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New Development of Smart Forestry: Machine and Automation

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

Deadline for manuscript submissions: 30 July 2024 | Viewed by 11857

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Special Issue Editors

Prof. Dr. Jiangming Kan

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Guest Editor

School of Technology, Beijing Forestry University, Beijing 100083, China
Interests: forestry machine and its automation; forestry environment sensing and its information processing; computer vision; forest robot

Prof. Dr. Feng Kang

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Guest Editor

School of Technology, Beijing Forestry University, Beijing 100083, China
Interests: forestry equipment and intelligent technology; phenotypic techniques for agricultural and forestry crops; forestry and agricultural robot

Prof. Dr. Liangkuan Zhu

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Guest Editor

School of Mechanical and Electrical Engineering, Northeast Forestry University, Harbin 15004, China
Interests: forestry intelligent equipment and its automation; deep learning in forest; automatic testing system for precision data collection in forest; robust control

Special Issue Information

Dear Colleagues,

Forest sectors around the globe face critical challenges. The forestry workforce is diminishing and aging. Forest worker safety has been a continual issue for forest operations given the remote settings and natural conditions of forestry work sites. Notwithstanding the enormous efforts of academic researchers and industry, a general solution for restricting forest operation mechanization and its automatization remains to be found. Therefore, new strategies for collaborating with various engineering disciplines, such as mechanical, electrical, industrial and computer engineering, to develop new forestry machines and cognitive and control systems and automatic testing systems for precise and intelligent forest operations are urgently needed. This Special Issue aims to provide an overview of the most recent advances in the field of forest operation mechanization and its automatization. This Special Issue is aimed at providing selected contributions on advances in the design, development, and application of forestry machines and their automation, automatic testing systems, and forest robot to help to enhance work efficiency, protect the environment, and improve worker health and safety.

Potential topics include but are not limited to:

Forestry chassis;
Forestry machines and their automation;
Automatic testing systems for precision data collection in forest;
Forest robot;
Artificial Intelligence in forest.

Prof. Dr. Jiangming Kan
Prof. Dr. Feng Kang
Prof. Dr. Liangkuan Zhu
Guest Editors

Manuscript Submission Information

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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.

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Keywords

forestry chassis
harvesting and transportation machine
forest cultivation machine
picking robot
automatic testing system
autonomous robot
computer vision
artificial intelligence

Published Papers (11 papers)

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Research

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17 pages, 32075 KiB

Open AccessArticle

Enhancing Afforestation Practices in Hilly Terrain: A Study on Soil Disturbance by Earth Augers Based on the Discrete Element Method

by Guofu Wang, Wei Zhang, Xingliang Diao, Min Ji, Hu Miao and Meiling Chen

Forests 2024, 15(1), 190; https://doi.org/10.3390/f15010190 - 17 Jan 2024

Viewed by 768

Abstract

Afforestation operations in hilly regions are both arduous and unsafe. The mechanized afforestation method that takes into account soil and water conservation measures is deemed highly important. This paper examines the operational process and the auger’s mechanism of digging below the ground using the discrete element method (DEM). Using this model, soil disturbance parameters and reaction forces are satisfactorily predicted, exhibiting similar trends to experimental observations. This research also examines the influence of key parameters on soil disturbance and distribution patterns and analyzes the conditions and mechanisms of the formation of fish-scale pits to preserve soil and water. A field experiment of pit digging in woodland is carried out to test the performance of the device. The error rates for the actual and simulated values of the efficiency of conveying soil and the distance of throwing soil on plain terrain and slopes were 12.7% and 8.2%, and 8.6% and 15.7%, respectively. Overall, this research provides a theoretical basis for the innovative exploration, development, and optimized design of earth augers in hilly regions. Full article

(This article belongs to the Special Issue New Development of Smart Forestry: Machine and Automation)

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21 pages, 6896 KiB

Open AccessArticle

Sliding Cutting and Cutting Parameters of Concentric Curvilineal Edge Sliding Cutter for Caragana korshinskii (C.K.) Branches

by Haifeng Luo, Shaojun Guo, Zhenkun Zhi and Jiangming Kan

Forests 2023, 14(12), 2379; https://doi.org/10.3390/f14122379 - 05 Dec 2023

Viewed by 781

Abstract

To realize the reduction in cutting force and guarantee pruning section quality in the pruning and stubble work of Caragana korshinskii (C.K.), a concentric curvilineal edge sliding cutter was proposed and the related cutting characteristics were studied. The impacts of branch diameter (D), [...] Read more.

V_{c}

), blade wedge angle (

β

), cutting clearance (

c

) and moisture content (W) on peak torque (T) and cutting energy (E) with this cutter were explored in single-factor tests. On the basis of the Box—Behnken principle, a multi-factor test was further conducted based on the single-factor tests with

V_{c}

β

and c as influencing factors and with T and E as targets, and a regression model was established. Test results indicate that the peak torque (T) increases with the increase in D and

β

and reduces with the growth of

V_{c}

and W; with the increase in

c

, it reduces first and then rises; the cutting energy (E) increases with the growth of D and

β

, declines with the increase in W and diminishes first and then rises with the increase in

V_{c}

and

c

. The optimal parameter combination of the regression model was obtained with

V_{c}

of 2.16 rad/s,

β

of 20

°

and

c

of 1.0 mm, which resulted in a T of 17.25 N·m and P of 7.03 J. The discrepancies between the observed and forecasted values for T and E are 0.87% and 5.004%. New cutting tool and data support for the development of subsequent C.K. branch stubble equipment can be obtained with this new sliding cutter. Full article

(This article belongs to the Special Issue New Development of Smart Forestry: Machine and Automation)

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22 pages, 5143 KiB

Open AccessArticle

A Deformable Shape Model for Automatic and Real-Time Dendrometry

by Lucas A. Wells and Woodam Chung

Forests 2023, 14(12), 2299; https://doi.org/10.3390/f14122299 - 23 Nov 2023

Viewed by 828

Abstract

We present a stereo image-based algorithm for tree stem diameter measurement and form analysis. The algorithm uses planar parametric curves to represent two-dimensional projections of tree stems in stereo images. The curves evolve according to an energy formulation based on the gradients of the images and inductive priors related to biomechanics and morphology of tree stems. After energy minimization, the curves are reconstructed to three dimensions, allowing for diameter measurements at any point along the height of the stem. We describe the algorithm and report the validation test results comparing predicted diameter measurements to external observations. Our findings demonstrate that the algorithm can automatically estimate diameters for trees within 20 m of the camera with an error of 5.52%. We highlight how this method can aid product value optimization through taper analysis and sweep or crook detection. A run-time analysis shows that the algorithm can estimate dendrometric variables for ten trees simultaneously at 15 frames per second on a consumer-grade computer. Furthermore, we discuss the opportunity to produce training data for machine learning algorithms that generalize across domains and eliminate the need to manually tune parameters. Full article

(This article belongs to the Special Issue New Development of Smart Forestry: Machine and Automation)

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13 pages, 6661 KiB

Open AccessArticle

Remote Monitoring of Amur Tigers in Forest Ecosystems Using Improved YOLOX Algorithm

by Yonghua Xie and Wenhua Yu

Forests 2023, 14(10), 2000; https://doi.org/10.3390/f14102000 - 05 Oct 2023

Viewed by 769

Abstract

In response to the challenge of collecting behavioral data on Amur tigers living in forests, a remote real-time data collection approach is proposed. In this article, a novel attention mechanism named CBAM-E is introduced, and CBAM-E as well as the CIoU loss function are incorporated into the YOLOX object detection algorithm, resulting in a new YOLOX model. The new model demonstrates significant performance improvements over the original model, with the

{m A P}_{0.5}

detection accuracy metric rising from 97.32 to 98.18%, indicating a boost of 0.86%, and the

{m A P}_{0.75}

metric increasing from 75.10 to 78.70%, marking an enhancement of 3.60%. The enhanced algorithm is subsequently applied to remote terminal information collection, offering a reference for detection algorithms in the study of wild behaviors of Amur tigers in forests, biodiversity conservation, and the collection of related field data about Amur tigers in the wild. Full article

(This article belongs to the Special Issue New Development of Smart Forestry: Machine and Automation)

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10 pages, 7358 KiB

Open AccessArticle

Study on the Milling Machinability of Bamboo-Based Fiber Composites

by Yucheng Ding, Tongbin Liu, Yaqiang Ma, Chunmei Yang, Changyu Shi, Yongjian Cao and Jiawei Zhang

Forests 2023, 14(9), 1924; https://doi.org/10.3390/f14091924 - 21 Sep 2023

Cited by 2 | Viewed by 843

Abstract

Bamboo-based fiber composites, known as recombinant bamboo, have emerged as a new material in the construction and decoration industry. With its excellent mechanical and ornamental properties, recombinant bamboo is gaining popularity. However, its high hardness and abrasion resistance pose challenges in the milling process. To address this, we conducted an experimental study to investigate the milling machinability of recombinant bamboo. We studied the impact of various factors—fiber angle, feed rate, and spindle speed—on the tangential and normal roughness of milled surfaces. Our findings indicated that increasing the spindle speed within an acceptable range can effectively mitigate issues such as carbonization and endface cracking on a milled surface. Additionally, we developed a prediction model to assess the probability of end splitting in recombinant bamboo. This research aimed to enhance the milling quality of recombinant bamboo, improve control over surface roughness, reduce the likelihood of end splitting, and, ultimately, expand application possibilities. Full article

(This article belongs to the Special Issue New Development of Smart Forestry: Machine and Automation)

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17 pages, 14110 KiB

Open AccessArticle

Experimental Study on the Dynamic Stability of Circular Saw Blades during the Processing of Bamboo-Based Fiber Composite Panels

by Yucheng Ding, Yaqiang Ma, Tongbin Liu, Jiawei Zhang and Chunmei Yang

Forests 2023, 14(9), 1855; https://doi.org/10.3390/f14091855 - 12 Sep 2023

Cited by 1 | Viewed by 718

Abstract

Bamboo-based fiber composite panel is a new type of composite material with excellent performance. When processing bamboo-based fiber composite panels, the dynamic stability of the circular saw blade affects the surface quality of the product and the life of the machinery and equipment. Sawing heat and vibration characteristics can significantly affect the dynamic stability of circular saw blades. Circular saw blade temperature and vibration characteristics are affected by the processing parameters, and the circular saw blade temperature and vibration characteristics are analyzed by changing the processing parameters. Adopting the thermoset coupling model can be used to analyze the change rule of circular saw blade temperature when sawing bamboo-based fiber composite boards, and at the same time to analyze the change rule of circular saw blade temperature, vibration speed, and vibration acceleration through the use of by CCD experiments. The regression equations for circular saw blade temperature, vibration velocity, and vibration acceleration were derived through the use of ANOVA and significance analysis. The thermoset coupling model predictions agree with the experimental results, and the density of the isotherms is progressively thinner as the temperature is conducted from the serrated region to the body of the saw. Finally, the accuracy of the regression equations for circular saw blade temperature, vibration velocity, and vibration acceleration was checked via error analysis. The temperature change regression equation has the highest fitting accuracy, with an average error of only 1.37%; the vibration velocity and vibration acceleration regression equations have poorer fitting accuracy, with an average error of 9.5% and 11.45%, respectively, but all of them have sufficient accuracy to predict the dynamic stability of circular saw blades. The results of the study can provide some guidance for the innovative design of circular saw blades. Full article

(This article belongs to the Special Issue New Development of Smart Forestry: Machine and Automation)

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16 pages, 8507 KiB

Open AccessArticle

Detection of Forestry Pests Based on Improved YOLOv5 and Transfer Learning

by Dayang Liu, Feng Lv, Jingtao Guo, Huiting Zhang and Liangkuan Zhu

Forests 2023, 14(7), 1484; https://doi.org/10.3390/f14071484 - 20 Jul 2023

Cited by 3 | Viewed by 1569

Abstract

Infestations or parasitism by forestry pests can lead to adverse consequences for tree growth, development, and overall tree quality, ultimately resulting in ecological degradation. The identification and localization of forestry pests are of utmost importance for effective pest control within forest ecosystems. To tackle the challenges posed by variations in pest poses and similarities between different classes, this study introduced a novel end-to-end pest detection algorithm that leverages deep convolutional neural networks (CNNs) and a transfer learning technique. The basic architecture of the method is YOLOv5s, and the C2f module is adopted to replace part of the C3 module to obtain richer gradient information. In addition, the DyHead module is applied to improve the size, task, and spatial awareness of the model. To optimize network parameters and enhance pest detection ability, the model is initially trained using an agricultural pest dataset and subsequently fine-tuned with the forestry pest dataset. A comparative analysis was performed between the proposed method and other mainstream target detection approaches, including YOLOv4-Tiny, YOLOv6, YOLOv7, YOLOv8, and Faster RCNN. The experimental results demonstrated impressive performance in detecting 31 types of forestry pests, achieving a detection precision of 98.1%, recall of 97.5%, and mAP@.5:.95 of 88.1%. Significantly, our method outperforms all the compared target detection methods, showcasing a minimum improvement of 2.1% in mAP@.5:.95. The model has shown robustness and effectiveness in accurately detecting various pests. Full article

(This article belongs to the Special Issue New Development of Smart Forestry: Machine and Automation)

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14 pages, 5085 KiB

Open AccessArticle

Experimental Study of Surface Roughness of Pine Wood by High-Speed Milling

by Chunmei Yang, Yaqiang Ma, Tongbin Liu, Yucheng Ding and Wen Qu

Forests 2023, 14(6), 1275; https://doi.org/10.3390/f14061275 - 20 Jun 2023

Cited by 1 | Viewed by 1242

Abstract

The surface roughness of wood has a great influence on its performance and is a very important indicator in processing and manufacturing. In this paper, we use the central composite design experiment (CCD experiment) and artificial neural network (ANN) model to study the changing pattern of surface roughness during the high-speed milling process of pine wood. In the CCD experiments, the spindle speed, feed speed, and depth of cut are used as the influencing factors, and the surface roughness is used as the index to analyze the variation law and fit the surface roughness parameter equation. By measuring the chip size in each group in the CCD experiment, the ANN model is used to predict the surface roughness under this machining parameter by measuring the chip size in each test group. The experimental results showed that the mean error of the surface roughness prediction values in the CCD experiment (12.2%) was larger than that of the ANN model (7.8%), and the mean squared error (MSE) of the ANN model was 0.025, the mean absolute percentage error(MAPE) was 0.01, and the coefficient of determination R² was 0.95. Compared with the CCD experiment, the ANN model had a higher prediction accuracy. The results of this paper can provide some guidance for the prediction of surface roughness during wood processing. Full article

(This article belongs to the Special Issue New Development of Smart Forestry: Machine and Automation)

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14 pages, 7992 KiB

Open AccessArticle

Power Compensation Strategy and Experiment of Large Seedling Tree Planter Based on Energy Storage Flywheel

by Binhai Zhu, Jiuqing Liu, Chunmei Yang, Wen Qu and Peng Ding

Forests 2023, 14(5), 1039; https://doi.org/10.3390/f14051039 - 18 May 2023

Cited by 2 | Viewed by 1126

Abstract

The intermittent hole-digging tree-planting machine shows a periodic short-time peak load law in planting operation, and the operation process is “idling” for small loads most of the time, leading to large torque fluctuations in the transmission system, unscientific power matching, and high energy consumption. To solve the above problems, this article proposes to use a series of energy-saving flywheels in the transmission system of the tree planting machine. On the premise of obtaining holes that meet the target young tree planting requirements, the optimal power compensation strategy for the flywheel system of the tree planting machine is studied to reduce torque fluctuations in the power transmission system, use smaller power drive units, and save energy. Firstly, the nonlinear multi-body dynamics simulation model of soil cutting by the hole-digging component is established. The boundary and contact conditions are set to simulate the power consumption of the hole-digging component at three rotating speeds. Based on the simulation results, the flywheel power compensation strategy is discussed, and the torque fluctuation of the flywheel balance system is analyzed. The results showed that the higher the speed, the greater the power consumption. The power value suddenly increased from 17.82 kW (1.28 s) to 27.93 kW (1.43 s) when the speed was 220 r/min. Then, the power value rapidly decreased, and the power consumption presented a short-term peak feature. The transmission system’s maximum input power is determined as 17.82 kW according to the various simulated power consumption characteristics. The part exceeding the power consumption is compensated by the energy storage flywheel. The total compensation energy was 2382.5 J. After the flywheel system was involved, the maximum output power of the tractor power output shaft decreased by 36.2%, and the peak torque decreased from 445.7 N·m to 285.1 N·m. The power consumption obtained from the field test and simulation was similar, but the energy required to overcome peak load was jointly provided by the flywheel and the engine. The actual input power of the power output shaft during the energy release period of the flywheel system was 18.51 kW when the rotating speed of the hole-digging component was 220 r/min, and the relative error with the simulation value was 2.43%. The measured actual speed reduction of the flywheel system was 8.9%. After installing an energy storage flywheel in the transmission system of the tree planting machine, the output power of the power unit can be stabilized. Tree planting machines can be equipped with smaller power units, which can reduce energy consumption and exhaust emissions. Full article

(This article belongs to the Special Issue New Development of Smart Forestry: Machine and Automation)

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18 pages, 12927 KiB

Open AccessArticle

Study of the Movement of Chips during Pine Wood Milling

by Chunmei Yang, Tongbin Liu, Yaqiang Ma, Wen Qu, Yucheng Ding, Tao Zhang and Wenlong Song

Forests 2023, 14(4), 849; https://doi.org/10.3390/f14040849 - 20 Apr 2023

Cited by 2 | Viewed by 1171

Abstract

Circumferential milling is used in wood processing, yet it generates vast quantities of dust and chips in a single pass, highlighting the need to predict chip dispersion and prevent associated hazards. This article presents findings from a theoretical and experimental analysis of chip size and kinematics of pine wood during cutting. A chip diffusion boundary surface model was established and its key parameters were determined through CCD testing. Results reveal that chip diffusion can be divided into three distinct areas based on motion state: main diffusion, random diffusion, and vortex. Notably, spindle speed and feed rate are most influential on the orthogonal diffusion angle of the main diffusion zone, whereas cutting depth most heavily impacts the top view diffusion angle. Chip scattering on the table showed an exponential increase in average chip size with sampling distance, whereas the boundary surface model accurately characterizes chip motion and demonstrates a reasonable degree of reliability, offering potential in predicting chip morphology and diffusion state. This model has important implications for wood milling practices, particularly in controlling chip dispersion. Full article

(This article belongs to the Special Issue New Development of Smart Forestry: Machine and Automation)

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Review

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21 pages, 14136 KiB

Open AccessReview

Research Hotspots and Frontier Prospects in the Field of Agroforestry Picking Robots in China—Cite Space Bibliographic Analysis

by Na Jia, Hangyu Zhang, Haoshu Gao and Jiuqing Liu

Forests 2023, 14(9), 1874; https://doi.org/10.3390/f14091874 - 14 Sep 2023

Viewed by 870

Abstract

The research on picking robots is vital to the transformation and upgrading of the agroforestry industry and the revitalization and development of rural areas. This paper examines the research field of agroforestry picking robots by meticulously combing and analyzing 623 CNKI and 648 WoS core literature from 2004 to 2022 selected in China Knowledge Network (CNKI) and Web of Science (WoS) databases using Cite Space 6.1R3 software. The analysis includes the quantity of literature, issuing countries, organizations, keywords, keyword clustering, emerging terms, etc. On this basis, research hotspots in the field of agroforestry picking robots are identified, such as research based on the identification of picking targets, the control of motion planning, structural design and simulation, and the planning of walking paths. This paper analyzes and discusses these research hotspots and main lines, providing a reference for future studies in this field. This bibliometric approach can provide comprehensive literature information for research in related fields, as well as identify and summarize the major research hotspots in a shorter time, allowing new researchers to enter the field more quickly and obtain more valuable scientific information. Full article

(This article belongs to the Special Issue New Development of Smart Forestry: Machine and Automation)

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