Applications of Advanced Technologies for Improved Precision in Forest Operations

A special issue of Forests (ISSN 1999-4907). This special issue belongs to the section "Forest Operations and Engineering".

Deadline for manuscript submissions: 31 December 2024 | Viewed by 1979

Special Issue Editors


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Guest Editor
Natural Resources Institute Finland (Luke), Joensuu, Finland
Interests: optimization methods in forestry; wood supply chain management and optimization; sensing technology; AI and automation in forestry
Special Issues, Collections and Topics in MDPI journals

E-Mail Website
Guest Editor
Natural Resources Institute Finland (Luke), Joensuu, Finland
Interests: forestry and harvesting; logistic of wood procurement; discrete-event simulation and drones

E-Mail Website
Guest Editor
Chair of Forest Operations, Faculty of Environment and Natural Resources, University of Freiburg, Freiburg, Germany
Interests: digitalization of forestry; forest 4.0 optimization; integration of new potential and supporting decision making for the entire forest supply chain; evaluation and optimization of technological impact for operations in forests; optimization of processes and processing steps; interaction between humans and their forestry operations

Special Issue Information

Dear Colleagues,

Forest operations have undergone profound transformations with the integration of advanced technologies to achieve superior precision in decision making. Sensing technologies like lidar, stereo cameras, and radar are crucial to this evolution, collectively contributing to a heightened perception of the forest environment. Lidar's laser-based mapping capabilities provide intricate details about the topography and tree structure, while stereo cameras and radar offer depth information, adding layers of intricacy to spatial understanding and further refining the comprehension of the forest's composition. In tandem with these sensing technologies, advanced global navigation satellite systems (GNSS) ensure the precise location tracking of machines, trees, and infrastructure, forming a foundational element of efficient forest management. The synergy extends to robotic systems and autonomous vehicles that leverage these precise measurements to navigate and operate seamlessly within the forest terrain. Advanced location and path algorithms further optimize operations, enabling vehicles to adapt dynamically to changing conditions. Moreover, the integration of tree machine learning (ML) and artificial intelligence (AI) detection algorithms enhances the specificity of data, enabling targeted assessments of individual trees. This integration of precise positioning technology improves the efficiency of harvesting, transport, and road operations and facilitates better planning and management by providing real-time spatial data. In addition, this holistic integration of technologies marks a paradigm shift towards precision, efficiency, sustainability, and harmonious coexistence between technological advancements and delicate forest ecosystems. 

Potential topics include, but are not limited to:

  • Use of sensing technology (e.g., lidar, depth/RGB cameras, radar) for improved forest operations
  • ML and AI algorithms and applications for tree detection and mapping of the forest operations environment
  • Applications of advanced technologies for the location of machines, trees, and infrastructure
  • Planning of operations using remote and short-range sensing technology and optimization algorithms
  • Impact of driver-assisting sensing technologies on productivity and safety
  • Robotic systems and autonomous vehicles guided by sensors

Prof. Dr. Mauricio Acuna
Dr. Kari Väätäinen
Prof. Dr. Thomas Purfürst
Guest Editors

Manuscript Submission Information

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Keywords

  • sensing technologies
  • AI, ML, and optimization algorithms in forest operations
  • location of trees and harvesting equipment
  • forest operations planning using sensing technologies
  • driver-assistance sensing technologies
  • robotic systems and autonomous vehicles

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Published Papers (1 paper)

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Review

22 pages, 913 KiB  
Review
A Comparative Literature Review of Machine Learning and Image Processing Techniques Used for Scaling and Grading of Wood Logs
by Yohann Jacob Sandvik, Cecilia Marie Futsæther, Kristian Hovde Liland and Oliver Tomic
Forests 2024, 15(7), 1243; https://doi.org/10.3390/f15071243 - 17 Jul 2024
Viewed by 1526
Abstract
This literature review assesses the efficacy of image-processing techniques and machine-learning models in computer vision for wood log grading and scaling. Four searches were conducted in four scientific databases, yielding a total of 1288 results, which were narrowed down to 33 relevant studies. [...] Read more.
This literature review assesses the efficacy of image-processing techniques and machine-learning models in computer vision for wood log grading and scaling. Four searches were conducted in four scientific databases, yielding a total of 1288 results, which were narrowed down to 33 relevant studies. The studies were categorized according to their goals, including log end grading, log side grading, individual log scaling, log pile scaling, and log segmentation. The studies were compared based on the input used, choice of model, model performance, and level of autonomy. This review found a preference for images over point cloud representations for logs and an increase in camera use over laser scanners. It identified three primary model types: classical image-processing algorithms, deep learning models, and other machine learning models. However, comparing performance across studies proved challenging due to varying goals and metrics. Deep learning models showed better performance in the log pile scaling and log segmentation goal categories. Cameras were found to have become more popular over time compared to laser scanners, possibly due to stereovision cameras taking over for laser scanners for sampling point cloud datasets. Classical image-processing algorithms were consistently used, deep learning models gained prominence in 2018, and other machine learning models were used in studies published between 2010 and 2018. Full article
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