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Cutting-Edge Solutions in Advanced Forestry: Integrating Sensors, AI, IoT, Robotics,...
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Cutting-Edge Solutions in Advanced Forestry: Integrating Sensors, AI, IoT, Robotics, and Connectivity

A special issue of Forests (ISSN 1999-4907). This special issue belongs to the section "Forest Inventory, Modeling and Remote Sensing".

Deadline for manuscript submissions: 30 October 2025 | Viewed by 5222

Special Issue Editors

Dr. Heesung Woo

E-Mail Website
Guest Editor
Department of Forest Engineering, Resources & Management, College of Forestry, Oregon State University, Corvallis, OR 97331-5704, USA
Interests: precision forestry; robotics; sensor application; advanced forestry; AI; machine vision; forest operation; harvesting
Dr. Jay Kim

E-Mail Website
Guest Editor
Environmental and Occupational Health Program, College of Public Health and Human Sciences, Oregon State University, Corvallis, OR 97331, USA
Interests: occupational ergonomics; biomechanics

Special Issue Information

Dear Colleagues,

This Special Issue, titled "Cutting-Edge Solutions in Advanced Forestry: Integrating Sensors, AI, IoT, Robotics, and Connectivity", aims to showcase the latest technological innovations reshaping forestry management and conservation. It will focus on the integration of advanced technologies such as sensors, artificial intelligence, the Internet of Things, robotics, and connectivity solutions, highlighting their applications in enhancing forest sustainability, efficiency, and data-driven management practices.

Submissions of various formats are invited, including original research and review articles, case studies, and brief reports. These contributions should offer new insights into the development and application of these technologies, evaluate their impact on forestry practices, and discuss their potential implications for policy and future technological integration.

Potential topics include, but are not limited to, the following:

  • Precision forestry techniques;
  • AI-driven predictive models;
  • IoT networks for environmental monitoring and forest machine connectivity;
  • Robotic automation in forestry operations and applications in the forestry sector;
  • Advancements in connectivity that improve real-time data transmission and technological coordination in challenging forest environments.

This Special Issue will serve as a comprehensive resource for researchers, practitioners, and policymakers interested in the integration of advanced technologies in forestry, offering perspectives on current capabilities, challenges, and future directions.

Dr. Heesung Woo
Dr. Jay Kim
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Forests is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • robotics application
  • sensor
  • precision forestry
  • AI
  • data acquisition
  • advanced forestry
  • connectivity

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Further information on MDPI's Special Issue policies can be found here.

Published Papers (5 papers)

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Research

18 pages, 8135 KiB  
Article
Global Navigation Satellite System/Inertial Navigation System-Based Autonomous Driving Control System for Forestry Forwarders
by Hyeon-Seung Lee, Gyun-Hyung Kim, Hong-Sik Ju, Ho-Seong Mun, Jae-Heun Oh and Beom-Soo Shin
Forests 2025, 16(4), 647; https://doi.org/10.3390/f16040647 - 8 Apr 2025
Viewed by 517
Abstract
Logging operations comprise a repeated and tedious job in forestry operations because forestry forwarders must keep completing round-trip transportation on forest roads from tree-cutting sites to forest roads where their truck can be accessed. In this study, an autonomous driving system for tracked [...] Read more.
Logging operations comprise a repeated and tedious job in forestry operations because forestry forwarders must keep completing round-trip transportation on forest roads from tree-cutting sites to forest roads where their truck can be accessed. In this study, an autonomous driving system for tracked forwarders was developed using GNSS (Global Navigation Satellite System)/INS (Inertial Navigation System). The mechanical control system of the forwarder was replaced with an electronic control system, and path-planning and -tracking algorithms were implemented. The electronic control system, operated by servo motors to operate the driving levers, exhibited a response that was 150 milliseconds faster in lever control compared to manual operation. To generate an autonomous driving path, a skilled operator drove the forwarder along a forest road, and the recorded path was post-processed using the Novatel Inertial Explorer 8.70 GNSS + INS software to minimize GNSS errors. The autonomous forwarder followed the generated path using the pure pursuit algorithm. Autonomous driving tests conducted along this path achieved a root mean square error (RMSE) within 0.4 m (range: 0.389–0.393). Driving errors were primarily attributed to GNSS positional inaccuracies, especially in environments with dense canopies and landslide prevention structures located higher than the GNSS antenna, obstructing satellite signals. These findings underscore the importance and feasibility of autonomous forwarders in diverse forest environments, providing a critical foundation for advancing autonomous forestry machinery. The proposed technologies are expected to significantly contribute to enhancing the productivity of forestry operations. Full article
(This article belongs to the Special Issue Cutting-Edge Solutions in Advanced Forestry: Integrating Sensors, AI, IoT, Robotics, and Connectivity)
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21 pages, 4658 KiB  
Article
Improvement of YOLO Detection Strategy for Detailed Defects in Bamboo Strips
by Ru-Xiao Yang, Yan-Ru Lee, Fu-Shin Lee, Zhenying Liang, Nanhua Chen and Yang Liu
Forests 2025, 16(4), 595; https://doi.org/10.3390/f16040595 - 28 Mar 2025
Viewed by 324
Abstract
It is difficult to detect several detailed defects when detecting surface defects in bamboo strips. The morphology of these defect characteristics exhibits relatively simple patterns but closely resembles the underlying fiber texture or coloration, as exemplified by cracks, mildew, wormholes, and burr formation. [...] Read more.
It is difficult to detect several detailed defects when detecting surface defects in bamboo strips. The morphology of these defect characteristics exhibits relatively simple patterns but closely resembles the underlying fiber texture or coloration, as exemplified by cracks, mildew, wormholes, and burr formation. In this regard, this study proposes an improved model based on the YOLOv8 deep learning network. The improved model uses dynamic convolution and a Ghost module to improve the C3k2 modules in YOLOv8 to reconstruct its backbone and neck parts, where the research introduces the DySample module to replace the original upsample module to avoid the loss of feature information of targets after the network is used multiple times, further ensuring the detection effect of detailed features, as well as the EMA mechanism in the neck part. Experimental validation of the developed model demonstrated robust detection performance, achieving mAP values of 93.1%, 92.9%, 92.2%, and 92.2% for burr, mildew, cracking, and wormhole detection, with a total mAP of 92.6% and a precision of 81.5%; at the same time, the weight was decreased by 14%. The experimental results show that the improved model in this study has a certain detection effect on difficult-to-identify features on the surface of bamboo strips. This research demonstrates that employing YOLOv8 helps in detecting several challenging minor defects in bamboo strips. Full article
(This article belongs to the Special Issue Cutting-Edge Solutions in Advanced Forestry: Integrating Sensors, AI, IoT, Robotics, and Connectivity)
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16 pages, 6025 KiB  
Article
Assessing Rutting and Soil Compaction Caused by Wood Extraction Using Traditional and Remote Sensing Methods
by Ikhyun Kim, Jaewon Seo, Heesung Woo and Byoungkoo Choi
Forests 2025, 16(1), 86; https://doi.org/10.3390/f16010086 - 7 Jan 2025
Cited by 1 | Viewed by 992
Abstract
Machine traffic during timber harvesting operations induces soil compaction, which is particularly evident in the formation of ruts. Visual inspection of rut formation is labor-intensive and limits the volume of data that can be collected. This study aims to contribute to the limited [...] Read more.
Machine traffic during timber harvesting operations induces soil compaction, which is particularly evident in the formation of ruts. Visual inspection of rut formation is labor-intensive and limits the volume of data that can be collected. This study aims to contribute to the limited knowledge base regarding the extent of soil physical disturbance caused by machine traffic on steep slopes and to evaluate the utility of LiDAR and UAV photogrammetry techniques. The selected traffic trails included single-pass uphill, single-pass downhill, three-pass round trip, and five-pass round trip trails, with an average slope of 70.7%. Traditional methods were employed to measure rut depth using a pin board and to assess soil bulk density (BD) and soil porosity (SP) from soil samples. The results revealed that the average rut depth was 19.3 cm, while the deepest ruts were observed after a single pass (uphill: 20.0 cm; downhill: 22.7 cm), where BD and SP showed the most significant changes. This study provides a rare quantitative evaluation of the applicability of remote sensing methods in forestry by comparing surface height data collected via a pin board with that derived from a Mobile LiDAR System (MLS) and UAV photogrammetry using structure-from-motion (SfM). When compared to pin board measurements, the MLS data showed an R2 value of 0.74 and an RMSE of 4.25 cm, whereas the SfM data had an R2 value of 0.62 and an RMSE of 5.27 cm. For rut depth estimation, SfM (16.0 cm) significantly underestimated values compared to the pin board (19.3 cm) and MLS (19.9 cm). These findings not only highlight the potential and limitations of remote sensing methods for assessing soil disturbance in steep forest environments but also contribute to addressing the knowledge gaps surrounding the effects of soil compaction in steep terrain. Full article
(This article belongs to the Special Issue Cutting-Edge Solutions in Advanced Forestry: Integrating Sensors, AI, IoT, Robotics, and Connectivity)
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14 pages, 6367 KiB  
Article
Development and Application of Tree Radial Measurement Device
by Kejie Zhao, Shangyang Li, Jie Wang, Linhao Sun, Luming Fang and Jingyong Ji
Forests 2024, 15(10), 1710; https://doi.org/10.3390/f15101710 - 27 Sep 2024
Viewed by 870
Abstract
Tree diameter at breast height (DBH) can visually reflect the size of trees and is closely related to forest carbon sinks. As its continuous change is affected by the growing environment, it is an important indicator for forest surveys, and it is of [...] Read more.
Tree diameter at breast height (DBH) can visually reflect the size of trees and is closely related to forest carbon sinks. As its continuous change is affected by the growing environment, it is an important indicator for forest surveys, and it is of great value for forest carbon economy and microecological research. In order to realize the accurate and continuous digital measurement of tree diameter at breast height, this paper develops a low-power tree diameter at breast height continuous measurement device based on the magneto-resistive effect. Compared to the traditional method of tree diameter measurement, this device has the advantages of real-time transmission of data, low-cost, anti-interference, and so on. In Zhejiang Jinhua Nanshan Nature Reserve, at a number of sample sites, tree diameter at breast height for 10 different species of trees was measured every 16 h before developing the corresponding upper software, background analysis software, and real-time acceptance of the measurement data to make timely analysis. After 12 months, experimental application and data analysis showed that the measurement accuracy of this device can be up to 0.001 mm. Compared to traditional tree diameter ruler measurement, measurement error is within 0.1%. This device, therefore, enables the continuous measurement, transmission, storage, and analysis of the tree diameter as a whole, and reveals the growth and carbon sink change rules of the tree diameter of a specific region at a certain age. Full article
(This article belongs to the Special Issue Cutting-Edge Solutions in Advanced Forestry: Integrating Sensors, AI, IoT, Robotics, and Connectivity)
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13 pages, 5155 KiB  
Article
Developing Forest Road Recognition Technology Using Deep Learning-Based Image Processing
by Hyeon-Seung Lee, Gyun-Hyung Kim, Hong Sik Ju, Ho-Seong Mun, Jae-Heun Oh and Beom-Soo Shin
Forests 2024, 15(8), 1469; https://doi.org/10.3390/f15081469 - 21 Aug 2024
Cited by 1 | Viewed by 1298
Abstract
This study develops forest road recognition technology using deep learning-based image processing to support the advancement of autonomous driving technology for forestry machinery. Images were collected while driving a tracked forwarder along approximately 1.2 km of forest roads. A total of 633 images [...] Read more.
This study develops forest road recognition technology using deep learning-based image processing to support the advancement of autonomous driving technology for forestry machinery. Images were collected while driving a tracked forwarder along approximately 1.2 km of forest roads. A total of 633 images were acquired, with 533 used for the training and validation sets, and the remaining 100 for the test set. The YOLOv8 segmentation technique was employed as the deep learning model, leveraging transfer learning to reduce training time and improve model performance. The evaluation demonstrates strong model performance with a precision of 0.966, a recall of 0.917, an F1 score of 0.941, and a mean average precision (mAP) of 0.963. Additionally, an image-based algorithm is developed to extract the center from the forest road areas detected by YOLOv8 segmentation. This algorithm detects the coordinates of the road edges through RGB filtering, grayscale conversion, binarization, and histogram analysis, subsequently calculating the center of the road from these coordinates. This study demonstrates the feasibility of autonomous forestry machines and emphasizes the critical need to develop forest road recognition technology that functions in diverse environments. The results can serve as important foundational data for the future development of image processing-based autonomous forestry machines. Full article
(This article belongs to the Special Issue Cutting-Edge Solutions in Advanced Forestry: Integrating Sensors, AI, IoT, Robotics, and Connectivity)
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