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To better understand the pattern phase transition of both physical and biological systems, we investigate a two-dimensional spin particle lattice system using statistical mechanics methods together with XY model governed by Hamiltonian equations of motion. By tweaking the coupling strength and the intensity of the generalization field, we observe phase transitions among four patterns of spin particles, i.e., vortex, ferromagnet, worm and anti-ferromagnet. In addition, we analyze the effect of space boundaries on the formations of vortex and worm. Considering the inherent dynamics of individual particles, we revealed the forming mechanism of such phase transitions, which provides a new perspective for understanding the emergence of phase transition of spin particles systems.

Promoting the deployment and application of embedded systems in complex forest scenarios is an inevitable developmental trend in advanced intelligent forestry equipment. Unstructured roads, which lack effective artificial traffic signs and reference objects, pose significant challenges for driverless technology in forest scenarios, owing to their high nonlinearity and uncertainty. In this research, an unstructured road parameterization construction method, “DeepLab-Road”, based on tight coupling of multisource information is proposed, which aims to provide a new segmented architecture scheme for the embedded deployment of a forestry engineering vehicle driving assistance system. DeepLab-Road utilizes MobileNetV2 as the backbone network that improves the completeness of feature extraction through the inverse residual strategy. Then, it integrates pluggable modules including DenseASPP and strip-pooling mechanisms. They can connect the dilated convolutions in a denser manner to improve feature resolution without significantly increasing the model size. The boundary pixel tensor expansion is then completed through a cascade of two-dimensional Lidar point cloud information. Combined with the coordinate transformation, a quasi-structured road parameterization model in the vehicle coordinate system is established. The strategy is trained on a self-built Unstructured Road Scene Dataset and transplanted into our intelligent experimental platform to verify its effectiveness. Experimental results show that the system can meet real-time data processing requirements (≥12 frames/s) under low-speed conditions (≤1.5 m/s). For the trackable road centerline, the average matching error between the image and the Lidar was 0.11 m. This study offers valuable technical support for the rejection of satellite signals and autonomous navigation in unstructured environments devoid of high-precision maps, such as forest product transportation, agricultural and forestry management, autonomous inspection and spraying, nursery stock harvesting, skidding, and transportation.

This research examines the potential of digital twin (DT) technology for reformation within China’s traditional solid-wood-panel processing industry, which currently suffers from production inefficiencies and the slow adoption of digital technology. The research centers around developing a digital twin system, elucidating improvements in manufacturing efficiency, waste management, process simulation, and real-time monitoring. These capabilities facilitate immediate problem solving and offer transparency in the process. The digital twin system is comprised of physical, transport, virtual, and application layers, employing a MySQL database and using the Open Platform Communications Unified Architecture (OPC UA) protocol for communication. The application of this system has led to heightened production efficiency and better material use in the solid-wood-panel manufacturing line. Integrating the dynamic selection adaptive genetic algorithm (DSAGA) into the virtual layer drives the system’s efficiency forward. This evolved approach has allowed for an enhancement of 8.93% in the scheduling efficiency of DSAGA compared to traditional genetic algorithms (GAs), thereby contributing to increased system productivity. Real-time mapping and an advanced simulation interface have strengthened the system’s monitoring aspect. These additions enrich data visualization, leading to better comprehension and a holistic process view. This research has ignited improvements in solid-wood-panel production, illustrating the tangible benefits and representing progress in incorporating digital technology into traditional industries. This research sets a path for transforming these industries into smart manufacturing by effectively bridging the gap between physical production and digital monitoring. Furthermore, the adjustability of this approach extends beyond solid-wood-panel production, indicating the capability to expedite movement towards intelligent production in various other manufacturing sectors.

The leaf area index (LAI) is an essential indicator for assessing vegetation growth and understanding the dynamics of forest ecosystems and is defined as the ratio of the total leaf surface area in the plant canopy to the corresponding surface area below it. LAI has applications for obtaining information on plant health, carbon cycling, and forest ecosystems. Due to their price and portability, mobile devices are becoming an alternative to measuring LAI. In this research, a new method for estimating LAI using a smart device with a fisheye lens (SFL) is proposed. The traditional Otsu method was enhanced to improve the accuracy and efficiency of foreground segmentation. The experimental samples were located in Gansu Ziwuling National Forest Park in Qingyang. In the accuracy parameter improvement experiment, the variance of the average LAI value obtained by using both zenith angle segmentation and azimuth angle segmentation methods was reduced by 50%. The results show that the segmentation of the front and back scenes of the new Otsu method is more accurate, and the obtained LAI values are more reliable. In the efficiency parameter improvement experiment, the time spent is reduced by 17.85% when the enhanced Otsu method is used to ensure that the data anomaly rate does not exceed 10%, which improves the integration of the algorithm into mobile devices and the efficiency of obtaining LAI. This study provides a fast and effective method for the near-ground measurement of forest vegetation productivity and provides help for the calculation of forest carbon sequestration efficiency, oxygen release rate, and forest water and soil conservation ability.

For extracting tree structural data from LiDAR point clouds, individual tree segmentation is of great significance. Most individual tree segmentation algorithms miss segmentation and misrecognition, requiring manual post-processing. This study utilized a hierarchical approach known as segmentation based on hierarchical strategy (SHS) to improve individual tree segmentation. The tree point cloud was divided into the trunk layer and the canopy layer to carry out trunk detection and canopy segmentation, respectively. The effectiveness of SHS was evaluated on three mixed broadleaf forest plots. The segmentation efficacy of SHS was evaluated on three mixed broadleaf forest plots and compared with the point cloud segmentation algorithm (PCS) and the comparative shortest-path algorithm (CSP). In the three plots, SHS correctly identified all the trunk portion, had a recall (r) of 1, 0.98, and 1, a precision (p) of 1, and an overall segmentation rate (F) of 1, 0.99, and 1. CSP and PCS are less accurate than SHS. In terms of overall plots, SHS had 10%–15% higher F-scores than PCS and CSP. SHS extracted crown diameters with R2s of 0.91, 0.93, and 0.89 and RMSEs of 0.24 m, 0.23 m, and 0.30 m, outperforming CSP and PCS. Afterwards, we evaluate the three algorithms’ findings, examine the SHS algorithm’s parameters and constraints, and discuss the future directions of this research. This work offers an enhanced SHS that improves upon earlier research, addressing missed segmentation and misrecognition issues. It improves segmentation accuracy, individual tree segmentation, and provides both theoretical and data support for the LiDAR application in forest detection.

To improve the estimation accuracy of wood density and study the linear correlation between drill feed resistance and wood density, a new micro drill instrument prototype that can simultaneously measure the rotation resistance and feed resistance of the drill needle was designed. The test tree species included hard and soft broad-leaved trees and coniferous trees, and the absolute dry density of each wood sample was measured. The drill resistance data were tested by our newly proposed prototype and a Resistograph 650-SC, and four linear models were established to define the relation between drill resistance and the absolute dry density of the wood. The results showed that (1) the statistical indicators of each model for our proposed prototype were better than the corresponding indicators of the Resistograph 650-SC for three of the four species tested; (2) the coefficient of determination of the linear regression model between the feed resistance of our proposed prototype and the absolute dry density of wood was 0.946; and (3) the statistical indicators of the model that included rotation resistance and feed resistance were better than those of the model that only including rotation resistance. Although the proposed prototype produced a competitive level of accuracy and explicitly demonstrated that including feed resistance improved wood density measurement accuracy, the prototype should be considered a first iteration as further hardware design changes and in-forest performance assessments across wider diverse set of test species are required before a conclusive evaluation can be rendered.

The tractor–trailer wheeled robot (TTWR) is a typical modular mobile robot system. It is widely used in industry and agriculture transportation because of its simple structure, heavy load, high efficiency, strong adaptability, and flexibility. Because it is a nonlinear and underactuated system subjected to nonholonomic constraints, the complexity of the system and influence of the inner wheel difference pose great challenges to its trajectory prediction and path planning. To solve this problem, this paper proposes a trajectory prediction algorithm for an unpowered trailer for the TTWR system. In contrast to traditional single-path planning, the algorithm combines the motion characteristics of the Ackermann chassis tractor, analyzes the trailer motion law, and fully considers the lateral deviation of the trailer during the turning process. The algorithm can plan a reasonable cooperative path for the TTWR system. To verify the feasibility and effectiveness of the algorithm, the algorithm is transplanted to the TTWR intelligent experimental platform. A comparison of the simulation and experimental results shows that under the control of the preview tracking algorithm, the maximum error between the actual trajectory of the trailer and the trajectory predicted by the algorithm is generally no more than 9 cm, and the average error is 3.7 cm. This algorithm can provide an effective basis for TTWR system trajectory planning and path tracking. This algorithm is of great significance to TTWRs in warehousing logistics, freight forwarding, and skidding transportation.

Abstract Background Forest canopies are highly sensitive to their growth, health, and climate change. The study aims to obtain time sequence images in mix foresters using a near-earth remote sensing method to track the seasonal variation in the color index and select the optimal color index. Three different regions of interest (RIOs) were defined and six color indexes (GRVI, HUE, GGR, RCC, GCC, and GEI) were calculated to analyze the microenvironment difference. The key phenological phase was identified using the double logistic model and the derivative method, and the phenology forecast of color indexes was performed based on the long short-term memory (LSTM) model. Results The results showed that the same color index in different RIOs and different color indexes in the same RIO present a slight difference in the days of growth and the days corresponding to the peak value, exhibiting different phenological phases; the mean squared error (MSE), root mean squared error (RMSE), mean absolute error (MAE), and mean absolute percentage error (MAPE) of the LSTM model was 0.0016, 0.0405, 0.0334, and 12.55%, respectively, indicating that this model has a good forecast effect. Conclusions In different areas of the same forest, differences in the micro-ecological environment in the canopies were prevalent, with their internal growth mechanism being affected by different cultivation ways and the external environment. Besides, the optimal color index also varies with species in phenological response, that is, different color indexes are used for different forests. With the data of color indexes as the training set and forecast set, the feasibility of the LSTM model in phenology forecast is verified.

Investigating forest phenology prediction is a key parameter for assessing the relationship between climate and environmental changes. Traditional machine learning models are not good at capturing long-term dependencies due to the problem of vanishing gradients. In contrast, the Gated Recurrent Unit (GRU) can effectively address the problem of vanishing gradients and allow the neural network to capture longer-range dependencies. In this study, an optical camera was used as experimental equipment to obtain forest images. The absolute greenness index (GEI) data of the region of interest (ROI) in the images were calculated to fit the seasonal variation curve of forest phenology. The GRU neural network model was introduced to train and analyze the GEI data, and the performance of the GRU neural network was evaluated using the Mean Squared Error (MSE), Root Mean Squared Error (RMSE), and Mean Absolute Error (MAE) methods. Finally, the model was used to predict the trend of GEI data for the next 60 days. The results showed that: (1) In terms of training and predicting forest phenology, the GRU model was validated using histograms and autocorrelation graphs, which indicated that the distribution of predicted data was consistent with the trend of actual data, the GRU model data was feasible, and the model was stable. (2) The MSE values of the GRU model at rain-fed-CK (preset point 1), sufficient drip irrigation-DIFI (preset point 3), and sufficient furrow irrigation-BIFI (preset point 5) were 9.055 × 10−5, 12.91 × 10−5, and 8.241 × 10−5, respectively. The RMSE values were 9.516 × 10−3, 11.36 × 10−3, and 7.313 × 10−3, respectively. The MAE values were 7.174 × 10−3, 8.241 × 10−3, and 5.351 × 10−3, respectively. These results indicate that the overall performance of the GRU model was good. (3) The predicted GEI data for the next 60 days showed a trend consistent with actual changes in GEI data, as demonstrated by the GRU model. The GRU model has become the preferred method for phenological prediction due to its simple internal structure and relatively short training time. Results show that the GRU model can achieve forest phenological change prediction and can reveal in-depth insights into future forest growth and climate change, providing a theoretical basis for the application of forest phenological prediction.

Abstract Background Whole body vibration (WBV) training as an intervention method can cure chronic low back pain (CLBP). Different WBV parameters exert different effects on lumbar-abdominal muscle performance. Currently, there is a lack of study researched the influence of WBV training on patients with CLBP by lumbar–abdominal muscle activity. Therefore, this study aimed to investigate how WBV and exercise and their interactions influence lumbar-abdominal muscle activity in patients with CLBP. Methods a group of ambulatory patients with chronic low back pain. Muscle activities of the multifidus (MF), erector spinae (ES), abdominal oblique externus muscle (AOE) and the rectus abdominis muscle (RA) were measured by surface electromyography, whereas participants performed 4 different exercises (single bridge, plank, side stay and V crunch) during three whole body vibration conditions and a no-vibration condition in a single experimental session. Results Compared with the same exercises without whole body vibration, muscle activity increased when whole body vibration was added to the exercises. MF; the WBV frequency (P = 0.002,) and exercise (P

Maturity grading and identification of Camellia oleifera are prerequisites to determining proper harvest maturity windows and safeguarding the yield and quality of Camellia oil. One problem in Camellia oleifera production and research is the worldwide confusion regarding the grading and identification of Camellia oleifera fruit maturity. To solve this problem, a Camellia oleifera fruit maturity grading and identification model based on the unsupervised image clustering model DeepCluster has been developed in the current study. The proposed model includes the following two branches: a maturity grading branch and a maturity identification branch. The proposed model jointly learns the parameters of the maturity grading branch and maturity identification branch and used the maturity clustering assigned from the maturity grading branch as pseudo-labels to update the parameters of the maturity identification branch. The maturity grading experiment was conducted using a training set consisting of 160 Camellia oleifera fruit samples and 2628 Camellia oleifera fruit digital images collected using a smartphone. The proposed model for grading Camellia oleifera fruit samples and images in training set into the following three maturity levels: unripe (47 samples and 883 images), ripe (62 samples and 1005 images), and overripe (51 samples and 740 images). Results suggest that there was a significant difference among the maturity stages graded by the proposed method with respect to seed oil content, seed soluble protein content, seed soluble sugar content, seed starch content, dry seed weight, and moisture content. The maturity identification experiment was conducted using a testing set consisting of 160 Camellia oleifera fruit digital images (50 unripe, 60 ripe, and 50 overripe) collected using a smartphone. According to the results, the overall accuracy of maturity identification for Camellia oleifera fruit was 91.25%. Moreover, a Gradient-weighted Class Activation Mapping (Grad-CAM) visualization analysis reveals that the peel regions, crack regions, and seed regions were the critical regions for Camellia oleifera fruit maturity identification. Our results corroborate a maturity grading and identification application of unsupervised image clustering techniques and are supported by additional physical and quality properties of maturity. The current findings may facilitate the harvesting process of Camellia oleifera fruits, which is especially critical for the improvement of Camellia oil production and quality.

Abstract Background Patellofemoral pain syndrome (PFPS) is common and affects approximately 15% of individuals at different ages and activity levels. As a non-surgical intervention, physiotherapy is widely used to treat PFPS. Neuromuscular training exercise is one of the most effective methods for decreasing musculoskeletal pain and improving knee function. However, the effectiveness of neuromuscular training exercise for treating PFPS is not without argument. The purpose of this study is to evaluate the effect of neuromuscular training exercise on patellofemoral pain and whether the neuromuscular training exercise have more advantage effects than taping and health education. Methods We will operate a prospective, single-blind, randomized controlled trial of 60 patients with patellofemoral pain. Individuals will be indiscriminately assigned to two intervention groups and a health education group. The neuromuscular training exercise which includes the muscle strength training, balance training and knee joint proprioception training, and taping group will use “Y” and “I” type taping on the participants three times a week for three months. The health education group will be given education lectures once each week and which last for three months. The primary outcome measures include the adverse events, visual analog scale for pain, and Anterior Knee Pain Scale Index, which is a knee function self-report questionnaire to evaluate the function of the knee especially for PFPS patients. The secondary outcome measures are the muscle strength and endurance of knee joint flexion and extensor muscles, knee joint proprioception, muscle thickness of the quadriceps femoris, knee function ability, and quality of life. We will manage the intention-to-treat analysis for individuals who will withdraw from this study. Discussion According to previous studies, neuromuscular training exercise and the taping method are effective treatment for PFPS patients. In this study, we will perform a neuromuscular training exercise for patients with PFPS. We believe that this study may prove the effectiveness of neuromuscular training exercise in treating PFPS. Trial registration Chinese Clinical Trial Registry, ChiCTR1800014995. Registered on 27 February 2018.

Oil tea (Camellia oleifera Abel.) is a high-quality woody oil crop unique to China and has extremely high economic value and ecological benefits. One problem in oil tea production and research is the worldwide confusion regarding oil tea cultivar nomenclature. The purpose of this study was to automatic recognize some oil tea cultivars using bilinear attention network. For this purpose, we explored this possibility utilizing the bilinear attention network for five common China cultivars Ganshi 83-4, Changlin 53, Changlin 3, Ganshi 84-8, and Gan 447. We adopted the bilinear EfficientNet-B0 network and the convolutional block attention module (CBAM) to build BA-EfficientNet model being able to automatically and accurately recognize oil tea cultivars. In addition, the InceptionV3, VGG16, and ResNet50 algorithms were compared with the proposed BA-EfficientNet. The comparative test results show that BA-EfficientNet can accurately recognize oil tea cultivars in the test set, with overall accuracy and kappa coefficients reaching 91.59% and 0.89, respectively. Compared with algorithms such as InceptionV3, VGG16, and ResNet50, the BA-EfficientNet algorithm has obvious advantages in most evaluation indicators used in the experiment. In addition, the ablation experiments were designed to quantitatively evaluate the specific effects of bilinear networks and CBAM modules on oil tea cultivar recognition results. The results demonstrate that BA-EfficientNet is useful for solving the problem of recognizing oil tea cultivars under natural conditions. This paper attempts to explore new thinking for the application of deep learning methods in the field of oil tea cultivar recognition under natural conditions.

To address the current challenges of the heavy workload, time-consuming nature and labor-intensiveness involved in existing crested ibis’s (Nipponia nipponTemminck, 1835) habitat identification approaches, this paper proposes an automatic habitat identification method based on spatiotemporal density detection. With consideration of the characteristics of the crested ibis’s trajectory data, such as aggregation, repeatability, and uncertainty, this method achieves detecting the crested ibis’s stopping points by using the spatial characteristics of the trajectory data. On this basis, an improved spatiotemporal clustering-based DBSCAN method is proposed in this paper, incorporating temporal characteristics of the trajectory data. By combining the spatial and temporal features, the proposed method is able to accurately identify the roosting and foraging sites among the crested ibis’s stopping points. Supported by remote sensing images and field investigations, it was found that the method proposed in this paper has a good clustering effect and can effectively identify the crested ibis’s foraging sites and overnight roosting areas. Specifically, the woodland, farmland, and river areas are the common foraging sites for the crested ibis, while the woodland with large trees is their common overnight site. Therefore, the method proposed in this paper can provide technical support for identifying and protecting the crested ibis’s habitats.

Micro-drilling resistance method is a widely used tree ring micro-destructive detection technology. To solve the problem that the detection signal of the analog micro-drilling resistance method has excessive noise interference and cannot intuitively identify tree ring information, this research proposes a digital micro-drilling resistance method and provides a recommended hardware implementation. The digital micro-drilling resistance method adopts the photoelectric encoder instead of ADC as the signal sampling module. Through the theoretical analysis of the DC motor characteristic, the PWM closed-loop speed control, the detection principle of the digital method is given. Additionally, the experimental equipment that can complete the detection of the digital method and the analog method simultaneously is designed to carry out comparative experiments. The experimental results show that: (1) The detection results of the digital method have a better-quality signal which can intuitively identify the tree rings. (2) The average correlation coefficient reaches 0.9365 between the detection results of the digital method and the analog method. (3) The average Signal-to-Noise Ratio (SNR) of the digital method is 39.0145 dB, which is 19.2590 dB higher than that of the analog method. The average noise interference energy in the detection result of the digital method is only 1.27% of the analog method. In summary, hardware implementation of the digital micro-drilling resistance method can correctly reflect the tree ring information and significantly improve the signal quality of the micro-drilling resistance technology. This research is helping to improve the identification accuracy of micro-drilling resistance technology, and to develop the application of tree ring micro-destructive detection technology in the high-precision field.

Due to global warming, changes in plant phenology such as an early leaf spreading period in spring, a late abscission period in autumn, and growing season extension are commonly seen. Here, near-earth remote sensing images were used to monitor the canopy phenology of Populus tomentosa B301 in planted forests under full drip irrigation, full furrow irrigation, and no irrigation (rain fed). Experiments were conducted to collect phenological data across a growing season. Continuous canopy images were used to calculate different vegetation indices; the key phenological period was determined via the double logistic model and the curvature method. The effects of irrigation methods and precipitation in the rainy season on tree growth changes and key phenological periods were analyzed. The results showed that: (1) The green chromatic coordinate (GCC) conformed to the vegetation index of the tree species canopy phenological study. (2) During the phenological period throughout the year, the GCC reaching peak time (MOE) of the canopy phenology of Populus tomentosa B301 was the same in the three methods, while the time of shedding at the end of the growing season without irrigation (preset point 1) was 8 days longer than with full drip irrigation (preset point 3), and 7 days faster than with full furrow irrigation (preset point 5). (3) In the preliminary rainy season, different irrigation volumes induced different growth changes and phenological periods of the trees, resulting in different data of vegetation indicators under different growth conditions. (4) During the rainy season, the precipitation had different effects on cultivating P. tomentosa B301 using the three methods, that is, high precipitation could increase the growth rate of the fully irrigated area, otherwise the growth rate of this tree species was increased in full drip irrigation areas. Precipitation was lower and irregular, and the growth rate of this species was faster than the other two irrigation methods in the non-irrigated area, which was more adaptable to external environmental changes. The internal growth mechanism of the phenological changes in different areas of the planted forests was influenced by the different cultivation methods. Moreover, the collected phenological data provide a basis for the study of plant phenology with large data sets and deepens our understanding of the phenology of planted forests in response to climate change.

According to the existing multi-antenna relay forwarding protocol, the relay matrix can be split into two parts-relay nodes forward and backward filters direct concatenation, then in actual channel matrix design, relay's forward and backward channel conditions are not ideal, which make system bit error rate (BER) performance is poor. Based on this problem, this paper proposes a modular algebraic precoding relay system model based on lattice reduction. At first, this method re-builds the relay's forward and backward channel matrices, which can make the channel satisfy the orthogonality characteristics. Then, a modified equivalent relay channel matrix is precoding, which solves tradition algorithms' poor performance problem when channel orthogonality is not good, and based on this idea, two improved algorithms are proposed. These two new algorithms can achieve better BER performance under poor relay channel conditions, make each user's performance average, and satisfy system fairness requirements. Simulation results show that comparing with tradition algorithms, this paper proposes a relay channel model which is based on the lattice reduction precoding method, it gains a certain bit error performance. Finally, the improved algorithms are compared and analyzed with the sorting-based algorithm. The results show that the proposed algorithms have better performance. Therefore, in order to ensure users fairness, the optimization algorithm can be used instead of the high complexity sorting-based algorithm in the actual system.

Cultivar identification is a basic task in oil tea (Camellia oleifera C.Abel) breeding, quality analysis, and an adjustment in the industrial structure. However, because the differences in texture, shape, and color under different cultivars of oil tea are usually inconspicuous and subtle, the identification of oil tea cultivars can be a significant challenge. The main goal of this study is to propose an automatic and accurate method for identifying oil tea cultivars. In this study, a new deep learning model is built, called EfficientNet-B4-CBAM, to identify oil tea cultivars. First, 4725 images containing four cultivars were collected to build an oil tea cultivar identification dataset. EfficientNet-B4 was selected as the basic model of oil tea cultivar identification, and the Convolutional Block Attention Module (CBAM) was integrated into EfficientNet-B4 to build EfficientNet-B4-CBAM, thereby improving the focusing ability of the fruit areas and the information expression capability of the fruit areas. Finally, the cultivar identification capability of EfficientNet-B4-CBAM was tested on the testing dataset and compared with InceptionV3, VGG16, ResNet50, EfficientNet-B4, and EfficientNet-B4-SE. The experiment results showed that the EfficientNet-B4-CBAM model achieves an overall accuracy of 97.02% and a kappa coefficient of 0.96, which is higher than that of other methods used in comparative experiments. In addition, gradient-weighted class activation mapping network visualization also showed that EfficientNet-B4-CBAM can pay more attention to the fruit areas that play a key role in cultivar identification. This study provides new effective strategies and a theoretical basis for the application of deep learning technology in the identification of oil tea cultivars and provides technical support for the automatic identification and non-destructive testing of oil tea cultivars.

The detection and recognition of unstructured roads in forest environments are critical for smart forestry technology. Forest roads lack effective reference objects and manual signs and have high degrees of nonlinearity and uncertainty, which pose severe challenges to forest engineering vehicles. This research aims to improve the automation and intelligence of forestry engineering and proposes an unstructured road detection and recognition method based on a combination of image processing and 2D lidar detection. This method uses the “improved SEEDS + Support Vector Machine (SVM)” strategy to quickly classify and recognize the road area in the image. Combined with the remapping of 2D lidar point cloud data on the image, the actual navigation requirements of forest unmanned navigation vehicles were fully considered, and road model construction based on the vehicle coordinate system was achieved. The algorithm was transplanted to a self-built intelligent navigation platform to verify its feasibility and effectiveness. The experimental results show that under low-speed conditions, the system can meet the real-time requirements of processing data at an average of 10 frames/s. For the centerline of the road model, the matching error between the image and lidar is no more than 0.119 m. The algorithm can provide effective support for the identification of unstructured roads in forest areas. This technology has important application value for forestry engineering vehicles in autonomous inspection and spraying, nursery stock harvesting, skidding, and transportation.

Analysis of a tree ring is the primary method for determining the growth and age of a tree. In a microdestructive tree-ring measurement system, the tree under test is drilled with a microdrill at a constant rotating speed to detect the difference in density between the early and late wood, thereby realizing a microdestructive measurement of the tree-ring. The measurement system comprises a microdrill with a diameter of 3 mm, mechanical transmission, direct current (DC) servomotor, stepper motor, and control and detection circuit. The DC servomotor and stepper motor realize rotation and translation of the microdrill, respectively, through mechanical transmission. When the microdrill rotates and drills into the tree, the control and detection circuit samples and acquires the armature current of the DC servomotor, which is proportional to the resistance encountered by the drill bit and reflects the change in the density of the tree. The tree-ring number can be obtained by filtering the sampled original signals of the armature current using a finite impulse response (FIR) filtering algorithm. The annual rings of larch and fir tree discs were measured and tested using the designed system. It was observed that the average annual ring measurement accuracy of the larch discs reached 95.28%, while that of the fir discs was 84.16%. The diameter of the drill hole in the trunk was less than 3 mm after measuring the living wood, thereby achieving a microdestructive measurement of the tree-ring.

Space grippers are the key devices for accomplishing space non-cooperative target capture, which has a great significance for satellite services and space debris removal. This article proposes a novel mechanical gripper device for the capture of aluminum honeycomb panels of non-cooperative satellites. The gripper was modeled and assembled in the three-dimensional modeling platform UGNX. The model was imported into the simulation software ADAMS. ADAMS is capable of analyzing the kinematic feasibility of the gripper model. Collision and penetrating power analysis of the mechanical claws into an aluminum honeycomb plate were carried out in LS-DYNA. Through non-vertical piercing experiment, the maximum approaching angle tolerance is 10°. The established rigid connection can withstand a destructive force greater than 1000 N. A prototype of the mechanical gripper is built. A ground test was carried out with this prototype, in which a test-platform simulated the space microgravity environment. The results certified that the prototype could reach the target at a relative speed of 0.5 m/s and then quickly complete the grabbing motion and establish a rigid connection. The experiment shows that this mechanical gripper can accomplish the task of repeatedly capturing the surface of non-cooperative space satellites.

Based on Internet-of-Things and multi-sensor technology, an intelligent wireless monitoring system was developed to obtain field ecological parameters and provide forest fire warning in real-time. The GPRS and China’s Beidou satellite communication were selectively used for date transmission in the field with weak cell phone signals. This monitoring system is mainly composed of several field ecological monitoring stations, a cloud server, and online system software. Atmosphere, soil, sunlight and plant parameters of different regions are obtained real-time by sensors stably and reliably. This system has functions such as field ecological data storage, dynamic query, report generation, and data analysis. As an example of typical application, the forest fire weather grade, which was supplemented with the litter layer soil humidity, was calculated to realize the early warning of the local forest fire in this system through continuous experiments at Beijing Jiufeng National Forest Park from March to May 2017 and Inner Mongolia from March to June 2018. The success ratios of data transmission through Beidou satellite were 98.57%, 99.43%, 99.59%, and 98.85%, respectively, in Beijing, and through GPRS were 99.89% and 99.90% in Inner Mongolia. Long-term real-time field ecological monitoring and forest fire warning were successfully realized. This system can be widely used for big data field acquisition and analysis in forest and agriculture regions.

Logging harvesters represent a set of high-performance modern forestry machinery, which can finish a series of continuous operations such as felling, delimbing, peeling, bucking and so forth with human intervention. It is found by experiment that during the process of the alignment of the harvesting head to capture the trunk, the operator needs a lot of observation, judgment and repeated operations, which lead to the time and fuel losses. In order to improve the operation efficiency and reduce the operating costs, the point clouds for standing trees are collected with a low-cost 2D laser scanner. A cluster extracting algorithm and filtering algorithm are used to classify each trunk from the point cloud. On the assumption that every cross section of the target trunk is approximate a standard circle and combining the information of an Attitude and Heading Reference System, the radii and center locations of the trunks in the scanning range are calculated by the Fletcher-Reeves conjugate gradient algorithm. The method is validated through experiments in an aspen forest, and the optimized calculation time consumption is compared with the previous work of other researchers. Moreover, the implementation of the calculation result for automotive capturing trunks by the harvesting head during the logging operation is discussed in particular.

This article presents the kinematics model and control simulation of a hydraulic boom. This hydraulic boom is used on the logging harvester, which is a typical forestry vehicle. The kinematics equations of the hydraulic boom and the transformation between joint variables and hydraulic cylinder variables are obtained for the control purpose. An autonomous control method is suggested for the harvesting head to capture trunks. There are two separate closed loops in the control scheme: the inner closed loop realizes the control on the hydraulic cylinders, and the outer loop plans the desired trajectory of the harvesting head to capture trunks based on the laser measurement data. An interactive real-time dynamic simulation system is developed, and the proposed control method is verified.

This paper describes a small range six-axis accelerometer (the measurement range of the sensor is ±g) with high sensitivity DCB (Double Cantilever Beam) elastic element. This sensor is developed based on a parallel mechanism because of the reliability. The accuracy of sensors is affected by its sensitivity characteristics. To improve the sensitivity, a DCB structure is applied as the elastic element. Through dynamic analysis, the dynamic model of the accelerometer is established using the Lagrange equation, and the mass matrix and stiffness matrix are obtained by a partial derivative calculation and a conservative congruence transformation, respectively. By simplifying the structure of the accelerometer, a model of the free vibration is achieved, and the parameters of the sensor are designed based on the model. Through stiffness analysis of the DCB structure, the deflection curve of the beam is calculated. Compared with the result obtained using a finite element analysis simulation in ANSYS Workbench, the coincidence rate of the maximum deflection is 89.0% along the x-axis, 88.3% along the y-axis and 87.5% along the z-axis. Through strain analysis of the DCB elastic element, the sensitivity of the beam is obtained. According to the experimental result, the accuracy of the theoretical analysis is found to be 90.4% along the x-axis, 74.9% along the y-axis and 78.9% along the z-axis. The measurement errors of linear accelerations ax, ay and az in the experiments are 2.6%, 0.6% and 1.31%, respectively. The experiments prove that accelerometer with DCB elastic element performs great sensitive and precision characteristics.