Hasil untuk "Forestry"

Marc-Philip Ecker, Christoph Fröhlich, Johannes Huemer et al.

Forestry cranes operate in dynamic, unstructured outdoor environments where simultaneous collision avoidance and payload sway control are critical for safe navigation. Existing approaches address these challenges separately, either focusing on sway damping with predefined collision-free paths or performing collision avoidance only at the global planning level. We present the first collision-free, sway-damping model predictive controller (MPC) for a forestry crane that unifies both objectives in a single control framework. Our approach integrates LiDAR-based environment mapping directly into the MPC using online Euclidean distance fields (EDF), enabling real-time environmental adaptation. The controller simultaneously enforces collision constraints while damping payload sway, allowing it to (i) replan upon quasi-static environmental changes, (ii) maintain collision-free operation under disturbances, and (iii) provide safe stopping when no bypass exists. Experimental validation on a real forestry crane demonstrates effective sway damping and successful obstacle avoidance. A video can be found at https://youtu.be/tEXDoeLLTxA.

Pankaj Deoli, Karthik Ranganath, Karsten Berns

RGB-NIR image registration plays an important role in sensor-fusion, image enhancement and off-road autonomy. In this work, we evaluate both classical and Deep Learning (DL) based image registration techniques to access their suitability for off-road forestry applications. NeMAR, trained under 6 different configurations, demonstrates partial success however, its GAN loss instability suggests challenges in preserving geometric consistency. MURF, when tested on off-road forestry data shows promising large scale feature alignment during shared information extraction but struggles with fine details in dense vegetation. Even though this is just a preliminary evaluation, our study necessitates further refinements for robust, multi-scale registration for off-road forest applications.

Yida Lin, Bing Xue, Mengjie Zhang et al.

Dense ground-truth disparity maps are practically unobtainable in forestry environments, where thin overlapping branches and complex canopy geometry defeat conventional depth sensors -- a critical bottleneck for training supervised stereo matching networks for autonomous UAV-based pruning. We present UE5-Forest, a photorealistic synthetic stereo dataset built entirely in Unreal Engine 5 (UE5). One hundred and fifteen photogrammetry-scanned trees from the Quixel Megascans library are placed in virtual scenes and captured by a simulated stereo rig whose intrinsics -- 63 mm baseline, 2.8 mm focal length, 3.84 mm sensor width -- replicate the ZED Mini camera mounted on our drone. Orbiting each tree at up to 2 m across three elevation bands (horizontal, +45 degrees, -45 degrees) yields 5,520 rectified 1920 x 1080 stereo pairs with pixel-perfect disparity labels. We provide a statistical characterisation of the dataset -- covering disparity distributions, scene diversity, and visual fidelity -- and a qualitative comparison with real-world Canterbury Tree Branches imagery that confirms the photorealistic quality and geometric plausibility of the rendered data. The dataset will be publicly released to provide the community with a ready-to-use benchmark and training resource for stereo-based forestry depth estimation.

Minh Nhat Vu, Alexander Wachter, Gerald Ebmer et al.

Forestry machines operated in forest production environments face challenges when performing manipulation tasks, especially regarding the complicated dynamics of underactuated crane systems and the heavy weight of logs to be grasped. This study investigates the feasibility of using reinforcement learning for forestry crane manipulators in grasping and lifting heavy wood logs autonomously. We first build a simulator using Mujoco physics engine to create realistic scenarios, including modeling a forestry crane with 8 degrees of freedom from CAD data and wood logs of different sizes. We further implement a velocity controller for autonomous log grasping with deep reinforcement learning using a curriculum strategy. Utilizing our new simulator, the proposed control strategy exhibits a success rate of 96% when grasping logs of different diameters and under random initial configurations of the forestry crane. In addition, reward functions and reinforcement learning baselines are implemented to provide an open-source benchmark for the community in large-scale manipulation tasks. A video with several demonstrations can be seen at https://www.acin.tuwien.ac.at/en/d18a/

Andrew Crawley, Adam Daigneault, Jonathan Gendron

From 2000 to 2017, 64% of Maine's pulp and paper processing mills shut down; these closures resulted in harmful effects to communities in Maine and beyond. One question this research asks is how will key macroeconomic and related variables for Maine's forestry and logging industry change in the future? To answer this, we forecast key macroeconomic and related variables with a vector error correction (VEC model) to assess past and predict future economic contributions from Maine's forestry and logging industry. The forecasting results imply that although the contribution of the industry in Maine would likely remain stable due to level prices and a slight increase in output, local Maine communities could be worse off due to decreases in employment and firms. We then incorporated these forecasts into a 3-stage modeling process to analyze how a negative shock to exchange rates from an increase in tariffs could affect Maine's employment and output. Our results suggest that increased tariffs will reduce output and increase employment volatility in Maine. Rising uncertainty and costs of business operations suggest care should be taken when changing tariffs and trade restrictions, especially when changes to business operations can harm markets and communities.

Yida Lin, Bing Xue, Mengjie Zhang et al.

Autonomous UAV forestry operations require robust depth estimation methods with strong cross-domain generalization. However, existing evaluations focus on urban and indoor scenarios, leaving a critical gap for specialized vegetation-dense environments. We present the first systematic zero-shot evaluation of eight state-of-the-art stereo methods--RAFT-Stereo, IGEV, IGEV++, BridgeDepth, StereoAnywhere, DEFOM (plus baseline methods ACVNet, PSMNet, TCstereo)--spanning iterative refinement, foundation model, and zero-shot adaptation paradigms. All methods are trained exclusively on Scene Flow and evaluated without fine-tuning on four standard benchmarks (ETH3D, KITTI 2012/2015, Middlebury) plus a novel 5,313-pair Canterbury forestry dataset captured with ZED Mini camera (1920x1080). Performance reveals scene-dependent patterns: foundation models excel on structured scenes (BridgeDepth: 0.23 px on ETH3D, 0.83-1.07 px on KITTI; DEFOM: 0.35-4.65 px across benchmarks), while iterative methods maintain cross-domain robustness (IGEV++: 0.36-6.77 px; IGEV: 0.33-21.91 px). Critical finding: RAFT-Stereo exhibits catastrophic ETH3D failure (26.23 px EPE, 98 percent error rate) due to negative disparity predictions, while performing normally on KITTI (0.90-1.11 px). Qualitative evaluation on Canterbury forestry dataset identifies DEFOM as the optimal gold-standard baseline for vegetation depth estimation, exhibiting superior depth smoothness, occlusion handling, and cross-domain consistency compared to IGEV++, despite IGEV++'s finer detail preservation.

Yida Lin, Bing Xue, Mengjie Zhang et al.

UAV-based autonomous forestry operations require rapid and precise tree branch segmentation for safe navigation and automated pruning across varying pixel resolutions and operational conditions. We evaluate different deep learning methods at three resolutions (256x256, 512x512, 1024x1024) using the Urban Street Tree Dataset, employing standard metrics (IoU, Dice) and specialized measures including Thin Structure IoU (TS-IoU) and Connectivity Preservation Rate (CPR). Among 22 configurations tested, U-Net with MiT-B4 backbone achieves strong performance at 256x256. At 512x512, MiT-B4 leads in IoU, Dice, TS-IoU, and Boundary-F1. At 1024x1024, U-Net+MiT-B3 shows the best validation performance for IoU/Dice and precision, while U-Net++ excels in boundary quality. PSPNet provides the most efficient option (2.36/9.43/37.74 GFLOPs) with 25.7/19.6/11.8 percentage point IoU reductions compared to top performers at respective resolutions. These results establish multi-resolution benchmarks for accuracy-efficiency trade-offs in embedded forestry systems. Implementation is available at https://github.com/BennyLinntu/PerformanceTreeBranchSegmentation.

Marc-Philip Ecker, Bernhard Bischof, Minh Nhat Vu et al.

Collision-free motion planning in complex outdoor environments relies heavily on perceiving the surroundings through exteroceptive sensors. A widely used approach represents the environment as a voxelized Euclidean distance field, where robots are typically approximated by spheres. However, for large-scale manipulators such as forestry cranes, which feature long and slender links, this conventional spherical approximation becomes inefficient and inaccurate. This work presents a novel collision detection algorithm specifically designed to exploit the elongated structure of such manipulators, significantly enhancing the computational efficiency of motion planning algorithms. Unlike traditional sphere decomposition methods, our approach not only improves computational efficiency but also naturally eliminates the need to fine-tune the approximation accuracy as an additional parameter. We validate the algorithm's effectiveness using real-world LiDAR data from a forestry crane application, as well as simulated environment data.

Aoduo Li, Peikai Lin, Jiancheng Li et al.

Detecting agricultural pests in complex forestry environments using remote sensing imagery is fundamental for ecological preservation, yet it is severely hampered by practical challenges. Targets are often minuscule, heavily occluded, and visually similar to the cluttered background, causing conventional object detection models to falter due to the loss of fine-grained features and an inability to handle extreme data imbalance. To overcome these obstacles, this paper introduces Forestpest-YOLO, a detection framework meticulously optimized for the nuances of forestry remote sensing. Building upon the YOLOv8 architecture, our framework introduces a synergistic trio of innovations. We first integrate a lossless downsampling module, SPD-Conv, to ensure that critical high-resolution details of small targets are preserved throughout the network. This is complemented by a novel cross-stage feature fusion block, CSPOK, which dynamically enhances multi-scale feature representation while suppressing background noise. Finally, we employ VarifocalLoss to refine the training objective, compelling the model to focus on high-quality and hard-to-classify samples. Extensive experiments on our challenging, self-constructed ForestPest dataset demonstrate that Forestpest-YOLO achieves state-of-the-art performance, showing marked improvements in detecting small, occluded pests and significantly outperforming established baseline models.

Minh Nhat Vu, Gerald Ebmer, Alexander Watcher et al.

Autonomous large-scale machine operations require fast, efficient, and collision-free motion planning while addressing unique challenges such as hydraulic actuation limits and underactuated joint dynamics. This paper presents a novel two-step motion planning framework designed for an underactuated forestry crane. The first step employs GPU-accelerated stochastic optimization to rapidly compute a globally shortest collision-free path. The second step refines this path into a dynamically feasible trajectory using a trajectory optimizer that ensures compliance with system dynamics and actuation constraints. The proposed approach is benchmarked against conventional techniques, including RRT-based methods and purely optimization-based approaches. Simulation results demonstrate substantial improvements in computation speed and motion feasibility, making this method highly suitable for complex crane systems.

Ankush Agarwal, Christian Ewald, Yihan Zou

We determine forest lease value and optimal harvesting strategies under model parameter uncertainty within stochastic bio-economic models that account for catastrophe risk. Catastrophic events are modeled as a Poisson point process, with a two-factor stochastic convenience yield model capturing the lumber spot price dynamics. Using lumber futures and US wildfire data, we estimate model parameters through a Kalman filter and maximum likelihood estimation and define the model parameter uncertainty set as the 95% confidence region. We numerically determine the forest lease value under catastrophe risk and parameter uncertainty using reflected backward stochastic differential equations (RBSDEs) and establish conservative and optimistic bounds for lease values and optimal stopping boundaries for harvesting, facilitating Monte Carlo simulations. Numerical experiments further explore how parameter uncertainty, catastrophe intensity, and carbon sequestration impact the lease valuation and harvesting decision. In particular, we explore the costs arising from this form of uncertainty in the form of a reduction of the lease value. These are implicit costs that can be attributed to climate risk and will be emphasized through the importance of forestry resources in the energy transition process. We conclude that in the presence of parameter uncertainty, it is better to lean toward a conservative strategy reflecting, to some extent, the worst case than being overly optimistic. Our results also highlight the critical role of convenience yield in determining optimal harvesting strategies.

Fitri Rahmafitria, Heri Puspito Diyah Setiyorini, Purna Hindayani et al.

This study explores how destination attributes, such as accessibility, natural attractions, facilities, and disaster knowledge, influence tourists' risk perceptions, ultimately shaping their travel preparedness. Data were collected through questionnaires distributed to 806 tourists visiting a tsunami-prone beach destination in Indonesia. Partial Least Squares Structural Equation Modeling (PLS-SEM) was implemented in the analysis. The findings indicate that accessibility and well-developed tourist facilities tend to lower tourists’ perceived risk, while disaster knowledge heightens it, leading to improved preparedness. Tourists generally feel safer when destinations offer accessible amenities and infrastructure, yet this sense of security may inadvertently decrease their readiness for disasters. This situation creates a paradox: While enhanced accessibility and high-quality amenities contribute to visitor satisfaction, they can unintentionally lower risk perception and preparedness levels. The study challenges the conventional view that accessibility and amenities are inherently beneficial, highlighting the importance of balancing these attributes with proactive risk management strategies. Destination providers, destination management organizations (DMOs), and governments should enhance tourists’ disaster awareness through well-crafted guidelines, educational campaigns, and community engagement programs; these efforts help equip tourists with the necessary knowledge to respond effectively in emergency situations. At the same time, they contribute to the development of safer and more enjoyable tourist destinations.  

Mazen Mohamad, Ramana Reddy Avula, Peter Folkesson et al.

The increased importance of cybersecurity in autonomous machinery is becoming evident in the forestry domain. Forestry worksites are becoming more complex with the involvement of multiple systems and system of systems. Hence, there is a need to investigate how to address cybersecurity challenges for autonomous systems of systems in the forestry domain. Using a literature review and adapting standards from similar domains, as well as collaborative sessions with domain experts, we identify challenges towards CE-certified autonomous forestry machines focusing on cybersecurity and safety. Furthermore, we discuss the relationship between safety and cybersecurity risk assessment and their relation to AI, highlighting the need for a holistic methodology for their assurance.

Jingyun Sun, Zhongze Luo

A policy knowledge graph can provide decision support for tasks such as project compliance, policy analysis, and intelligent question answering, and can also serve as an external knowledge base to assist the reasoning process of related large language models. Although there have been many related works on knowledge graphs, there is currently a lack of research on the construction methods of policy knowledge graphs. This paper, focusing on the forestry field, designs a complete policy knowledge graph construction framework, including: firstly, proposing a fine-grained forestry policy domain ontology; then, proposing an unsupervised policy information extraction method, and finally, constructing a complete forestry policy knowledge graph. The experimental results show that the proposed ontology has good expressiveness and extensibility, and the policy information extraction method proposed in this paper achieves better results than other unsupervised methods. Furthermore, by analyzing the application of the knowledge graph in the retrieval-augmented-generation task of the large language models, the practical application value of the knowledge graph in the era of large language models is confirmed. The knowledge graph resource will be released on an open-source platform and can serve as the basic knowledge base for forestry policy-related intelligent systems. It can also be used for academic research. In addition, this study can provide reference and guidance for the construction of policy knowledge graphs in other fields. Our data is provided on Github https://github.com/luozhongze/ForPKG.

Lars Nieradzik, Henrike Stephani, Jördis Sieburg-Rockel et al.

In this study, we explore the explainability of neural networks in agriculture and forestry, specifically in fertilizer treatment classification and wood identification. The opaque nature of these models, often considered 'black boxes', is addressed through an extensive evaluation of state-of-the-art Attribution Maps (AMs), also known as class activation maps (CAMs) or saliency maps. Our comprehensive qualitative and quantitative analysis of these AMs uncovers critical practical limitations. Findings reveal that AMs frequently fail to consistently highlight crucial features and often misalign with the features considered important by domain experts. These discrepancies raise substantial questions about the utility of AMs in understanding the decision-making process of neural networks. Our study provides critical insights into the trustworthiness and practicality of AMs within the agriculture and forestry sectors, thus facilitating a better understanding of neural networks in these application areas.

Limin Chuan, Jingjuan Zhao, Shijie Qi et al.

From the perspective of project and paper datasets, research frontier recognition in the field of agricultural resources and the environment using the Latent Dirichlet Allocation (LDA) topic extraction model was studied. By combining the wisdom of domain experts to judge the similarities and differences of clustering topics between the two data sources, multidimensional indicators, such as the emerging degree, attention degree, innovation degree, and intersection degree, were comprehensively constructed for frontier identification. The methods for hot research frontiers, emerging research frontiers, extinction research frontiers, and potential research frontiers were proposed. The empirical research in the field of agricultural resources and the environment showed that the “interaction mechanism of plant–rhizosphere–microbial diversity” was a hot research frontier in the years 2016–2021. The themes of “wastewater treatment technology and efficient utilization of water resources”, the “value-added utilization of agricultural wastes and sustainable development”, the “soil ecological response mechanism under agronomic management measures”, and the “mechanism of soil landslide, erosion, degradation and prediction evaluation” were judged as potential research frontiers. The theme of “ecosystems management and pollution control of agricultural and animal husbandry” was recognized as an emerging research frontier. The results confirm that the fusion method of extracting topics from project and paper data, combined with expert intelligence and frontier indicators for fine classification of frontiers, is an optional approach. This study provides strong support for accurately identifying the forefront of scientific research, grasping the latest research progress, efficiently allocating scientific and technological resources, and promoting technological innovation.

Debasmita Pal, Arun Ross

Plant phenology and phenotype prediction using remote sensing data are increasingly gaining attention within the plant science community as a promising approach to enhance agricultural productivity. This work focuses on generating synthetic forestry images that satisfy certain phenotypic attributes, viz. canopy greenness. We harness a Generative Adversarial Network (GAN) to synthesize biologically plausible and phenotypically stable forestry images conditioned on the greenness of vegetation (a continuous attribute) over a specific region of interest, describing a particular vegetation type in a mixed forest. The training data is based on the automated digital camera imagery provided by the National Ecological Observatory Network (NEON) and processed by the PhenoCam Network. Our method helps render the appearance of forest sites specific to a greenness value. The synthetic images are subsequently utilized to predict another phenotypic attribute, viz., redness of plants. The quality of the synthetic images is assessed using the Structural SIMilarity (SSIM) index and Fréchet Inception Distance (FID). Further, the greenness and redness indices of the synthetic images are compared against those of the original images using Root Mean Squared Percentage Error (RMSPE) to evaluate their accuracy and integrity. The generalizability and scalability of our proposed GAN model are established by effectively transforming it to generate synthetic images for other forest sites and vegetation types. From a broader perspective, this approach could be leveraged to visualize forestry based on different phenotypic attributes in the context of various environmental parameters.

Silvia Melissa Manrique-Veja, Oscar Alvarado-Sanabria

Abstract This study aims at assessing the impact of compost application on the physical (porosity, volumetric-moisture and bulk density) and the chemical traits of soil (pH, organic carbon, electrical conductivity, cation exchange capacity and soil nutrients) on the leaf nutrient concentration and growth (height, diameter, new leaf-structures and chlorophyll content) of five native species used in urban forestry. Using a two-way factorial design, we evaluated three substrates: i) Soil (ii) Soil-compost mixture SC-12.5 (12.5 % compost (v/v)) (iii) Soil-compost mixture SC-25 (25 % compost (v/v)) and five species: Retrophyllum rospigliosii, Inga edulis, Citharexylum montanum, Caesalpinia spinosa, and Citharexylum sulcatum. We found that SC-25 and SC-12.5 increased the electric conductivity, cation exchange capacity, organic carbon, and soil base saturation. Moreover, compost addition increased the growth of the five native species evaluated. Such results suggest that compost-application is a viable option to improve soil fertility and promote the growth of native trees.

Wei Zhou, Guilin Zeng, Haotian Jin et al.

In terms of new-generation energy-storing devices, aqueous zinc-ion batteries (AZIBs) are becoming the prime candidates because of their inexpensive nature, inherent safety, environmental benignity and abundant resources. Nevertheless, due to a restrained selection of cathodes, AZIBs often perform unsatisfactorily under long-life cycling and high-rate conditions. Consequently, we propose a facile evaporation-induced self-assembly technique for preparing V₂O₃@carbonized dictyophora (V₂O₃@CD) composites, utilizing economical and easily available biomass dictyophora as carbon sources and NH₄VO₃ as metal sources. When assembled in AZIBs, the V₂O₃@CD exhibits a high initial discharge capacity of 281.9 mAh g⁻¹ at 50 mA g⁻¹. The discharge capacity is still up to 151.9 mAh g⁻¹ after 1000 cycles at 1 A g⁻¹, showing excellent long-cycle durability. The extraordinary high electrochemical effectiveness of V₂O₃@CD could be mainly attributed to the formation of porous carbonized dictyophora frame. The formed porous carbon skeleton can ensure efficient electron transport and prevent V₂O₃ from losing electrical contact due to volume changes caused by Zn²⁺ intercalation/deintercalation. The strategy of metal-oxide-filled carbonized biomass material may provide insights into developing high-performance AZIBs and other potential energy storage devices, with a wide application range.

Yuehua Yang, Fangkui Wang, Jialin Jiang et al.

Soil drench treatment using <i>Paenibacillus polymyxa</i> strain KN-03 was applied to citrus plants infected with <i>Candidatus</i> Liberibacter asiaticus (<i>C</i>Las). The infection status was assessed using PCR and a real-time quantitative PCR detection system (qPCR). The application of KN-03 resulted in a notable reduction in <i>C</i>Las levels in citrus plants. Specifically, by the 257th day post treatment commencement, following 24 KN-03 applications, the negative rates of <i>C</i>Las in the vein, root tip, and shoot tip were 50%, 0%, and 50%, respectively. After 24 cycles, KN-03 application significantly enhanced plant growth and stimulated reactive oxygen production in citrus leaves compared to control plants. Transcriptome analysis identified specific upregulated pathways. Furthermore, flora analysis revealed an increased abundance of microorganisms possessing potential utilization value, including <i>Burkholderia-Caballeronia-Paraburkholderia</i>, <i>uncultured_bacterium_o_Acidobacteriales</i>, <i>uncultured_bacterium_f_Gemmatimonadaceae</i>, and <i>Rhodanobacter</i>, in the root zone. Moreover, the BugBase analysis indicated that KN-03 treatment increased the abundance of beneficial rhizosphere bacteria associated with biofilm formation, element mobilization, and stress tolerance. These findings support the utility of <i>Paenibacillus polymyxa</i> KN-03 as an effective plant-growth-promoting bacterium for <i>C</i>Las management, with additional benefits for plant growth and soil health, specifically offering detoxification resources for shoot tip grafting.