Tree height is a key parameter for assessing forest carbon storage， and satellite-borne laser radar technology provides an effective means for large-scale monitoring. The new generation of ice， cloud and land elevation satellite-2 （ICESat-2） equipped with the advanced topographic laser altimeter system （ATLAS） generates a lot of noise in the process of receiving signals， and the terrain is a key factor affecting the denoising results. To address this problem， a ground slope adaptive density clustering denoising algorithm is proposed to complete the photon cloud data denoising. Iterative median filtering and dynamic residual threshold method are used to classify photon clouds and then extract tree height. The canopy height model （CHM） obtained from airborne laser radar data is used as verification data. The reliability of extracting tree height from ICESat-2/ATLAS global geolocated photon data （ATL03） is analyzed and evaluated from three aspects： strong and weak beams， slope， and vegetation coverage. The results show that， 1） The recall rate （R）， precision rate （P） and harmonic mean （F） of the proposed denoising algorithm are better than those of the differential progressive gaussian adaptive denoising algorithm （DRAGANN）. 2） The accuracy of extracting tree height from nighttime strong beam data is the best， with a mean absolute error （MAE） of 2.49 m and a root mean square error （RMSE） of 3.03 m. 3） As the slope increases， the accuracy of tree height extraction gradually decreases， and the RMSE increases from 2.25 m to 6.52 m. 4） As the vegetation coverage increases， the accuracy of tree height extraction gradually decreases， and the RMSE increases from 3.06 m to 4.53 m. The results show that it is feasible to extract tree height using ATL03 photon cloud data， which can provide effective data support for studying forest growth conditions in forest areas.

Identifying the types and distribution of tree species is the foundation for monitoring tree diversity， and is crucial for forest protection and management and sustainable development of forests. Forest plot-scale hyperspectral images and LiDAR point cloud data scanned by unmanned aerial vehicle （UAV） were used as the data source， and based on the individual tree-scale hyperspectral and point cloud data obtained by the canopy height model， a convolutional neural network （CNN-EGNet） model combined with the attention mechanism of efficient channel attention （ECA） was proposed in this study， aiming at achieve precise tree species identification in mixed coniferous and broadleaf forests in the Maoershan area of Shangzhi City， Then， CNN-EGNet with three traditional CNN models VGG16， VGG19， and GoogLeNet in identification accuracy. Finally， on the basis of the results of the tree species identification， tree species diversity indices （Shannon-Wiener， Simpson， Pielou， Species richness） in the study area were calculated with the 40 m × 40 m window. Results showed that the proposed CNN-EGNet model achieved an overall accuracy （OA） of 89.58% and a Kappa coefficient of 0.8661. Compared with conventional models， the overall accuracy for species identification improved by 9.37%， 5.20%， and 14.58%， and the Kappa coefficients increased by 0.1175， 0.0652， and 0.1896， respectively. The Shannon-Wiener index primarily ranged from 0.8 to 1.4， while the Simpson index predominantly clustered between 0.5 and 0.7. The Pielou index generally fell within the range of 0.7 to 0.95， and the species richness index mostly varied between 3 and 5 species. The tree species diversity indices indicated that the distribution of tree species was uneven， with certain species being dominant while others were relatively scarce. The results of the study can provide technical and data references for the identification of tree species and the protection and management of tree species diversity in the mixed coniferous and broadleaf forests in the Northeast of China， and validate the possibility of identification， monitoring and evaluating the diversity of tree species by combining hyperspectral and LiDAR data from UAVs with convolutional neural networks.

In order to explore the effects of different soil and water conservation engineering measures on soil productivity of slope farmland in black soil area， two kinds of soil and water conservation measures （ terrace and ridge ） in Xingmu small watershed of Dongliao County， Liaoyuan City， Jilin Province in northeast black soil area were taken as the research objects， and the slope farmland without measureswas taken as the control （CK1 and CK2）. The differences of soil productivity of different soil and water conservation measures at different slope positions and the dominant factors affecting soil productivity were compared and analyzed. The results showed that： 1） terraces and ridges significantly improved soil quality. Compared with CK1 and CK2 slope farmland without measures， the contents of total nitrogen， total potassium， available phosphorus， organic matter and clay mass fraction in terraces and ridges increased by 40.25% and 16.16%， 9.14% and 5.57%， 33.27% and 24.50%， 30.25% and 7.94%， 8.47% and 5.03%， respectively， and the sand mass fraction decreased by 7.08% and 12.35 %. 2） The middle slope position was the most sensitive to soil and water conservation measures. Compared with CK1 and CK2， the contents of total nitrogen， total potassium， available phosphorus， organic matter and clay mass fraction in the middle slope position increased by 84.58% and 30.15%， 16% and 8.04%， 48.49% and 38.92%， 38.02% and 11.24%， 18.92% and 9.21%， respectively， and the difference was significant （P<0.05）. 3） The soil productivity index ranged from 0.29 to 0.49， and there were differences in soil productivity index under different measures. Compared with CK1， the soil productivity index of terraced fields increased by 24.05%， 65.85% （P<0.05） and 11.81% at the upper， middle and lower slopes， respectively. Compared with CK2， the soil productivity index of ridges increased by 10.00%， 49.95% （P<0.05） and 35.20% at the upper， middle and lower slopes， respectively. 4） The differences of soil water content， total nitrogen， total potassium， available phosphorus content and organic matter mass fraction under different measures gradually decreased with the increase of soil depth. The depth of 0-10cm soil layer was significantly higher than that of other soil layers， while the change trend of soil bulk density was opposite. 5） Multivariate variance and redundancy analysis showed that the type of measures， slope position， soil layer， type×slope position， type×soil layer had significant effects on soil productivity. Soil total nitrogen mass fraction had the greatest impact on soil productivity index， followed by soil bulk density. The implementation of soil and water conservation measures on slope farmland in black soil area increased soil water content， soil nutrients， clay mass fraction， and reduced bulk density and sand mass fraction， indicating that soil and water conservation measures can effectively improve soil productivity， and terrace measures are better than ridges； among different measures， the change of middle slope position is the most significant， which mainly affects the soil productivity by changing the soil total nitrogen mass fraction and bulk density of middle slope position. It is recommended to give priority to the implementation of terrace projects in the middle slope position， and to use total nitrogen and bulk density as key monitoring indicators to maximize the benefits of soil improvement and productivity improvement of soil and water conservation measures.

In order to explore the impact mechanism of vegetation restoration on soil aggregate structure and organic carbon dynamics in industrial and mining wastelands， Pinus sylvestris var. mongolica plantations with different planting years （18， 20， 23， 32 years） on industrial and mining wastelands in Qingling Forest Farm， Hegang City were studied. By combining field sampling and indoor analysis， the particle size distribution of soil aggregates， organic carbon mass fraction， and main soil chemical factors （pH， total nitrogen， and total phosphorus， etc.） in the 0-20 cm and 20-40 cm soil layers were systematically measured. The research results indicated that： 1） With the increase of vegetation restoration years， the structure of soil aggregates underwent significant changes. Among them， the proportion of large aggregates larger than 2 mm showed a significant increase trend （an increase of 50.76%）， while the proportion of micro aggregates smaller than 0.25 mm decreased significantly （a decrease of 44.24%）. The mean weight diameter （MWD） and geometric mean diameter （GMD） increased by 17.90% and 18.42%， respectively， indicating that the stability of soil aggregates was significantly enhanced with increasing forest age. 2） Through Mantel analysis， it was found that forest age， soil depth， and chemical factors had a significant impact on soil organic carbon （SOC） accumulation （P<0.01）. At the same time， forest age and chemical factors had a significant impact on aggregate stability （P<0.05）. The trend of changes in surface soil （0-20 cm） and deep soil （20-40 cm） was basically consistent， but the response of deep soil to environmental factors was relatively lagging behind. 3） Random forest analysis showed that ammonia nitrogen （ \mathrm{N} {\mathrm{H}}_{\mathrm{4}}^{+}- N）， pH， total phosphorus （TP）， and nitrate nitrogen （ \mathrm{N} {\mathrm{O}}_{\mathrm{3}}^{-}-N） had the highest explanatory power for soil aggregate stability. In summary， long-term vegetation restoration can effectively improve the soil structure of industrial and mining wastelands， promote the accumulation and sequestration of soil organic carbon， and provide new ideas for the restoration and reconstruction of degraded ecosystems.

In order to solve the problem of poor flower bud growth caused by insufficient light in autumn under facility cultivation conditions， four-year-old blueberry ‘Liberty’ container seedlings were used as materials， and LED lights were used as light sources. Setting different light intensity， light quality （red and blue light ratio） and photoperiod， a three-factor and three-level orthogonal test， and greenhouse natural light was used as a control. The chlorophyll content， gas exchange parameters， and nutrient resorption efficiency of nitrogen （N） and phosphorus （P） in different periods were measured. The weight and morphological indexes （transverse diameter and length） of flower buds were used to study the characteristics of photosynthesis and nutrient resorption of blueberry under different light conditions and their relationship with flower bud quality. The results showed that the photosynthetic capacity of blueberry was different under different light treatments after three weeks of supplementary light. The net photosynthetic rate of blueberry was the highest under low light intensity （300 μmol/（m²·s））， medium or low red and blue light quality ratio （1∶1 or 1∶3）， medium or short photoperiod （7.5 h or 6 h）. The nutrient resorption efficiency of blueberry under different light treatments was different after two weeks of stopping light supplementation. Under the light conditions of medium light intensity （450 μmol/（m²·s））， high red and blue light ratio （3∶1） and short photoperiod （6 h）， the nutrient resorption efficiency of nitrogen （N） and phosphorus （P） was the highest. The quality of flower buds was better under the conditions of high light intensity （600 μmol/（m²·s））， medium or low red and blue ratio （1∶1 or 1∶3）， medium or short photoperiod （7.5 h or 6 h） or medium light intensity （450 μmol/（m²·s））， high red and blue ratio （3∶1） and short photoperiod （6 h）. Different light treatments can affect the quality of flower buds by regulating the photosynthesis and the nutrient resorption efficiency of N and P in late-season blueberries. The effect of nutrient resorption efficiency on flower bud quality was greater than that of net photosynthetic rate， and the effect of N nutrient resorption efficiency on flower bud quality was greater than that of P nutrient resorption efficiency. Based on the effects of photosynthesis and nutrient resorption on flower bud quality， it was concluded that medium light intensity （450 μmol/（m²·s））， low red and blue light ratio （1∶3）， and short photoperiod （6 h） were most beneficial to improve the flower bud quality of blueberries.

This study focuses on the effects of extractives in Dipteryx odorata wood from South America on the color and dimensional stability of wood， offering a theoretical foundation for the efficient utilization of Dipteryx odorata wood. Employing the CIE 1976 L ^* a ^* b ^* color space system， colorimetric parameters of diverse wood colors of Dipteryx odorata wood were quantitatively analyzed. Fourier transform infrared spectrometer （FTIR） and gas chromatography-mass spectrometry （GC-MS） techniques were used to characterize the chemical composition of extractives， analyzing the influence of the composition of extractives on Dipteryx odorata wood color. The existence positions of extractives within wood were observed via microstructure observation. Additionally， the impact of extractives on dimensional stability was analyzed by testing wood's wet swelling rate and equilibrium moisture content under various constant humidities. Results demonstrated that the color differences in Dipteryx odorata wood was mainly influenced by the types and content of chromogenic substances in the extractives， especially phenols， flavonoids and analogs， heterocyclic compounds， and terpenes/resin acids， that the higher the content of the extractives， the higher the a ^* value and the lower the L ^* value. Ray cells were one of the main locations of extractives in Dipteryx odorata wood， affected radial moisture transport， thus having a significant impact on the radial dimensional stability of wood. The findings provide technical support for regulating Dipteryx odorata wood color and enhancing its overall properties， and laying a theoretical foundation for the high-value utilization of Dipteryx odorata wood.

Natural wood end surfaces exhibit irregular textures and defect features， making end surface recognition and localization a challenging problem. To enhance detection accuracy while reducing model parameters and improving computational efficiency for mobile deployment， this study proposes an improved end-to-end deep learning model tailored for log detection by enhancing the YOLO11 architecture. Firstly， the PP-LCNet backbone is adopted to replace the original YOLO11 backbone， effectively reducing the number of parameters， expanding the receptive field， and improving large target detection precision. Secondly， a parameter-free attention mechanism， SimAM， is integrated into the neck network to adaptively emphasize critical features and suppress redundant information， thereby enhancing small target recognition capabilities. Finally， the normalized Wasserstein distance （NWD） loss function is introduced， which is more suitable for measuring similarity between extremely small targets， further improves the accuracy and precision of wood end surface identification. Experimental results demonstrate that the improved model achieves higher end surface recognition accuracy compared to the baseline model， the improved model improves 2.65% and 5.29% on the mAP@0.5 and mAP@0.95 metrics， and FLOPs are decreased by 15.15%. It has good application value in the field of log volume measurement.

To predict the micro and macro failure behavior of wood under load， a prediction approach of wood damage precursors based on the b-value （describe the frequency distribution characteristics of acoustic emission signals） of the acoustic emission （AE） signal and the critical slowing down （CSD） characteristics was proposed. Firstly， to analyze the impact of load application rate on the wood failure process， two loading rates of 2 mm/min and 5 mm/min were employed in the wood three-point bending experiment， and AE signals released during the damage process of wood specimens were collected at a sampling rate of 500 kHz. Subsequently， an improved b-value calculation approach based on the classification of AE signal amplitude was proposed， and the AE signals generated during the experiment were divided into three levels， and the process of crack generation and development inside the wood specimen under load was described accordingly. Finally， in accordance with the principle of CSD， a prediction method of wood fracture precursors based on the length correlation coefficient and variance of AE signal was presented. The results demonstrated that the combination of different levels of b-values can reflect the fracture features and the generation of microcracks in the damage process of the specimen. CSD occurred when wood cracked at both the micro and macro levels， which was manifested in the increase of the correlation coefficient and variance of AE signal. At different loading rates， when the load reached approximately 80% of the ultimate yield strength， all the specimens displayed a significant CSD phenomenon.

Taking cold-formed thin-walled steel and straw board as the main materials， a new type of box-section straw-cold-formed thin-walled steel combined beam was constructed by using the connection method of straw board wrapped with cold-formed thin-walled steel. The finite element software ABAQUS was used to establish 7 groups of 25 combined beam models to analyze the effects of connection method， straw board thickness， steel thickness， steel height， web bolt rows， web bolt spacing and beam span on the flexural performance of the combined beams， and to explore the damage modes and damage mechanisms of the combined beams in the case of flexural damage. The results showed that the lower flange of the straw board reached the tensile strength before the upper flange in the finite element simulation loading process， which led to bending cracks and bending damage in the mid-span region of the combined beam， and the lower flange of the straw board underwent localized compression bending and conformal shear damage in the process of load increase. Each parameter had a different degree of influence on the flexural performance of the combined beams， among which the thickness of the straw board， the thickness of the steel and the height of the steel had the most significant influence on the flexural performance. The optimum combination parameters of the combined beams were as follows， with high bending load capacity and small deflection deformation， using adhesive connection， 48 mm thickness of straw board， 2.5 mm thickness of steel， and 200 mm height of steel. The web bolts were arranged in two rows with a spacing of 150 mm， and the overall span was 1800 mm， which was verified to be in line with the expectation by the test. The combination of cold-formed thin-walled steel and straw board can effectively utilize their respective advantages and significantly improve the flexural load capacity of the combined beam.

In the wood furniture market tends to be saturated at the moment， the competition between the supply chains of wood furniture manufacturing enterprises has become more and more prominent， and the optimization of competitive strategy has become a core proposition to determine the survival and development of enterprises. This paper takes the product life cycle theory as the basis， takes two wooden furniture manufacturing enterprise supply chains as the research object， takes the wooden furniture product price， log cost， online and offline publicity cost as the decision variables of the utility function， and introduces the fuzzy stochastic method to deal with the sensitivity coefficients in the function， and studies the wooden furniture manufacturing enterprise supply chain competition strategy in different life stages through Nash game and Stankelberg game method， combined with the omnichannel retail structure. The study shows that： the utility value is higher under the Stankelberg game， which is about 4% higher than that of the Nash game； the utility of the two wooden furniture manufacturing enterprises will decrease with the development of the life stage of the wooden furniture under different games； the wooden furniture manufacturing enterprises should dynamically adjust their competitive strategies according to the life cycle stage of the products， and give priority to the use of differentiated pricing and cost control strategies， especially in the introduction period and the development period.

Single-tree skeleton extraction is a critical step in 3D tree modeling， holding significant importance for precision forestry and forest resource management. Backpack LiDAR scanning （BLS）， as an emerging mobile measurement technology， offers advantages in flexibility and portability. However， its point cloud data suffers from uneven distribution and noise interference， which affect the accuracy of skeleton extraction. To address these issues， this study focuses on Cunninghamia lanceolata in the State-owned Gaofeng Forest Farm of Guangxi Zhuang Autonomous Region and proposes a hierarchical progressive skeleton extraction method based on key path detection using BLS data. This approach integrates geometric constraints and hierarchical analysis to achieve precise localization of branch axes， while employing perpendicular bisector intersection calculations to construct a continuous and topologically complete single-tree skeleton. Terrestrial laser scanning （TLS） data was used as validation data， and preprocessing techniques such as voxel filtering and local elevation normalization are applied to enhance BLS data quality. The results indicate that the proposed method exhibited high performance in branch classification. F ₁-scores ranged from 0.771 to 0.788， with precision ranging from 93.33% to 100%， and recall ranging from 66.67% to 90.63%. Furthermore， the comparative analysis of branch angle estimations based on BLS and TLS data yields a coefficient of determination （R²） of 0.84 and a root mean square error （R_MSE） of 7.22°. This study provides a high-precision technical framework for single-tree 3D modeling， laying a data foundation for forest resource management and ecological simulation.

Customized door and window materials are characterized by diverse orders and complicated specifications. However， existing methods （such as artificial experience algorithms and the maximax space strategy） mainly focus on the single goal of maximizing space utilization， with little consideration for framing convenience and overall efficiency. This paper proposes a framing palletizing strategy based on a hybrid particle swarm optimization （PSO） algorithm to enhance the overall efficiency of palletizing. Based on the processing characteristics before and after palletizing door/window materials， a framing palletizing strategy is proposed to solve the problem of difficult workpiece position tracking. The particle encoding includes both position attributes and palletizing attributes of the materials， strengthening the hierarchical clustering effect of materials belonging to the same door/window unit. A multi-objective collaborative optimization function is established， with the weighted comprehensive efficiency of space utilization and framing convenience as the optimization goal. The simulation based on the actual orders of enterprises shows that compared with the traditional artificial experience algorithm and the maximax space strategy， the comprehensive efficiency of the proposed algorithm is improved by 17.11% and 17.34%， respectively， the framing convenience is 97.93%， and the space utilization rate is 80.48%. The field experiment verifies the effectiveness of the algorithm in actual production. The research shows that the proposed strategy not only ensures high space utilization， but also greatly improves the framing convenience， so as to effectively improve the overall efficiency of the production line.

To address the low efficiency of manual water immersion and the susceptibility to damage of wooden spoon blanks in the molding process， this paper presents the design of an adaptive flexible gripper for grasping wooden spoon blank bundles. An optimized experimental design was developed using finite element analysis software. A regression prediction model for the gripper's strain energy was constructed using a Random Forest （RF） method optimized by a Bayesian optimization （BO） algorithm， termed BO-RF. Furthermore， the explainable machine learning method， SHAP （SHapley Additive exPlanations）， was employed to provide both global and local interpretability analysis of the model. Based on this predictive model， a genetic algorithm （GA） was utilized to perform an optimal design of the flexible gripper， with the conflicting objectives of maximizing the top surface strain energy and minimizing the overall strain energy. The multi-objective Pareto front was computed using the BO-RF model. Simulation results validate the effectiveness and feasibility of the proposed modeling and optimization methodology.

To meet the urgent need for high-frequency and automated measurement of tree diameter at breast height in forest ecological monitoring， a continuous measurement device for tree diameter at breast height based on Internet of Things technology is designed and implemented. This device took the ESP32 （a low-cost and low-power microcontroller integrating Wi-Fi and Bluetooth functions launched by Espressif Systems of China） as the core component， integrated high-precision displacement sensors， synchronous belt drive structures and wireless communication modules， and completed the real-time collection of data on the diameter changes of trees at breast height. It simultaneously possessed the efficient storage capacity for collected data as well as the stable remote transmission function. To verify the performance of the device， two different tree species （ash and larch） were selected and a one-year field experiment was carried out in the forest farm on the campus of Northeast Forestry University. The monitoring results showed that this system can accurately reflect the variation characteristics of the diameter at chest height of trees during the growth period and the dormant period. Its measurement error was controlled within 0.1% and was highly consistent with the manual measurement results （R²=0.91）. Furthermore， by combining the continuous diameter at breast height data with the measured tree height， a bivariate biomass model was used to calculate the annual carbon sink variation of individual trees， providing a reliable technical path for the dynamic estimation of individual tree carbon storage.

In order to improve the accuracy of forest pest identification， a forest pest detection model （GhostConv and SE attention enhanced YOLOv7， GS-YOLOv7） based on the improved YOLOv7 is proposed. Firstly， the model replaced the traditional convolution in the backbone network with GhostConv lightweight convolution to reduce the number of parameters in model operation and improve the model efficiency. Secondly， by adding the squeeze excitation （SE） attention module， the ability to extract the edges of pest images with insignificant features was enhanced， thereby further improving the feature extraction ability of the network. Thirdly， the content aware reassembly of features （CARAFE） lightweight operator was used to replace the traditional upsampling method to improve the quality of feature reconstruction， solve the scale mismatch problem， and enhance the detection performance. Finally， the coordinate convolution （CoordConv） module was introduced into the Neck network， and its position information was utilized to solve the problem of inaccurate target positioning and improve the model's sensitivity to spatial positions and its generalization ability. Experiments were conducted on six common pest and disease datasets， the precision of the GS-YOLOv7 model reached 93.15%， and the mean average precision at an intersection over union threshold of 0.5 reached 93.29%. Compared with the original model， the precision and mean average precision increased by 6.50% and 2.09%， respectively. The number of parameters and the model size decreased to 1.9×10⁷ units and 38.17 MB， representing a reduction of 51.4% and 46.53%， respectively， compared to the original model. Results indicate that the GS-YOLOv7 model demonstrates significant performance improvements over the original model， confirming the effectiveness of the model modifications.

This paper proposes a detection algorithm DDC-YOLO based on improved YOLOv10 to address the issues of occlusion interference and insufficient lighting in tree detection. Firstly， a dynamic convolutional mix block （DCMB） was designed to enhance the multi-scale feature fusion capability through adaptive dynamic convolution， solving the problem of singularity in traditional convolution kernels； Secondly， a dual backbone dynamic feature fusion network was proposed， combining the backbone structures of RT-DETR and YOLOv10， and utilizing the dynamic alignment fusion （DAF） module to adjust feature weights and enhance the model's adaptability to different features； Further introduced pyramid context feature extraction and spatial feature reconstruction techniques to optimize the neck network and achieve deep fusion of multi-level semantic information. The experiment was validated based on the self built dataset TreeImages （containing 7475 images）， and the results showed that the mAP50 of DDC-YOLO reached 46.7%， which was 5.0 percentage points higher than the original YOLOv10 model. The parameter size decreased from 2.27 M to 2.26 M （a decrease of 0.44%）， and the detection speed （FPS） increased from 202 to 254 （an increase of 25.4%）. The improved model exhibits higher robustness and real-time performance in complex scenarios， providing an efficient solution for forest resource surveys.

With increasing demand for ecological garden maintenance， pruning equipment faces challenges in efficiency， stability， and environmental performance. This study presents a high-efficiency pruning device driven by a unidirectional motor. By optimizing the transmission system and employing a customized lead screw-nut structure， the motor can complete both cutting and resetting with a single-direction rotation， avoiding the need for motor reversal in conventional devices. Finite element analysis verified the static and dynamic performance of the cutting components and transmission system， ensuring sufficient strength and stiffness for long-term operation. Calculations indicated an upper-bound required thrust of approximately 650 N （design baseline） for the motor-lead screw under the worst-case static condition. Under the specified dynamic simulation condition， the peak thrust was about 380 N， meeting the cutting and reset requirements with sufficient margin. Through a series of cutting experiments， the working performance of the equipment under different branch diameters was evaluated. Results demonstrated that the device outperforms traditional manual tools and standard electric pruners in efficiency and energy consumption， with strong continuous operation and energy-saving characteristics. The equipment is well-suited for ecological garden maintenance， meeting the high-efficiency requirements of ecological garden operations.

In order to study the influence of the structural parameters of the integrated blade for mowing and root removal and looseing on its operational performance， this paper establishes a three-dimensional discrete element simulation model based on discrete element method （EDEM） software to simulate the soil breaking effect of the blade in the process of soil grubbing operation. The influence of forward speed， rotation speed and blade inclination angle on soil breaking rate is analyzed through one-factor test， and Box-Behnken orthogonal test combined with response surface analysis is used to establish the prediction model of soil breaking rate and optimize the parameters. The results of orthogonal test show that the blade inclination angle is the main factor affecting the soil breaking rate， followed by the rotation speed， and the interaction of the three factors is also significant. Under the optimal combination of parameters， with a forward speed of 1.39 m/s， a rotational speed of 107 rad/s， and a blade inclination angle of 4.7°， the soil breaking rate reached 78.3%. The results of the study provide a theoretical basis and practical guidance for the structural design and parameter configuration of mountain mowing and root removal and loosening devices.

The apperance quality of Pu′er Dragon Ball tea plays a decisive role in its market value； however， conventional inspection approaches fail to simultaneously satisfy the demands of real-time efficiency， accuracy， and edge-level deployment. In response， we propose SHM-YOLO， a lightweight object detection framework. Extending YOLOv11， the model employs ShuffleNetV2 （denoted as S in SHM） as the backbone， integrating point wise group convolution with channel shuffling to minimize computational cost. Through the integration of a hierarchical scale feature pyramid network （HS-FPN， denoted as H in SHM） that combines channel attention with dimensional matching， the model strengthens the effectiveness of multi-scale feature fusion. At the same time， the multi-scale attention block （MAB， denoted as M in SHM） is utilized to optimize the C3K2 structure， enabling more effective image detail extraction. To improve bounding-box regression， the model combines Inner-IoU with SIoU loss， which expedites convergence and augments localization precision. Experimental validation on a self-developed dataset for Pu′er Dragon Ball tea appearance quality confirms that SHM-YOLO reaches 97.2% mAP@50， 92.7% precision （P）， 93.6% recall （R）， and 303 fps， with merely 0.969×10⁶parameters and 2.3 MB storage consumption. Compared to YOLOv11n， the model achieves higher accuracy while markedly decreasing floating-point computation （by 62.5%） and memory consumption （by 47.6%）， highlighting its excellent lightweight characteristics and strong suitability for industrial deployment.