- tree ring
- UAS
- UAV
- ultrasound sensors
- weakly supervised loss
- willow
- Wood
- wood technology
- YOLOv5
Links
DOI: 10.3390/books978-3-0365-6171-4Editions