Electronics, Close-Range Sensors and Artificial Intelligence in Forestry
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, de...
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| Year of Publication: | 2022 |
| Language: | English |
| Physical Description: | 1 electronic resource (248 p.) |
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Borz, Stelian Alexandru edt Electronics, Close-Range Sensors and Artificial Intelligence in Forestry Basel MDPI - Multidisciplinary Digital Publishing Institute 2022 1 electronic resource (248 p.) text txt rdacontent computer c rdamedia online resource cr rdacarrier 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. English Research & information: general bicssc Biology, life sciences bicssc Forestry & related industries bicssc 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) 3-0365-6172-2 Proto, Andrea R. edt Keefe, Robert edt Nita, Mihai edt Borz, Stelian Alexandru oth Proto, Andrea R. oth Keefe, Robert oth Nita, Mihai oth |
| language |
English |
| format |
eBook |
| author2 |
Proto, Andrea R. Keefe, Robert Nita, Mihai Borz, Stelian Alexandru Proto, Andrea R. Keefe, Robert Nita, Mihai |
| author_facet |
Proto, Andrea R. Keefe, Robert Nita, Mihai Borz, Stelian Alexandru Proto, Andrea R. Keefe, Robert Nita, Mihai |
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s a b sa sab a r p ar arp r k rk m n mn |
| author2_role |
HerausgeberIn HerausgeberIn HerausgeberIn Sonstige Sonstige Sonstige Sonstige |
| title |
Electronics, Close-Range Sensors and Artificial Intelligence in Forestry |
| spellingShingle |
Electronics, Close-Range Sensors and Artificial Intelligence in Forestry |
| title_full |
Electronics, Close-Range Sensors and Artificial Intelligence in Forestry |
| title_fullStr |
Electronics, Close-Range Sensors and Artificial Intelligence in Forestry |
| title_full_unstemmed |
Electronics, Close-Range Sensors and Artificial Intelligence in Forestry |
| title_auth |
Electronics, Close-Range Sensors and Artificial Intelligence in Forestry |
| title_new |
Electronics, Close-Range Sensors and Artificial Intelligence in Forestry |
| title_sort |
electronics, close-range sensors and artificial intelligence in forestry |
| publisher |
MDPI - Multidisciplinary Digital Publishing Institute |
| publishDate |
2022 |
| physical |
1 electronic resource (248 p.) |
| isbn |
3-0365-6171-4 3-0365-6172-2 |
| illustrated |
Not Illustrated |
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Electronics, Close-Range Sensors and Artificial Intelligence in Forestry |
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