Search Results (432)

Insect sex pheromone receptors (PRs) are crucial for regulating mating and reproduction. In the insect olfactory perception pathway, the pheromone-binding protein (PBP) facilitates the efficient translocation of sex pheromones, enabling them to bind to PRs. PRs convert chemical signals into electrical signals, which are transmitted to the insect central nervous system to ultimately regulate reproductive behaviors. Thus, conducting functional analysis of PRs not only clarifies the molecular mechanism underlying insect mating via sex pheromone recognition and reveals the intrinsic regulatory link between sex pheromone detection and mating behavior but also provides theoretical support for the scientific understanding of the insect olfactory system. Additionally, this research lays a core theoretical foundation for the development of green pest control technologies in agriculture and forestry. This paper systematically reviews the research methods, technical principles, and advantages and disadvantages of techniques used to study insect PR genes. It summarizes representative identified PRs and their corresponding research strategies, aiming to provide a reference for future investigations into insect chemical communication and for the advancement of pest control practices. Full article

Small pest detection in ultra-high-resolution forestry images is challenging due to extreme scale variation, complex backgrounds, and limited annotated data. To address these issues, we propose SSFPDet (Semi-Supervised Forest Pest Detector), a semi-supervised object detection framework designed for low-annotation settings. Built upon the Soft Teacher paradigm, SSFPDet integrates a YOLO-T-based overlapping slicing strategy, a Top-K pseudo-label selection mechanism, and a Kullback–Leibler (KL) divergence-based distribution alignment constraint. The slicing strategy enhances small-object representation without modifying the detector backbone, while the Top-K and KL modules improve pseudo-label reliability and semantic consistency during training. Under the 20% labeled setting, SSFPDet achieves an mAP@0.5:0.95 of 46.6, outperforming the baseline by 0.7 points. Notably, small-object detection performance (AP_S) improves by 6.6 percentage points. Ablation studies confirm the complementary contributions of spatial slicing and semantic alignment. Overall, SSFPDet provides a practical and scalable solution for high-resolution forestry pest monitoring under limited supervision. Full article

Forest pests and diseases are some of the major disturbances affecting the stability of forest ecosystems. Accurate identification of insect-infested trees is therefore crucial for assessing forest health and implementing precision forestry management. This study focuses on stand-level detection of cypress trees (Cupressus funebris Endl.) that were affected by the cypress bark beetle (Phloeosinus aubei Perris), and the framework enables individual tree segmentation, insect-infested tree detection, and stand infestation assessment. Firstly, individual trees were extracted from Light Detection and Ranging (LiDAR) point cloud data using the layer-stacking seed point algorithm. Based on the segmented tree crowns, four vegetation indices (Visible Atmospherically Resistant Index (VARI), Visible-band Difference Vegetation Index (VDVI), Red-Green Index (RGI), and Color Index of Vegetation Extraction (CIVE)) were calculated from Unmanned Aerial Vehicle (UAV) RGB imagery. Insect-infested cypress trees were extracted through threshold segmentation. Through visual interpretation, the optimal vegetation index was determined and the infestation rate at the stand level was calculated. Based on the above framework, a total of 1368 trees were identified in the cypress stand, with a segmentation Precision of 82.51%, a Recall of 80.00%, and an F1-score of 81.24%. RGI achieved the best performance (Precision = 100.00%, Recall = 86.96%, F1-score = 93.02%) and identified 20 infested trees, accounting for 1.46% of the cypress stand. Supplementary experiments further confirm the superiority of the RGI index and the μ ± 2σ thresholding method. These results demonstrate that the proposed method enables rapid detection of the infested cypress trees, effective monitoring of stand health and infestation severity, thereby supporting informed decision-making in pest control and forest management. Full article

Detecting rare ground-level obstacles (e.g., large boulders) in dense boreal forests from low-altitude UAV RGB imagery is challenging due to limited annotated data, strong background clutter, and expensive field labeling. This paper evaluates two complementary synthetic-data augmentation pipelines for low-data forestry object detection: segmentation-guided diffusion inpainting, where SegFormer-derived forest-floor masks constrain Stable Diffusion inpainting to plausible insertion regions, and simulator-based generation in Unreal Engine 5 with controlled domain randomization and automatic annotations. We conduct a ten-fold cross-validation study on a real UAV dataset of 64 images and report both accuracy and stability across folds. Compared to real-only training (mean mAP50 ≈ 0.579; mAP50-95 ≈ 0.350), inpainting improves mean performance (mAP50 ≈ 0.647; mAP50-95 ≈ 0.435) while substantially reducing cross-fold variance and lifting the worst-case fold from 0.301 to 0.619 in mAP50. Simulator augmentation yields slightly lower mean accuracy (mAP50 ≈ 0.546; mAP50-95 ≈ 0.344) but markedly improves robustness by mitigating collapse on difficult splits (minimum mAP50 0.496 vs. 0.301). These results indicate that carefully curated generative augmentation can reduce failure risk and improve generalization in extremely data-limited forestry detection settings without additional field data collection. Full article

Accurate identification of moso bamboo (Phyllostachys edulis) age classes is essential for effective forestry resource management, yet existing methods often struggle to achieve a satisfactory balance between accuracy and computational efficiency under complex field conditions. To address this challenge, this study proposes a lightweight object detection model, termed YOLO11-GCR, for fine-grained moso bamboo age-class classification based on close-range imagery. The proposed approach builds upon the YOLO11 framework and incorporates Ghost convolution, the Convolutional Block Attention Module (CBAM), and a Receptive Field Block (RFB) to reduce model complexity, enhance discriminative feature representation, and improve sensitivity to subtle texture variations among age classes. A dataset consisting of 9538 annotated bamboo culm images covering four age classes (I-du to IV-du) was constructed and divided into training, validation, and independent test sets with strict spatiotemporal separation. Experimental results indicate that YOLO11-GCR achieves robust detection performance with a lightweight architecture of 2.62 × 10⁶ parameters and 6.2 GFLOPs, yielding an mAP@0.5 of 0.913 and an mAP@0.5–0.95 of 0.895 on the independent test set. Notably, the model demonstrates improved classification stability for visually similar age classes, such as II-du and III-du. Overall, this study presents an efficient and practical imaging-based solution for automated moso bamboo age-class recognition in complex natural environments. Full article

Ungulate browsing is a major driver of forest regeneration dynamics and habitat structure in managed temperate forests, influencing species composition, regeneration success, and long-term stand development. Traditional assessments of browsing impacts often rely on field-based indicators such as regeneration density or visual cover, but these metrics provide limited insight into three-dimensional habitat structure. Mobile handheld LiDAR offers highly detailed measurements of forest structure, enabling objective and reproducible quantification of structural complexity that complements and extends conventional field-based methods. In this study, we applied handheld LiDAR as an innovative indicator for habitat structure within the ungulate browsing zone (<2 m height) to evaluate structural development across sites differing in management context. Paired fenced and unfenced plots (12 × 12 m) were surveyed within the WiWaldI project framework in 2019 and 2023 and compared across three hunting regimes representing different degrees of ungulate population management. Structural complexity was quantified by deriving box-counting dimensions from LiDAR point clouds, providing a measure of spatial arrangement and density relevant to ungulate–vegetation interactions. To support interpretation and ecological context, we complemented LiDAR indicators with streamlined field assessments. Based on this framework, we assessed whether forest structural complexity and visual cover differ among regions and over time, and whether ungulate browsing induces detectable structural differences between fenced whether structural differences between fenced and unfenced plots are detectable. We further examined the relative importance of tree species composition, plant architecture, and hunting regime as drivers of three-dimensional habitat structure. A simplified octant method characterized the spatial distribution of woody regeneration, while a silhouette-based approach quantified visual cover from the perspective of a standard ungulate profile. These auxiliary measures contextualize visual and spatial aspects of structure that LiDAR metrics capture with minimal observer bias. LiDAR studies have previously demonstrated potential for linking high-resolution structural data to ungulate habitat use, and our approach extends this by focusing on structural complexity as a habitat indicator. Results show a consistent increase in LiDAR-derived structural complexity between 2019 and 2023 across all regions. This increase occurred across management contexts and was not consistently explained by fencing or hunting regime effects, suggesting that site conditions, forest composition, and successional processes were dominant drivers during the observation period. Hunting regime showed no statistically significant and no consistent effect on structural complexity across regions or years. Visual cover metrics varied strongly among regions and species and declined over time. These findings suggest that three-dimensional habitat structure information has the potential to enhance the evaluation of ungulate impacts and may support evidence-based forest and wildlife management, particularly when interpreted in the context of site conditions and successional dynamics. Beyond ungulate impact assessment, the presented handheld LiDAR approach provides a scalable remote sensing framework for precision forestry by capturing three-dimensional structural attributes that are directly linked to forest stability, resilience, growth dynamics, and stand-level species mixing, thereby supporting evidence-based forest management recommendations. Full article

Monitoring soil contamination from petroleum products is vital for protecting human health and the environment. In forestry, hydraulic oil spills frequently result from leaks in equipment such as harvesters. This study evaluates a custom-built, inexpensive electronic nose, equipped with a Figaro TGS gas sensor array, for discriminating between pristine and contaminated soil samples. Two oil types and three pollution intensities were analyzed. The constructed electronic nose applied two sensor operation modes: (i) response to change of sensor operation condition from clean air to target odors and (ii) response to sensor heater temperature modulation. Classification was performed using Random Forest and Support Vector Machine (SVM) algorithms, and Linear Discriminant Analysis (LDA) was used to explore multidimensional data patterns. The sensor heater temperature modulation mode provided superior classification performance. Measurements at room temperature achieved an accuracy of 97%, clearly outperforming measurements on samples heated to 60 °C (75%). While the system successfully identified biodegradable oil contamination, standard mineral oil was more challenging to detect. Among the sensors tested, TGS 2602 was the most effective. These findings indicate that portable electronic noses can provide a statistically robust and cost-effective tool for assessing the severity of soil pollution. Full article

Species of the genus Phytophthora are among the most detrimental plant pathogens globally, representing a significant threat to global agriculture, horticulture, and forestry. These zoosporic oomycetes have historically caused devastating outbreaks, including, just to mention a few, late blight of potato in Ireland; jarrah dieback of eucalyptus in Western Australia; ink disease of chestnut in Europe; sudden oak death and sudden larch death of coast live oak and tanoak in the Western US, and of Japanese larch in the UK. The environmental and ecological impacts of the diseases they cause result in significant economic costs that often have social repercussions. With the acceleration of globalization, enhancing the movement of plant material, in particular with the global live plant trade, the spread of Phytophthora to new, uncontaminated territories has intensified. Nurseries play a key role in the movement of these pathogens, the trade of contaminated stocks representing their major dissemination route. However valuable, conventional detection techniques, including baiting and direct isolation, are too slow and labour-intensive to meet current diagnostic requirements, particularly given the huge volumes of plants traded globally. This problem becomes even more acute when large volumes of potentially infectious plant material need to be processed in a short time frame, as it is often necessary to provide accurate and timely responses to interested parties. Early and precise detection is thus vital to avert outbreaks and mitigate long-term consequences. This review evaluates and contrasts the efficacy of novel detection methods against traditional approaches, emphasizing their significance in managing the escalating threat posed by Phytophthora spp. worldwide. Despite technological advances, critical challenges remain that limit the reliability and large-scale adoption of new diagnostic methods. Research still needs to bridge the gap between the laboratory and the field in terms of accuracy, sensitivity and diagnostic costs. Recent innovations focus on sensor technology and point-of-care (POC) devices for faster, more sensitive, and low-cost specific detection of Phytophthora spp. in plant matrices, water and soil. Enhancing diagnostic capabilities through these tools is crucial for protecting agricultural productivity, local economies, and natural ecosystems. Full article

This study addresses the need for effective bamboo monitoring in smart forestry as UAV imagery and AI-based methods continue to advance. Bambusa stenostachya (thorny bamboo), commonly found in the badland regions of southern Taiwan, spreads rapidly due to its strong reproductive capacity and extensive rhizome system, often causing forestland degradation and challenges to sustainable management. An automated detection approach is therefore required to capture bamboo dynamics and support forest resource assessment. We use a dual-component framework for detecting bamboo forests and individual bamboo clumps from high-resolution UAV orthomosaic imagery. The first component performs semantic segmentation using U-Net or SegFormer to extract bamboo forest areas and generate a corresponding forest mask. The second component independently applies instance segmentation using YOLOv8-Seg and Mask R-CNN to delineate and localize individual bamboo clumps. The dataset was collected from Compartment 43 of the Qishan Working Circle in Kaohsiung, Taiwan. Experimental results show strong model performance: bamboo forest segmentation achieved an F1-score of 0.9569, while bamboo clump instance segmentation reached a precision of 0.8232. These findings demonstrate the promising potential of deep learning-based segmentation techniques for improving bamboo detection and supporting operational forest monitoring. Full article

Pine Wilt Disease (PWD) is a global destructive forest disease. It poses a serious threat to ecological security and forestry economy, and early detection of PWD is crucial for its prevention and control. Most current studies on identifying infected pine trees based on multispectral data only rely on Vegetation Indices (VIs). They fail to fully explore the role of Texture Features (TFs) in disease identification. Furthermore, existing models generally lack interpretability. To address these issues, this study proposes a machine learning classification framework integrating VIs and TFs. It also introduces the SHAP algorithm to clarify the contribution of key features to classification decisions. The results show that the method using fused VIs and TFs as input features performs significantly better than using single features. Among the four models evaluated, LGBM achieved the best performance (OA: 0.897, Macro-F1: 0.895), followed by LR (OA: 0.818, Macro-F1: 0.809), RF (OA: 0.790, Macro-F1: 0.786), and SVM (OA: 0.770, Macro-F1: 0.787) when using fused VIs-TFs. SHAP analysis further reveals that VIs such as Vegetation Atmospherically Resistant Index (VARI), Plant Senescence Reflectance Index (PSRI), Difference Vegetation Index (DVI), Anthocyanin Reflectance Index (ARI), and Normalized Difference Red Edge Index (NDRE), as well as TFs like NIR-Mean (NIR-M), play a dominant role in identifying disease stages. Among the VIs, VARI demonstrated the highest contribution, while NIR-M showed the most significant contribution among TFs. Specifically, VIs are more advantageous in distinguishing the pre-visual, early, middle, and late stages. In contrast, TFs contributed more to identifying healthy and dead trees. This study confirms that fusing VIs and TFs can effectively complement the physiological and structural information of pine canopies. Combined with the interpretable LGBM model, it provides a new technical path for the accurate monitoring of PWD. Full article

Endophyte fungi (EF) are considered a new and valuable reservoir of bioactive molecules of biotechnological interest for pharmacy, agricultural and forestry industries. In this study, thirty EFs, isolated from three Chilean Nothofagus species (N. alpina, N. dombeyi, N. oblicua) were identified and cultured in submerged liquid fermentations aimed at searching for natural active substances. The extracts obtained were evaluated against pathogenic bacteria and fungi. Sixteen extracts (53.3%) presented antibacterial and fourteen (46.6%) presented antifungal activities in different intensities. Extracts from isolates Coryneum sp.-72 and P. cinnamomea-78 exhibited the highest antimicrobial activity. Using bioautography, the compounds responsible for the antimicrobial activity exhibited by Coryneum sp.-72 and P. cinnamomea-78 were detected and characterized. Coryneum sp.-72 showed bactericidal properties at 200 μg/mL and bacteriostatic effects at 50 μg/mL against B. cereus, B. subtilis, L. monocytogenes and S. aureus. MIC values indicated that P. cinnamomea-78 exhibited a strong fungistatic and fungicidal effect against B. cinerea and C. gloesporioides at 10–50 μg/mL. Isolates were grouped in the following order: Botryosphaeriales, Diaporthales, Eurotiales, Helotiales, Hypocreales, Pleosporales, Magnaporthales, Sordariales and Polyporales. EF isolated, identified and evaluated constitute the first report for Chilean Nothofagus genus. Full article

The temperate Himalayan states of Jammu and Kashmir, Himachal Pradesh, Uttarakhand, Ladakh, Sikkim, and Arunachal Pradesh in India face unique agro-ecological challenges across agriculture and allied sectors, including pest and disease pressures, inefficient resource use, post-harvest losses, and fragmented supply chains. This review systematically examines 21 critical problem areas, with three key challenges identified per sector across agriculture, agricultural engineering, fisheries, forestry, horticulture, sericulture, and animal husbandry. Artificial Intelligence (AI) and Machine Learning (ML) interventions, including computer vision, predictive modeling, Internet of Things (IoT)-based monitoring, robotics, and blockchain-enabled traceability, are evaluated for their regional applicability, pilot-level outcomes, and operational limitations under temperate Himalayan conditions. The analysis highlights that AI-enabled solutions demonstrate strong potential for early pest and disease detection, improved resource-use efficiency, ecosystem monitoring, and market integration. However, large-scale adoption remains constrained by limited digital infrastructure, data scarcity, high capital costs, low digital literacy, and fragmented institutional frameworks. The novelty of this review lies in its cross-sectoral synthesis of AI/ML applications tailored to the Himalayan context, combined with a sector-wise revenue-loss assessment to quantify economic impacts and guide prioritization. Based on the identified gaps, the review proposes feasible, context-aware strategies, including lightweight edge-AI models, localized data platforms, capacity-building initiatives, and policy-aligned implementation pathways. Collectively, these recommendations aim to enhance sustainability, resilience, and livelihood security across agriculture and allied sectors in the temperate Himalayan region. Full article

Chinese fir [Cunninghamia lanceolata (Lamb.) Hook.] is a fast-growing species widely utilized in construction, industrial raw materials. Owing to its broad application scope, research on Chinese fir is fragmented across multiple disciplines, making it difficult to grasp the overall research context and trends. Following the PRISMA guidelines, we retrieved articles related to Chinese fir published between 1942 and 2024 from Chinese databases (i.e., CNKI, Wanfang Data, and VIP Chinese Journal Database) and the Web of Science Core Collection (WOSCC). After removing duplicate and irrelevant records, a total of 7174 valid records were retained, including 5862 from Chinese databases and 1312 from WOSCC. The PRISMA-screened literature was imported into CiteSpace V.6.2.R4 for bibliometric analysis. Through keyword clustering, burst detection, and timeline mapping, we focused on analyzing the domestic resource distribution, research hotspots, and evolutionary trends of Chinese fir research. The results showed that research publications on Chinese fir have increased year by year, and international research started earlier and is more in-depth, while Chinese research covers a wider scope. Both follow two stages (germination and growth). Chinese research focuses on basic application areas such as seedling cultivation and plantation management; international research emphasizes ecological functions and biomass development. Global research exhibits convergence in the field of eco-environmental interactions; specifically, both domestic and international studies investigate the impacts of climate change (e.g., drought and global warming) and nitrogen deposition on the growth and functional evolution of Chinese fir. This study provides references for researchers, forestry policymakers, and planters. Full article

Over the past two decades, Light Detection and Ranging (LiDAR) technology has evolved from early National Aeronautics and Space Administration (NASA)-led airborne laser altimetry into commercially mature systems that now underpin vegetation remote sensing across scales. Continuous advancements in laser engineering, signal processing, and complementary technologies—such as Inertial Measurement Units (IMU) and Global Navigation Satellite Systems (GNSS)—have yielded compact, cost-effective, and highly sophisticated LiDAR sensors. Concurrently, innovations in carrier platforms, including uncrewed aerial systems (UAS), mobile laser scanning (MLS), Simultaneous Localization and Mapping (SLAM) frameworks, have expanded LiDAR’s observational capacity from plot- to global-scale applications in forestry, precision agriculture, ecological monitoring, Above Ground Biomass (AGB) modeling, and wildfire science. This review synthesizes LiDAR’s cross-domain capabilities for the following: (a) quantifying vegetation structure, function, and compositional dynamics; (b) recent sensor developments encompassing ALS discrete-return ${(ALS}_D)$, and ALS full-waveform $\left({ALS}_{FW}\right)$, photon-counting LiDAR (PCL), emerging multispectral LiDAR (MSL), and hyperspectral LiDAR (HSL) systems; and (c) state-of-the-art data processing and fusion workflows integrating optical and radar datasets. The synthesis demonstrates that many LiDAR-derived vegetation metrics are inherently transferable across domains when interpreted within a unified structural framework. The review further highlights the growing role of artificial-intelligence (AI)-driven approaches for segmentation, classification, and multitemporal analysis, enabling scalable assessments of vegetation dynamics at unprecedented spatial and temporal extents. By consolidating historical developments, current methodological advances, and emerging research directions, this review establishes a comprehensive state-of-the-art perspective on LiDAR’s transformative role and future potential in monitoring and modeling Earth’s vegetated ecosystems. Full article

In the context of green transition and digital transformation, forestry is becoming a strategic area of application of current modern technologies. The Internet of Things (IoT), artificial intelligence (AI), big data analysis (Big Data) and Digital Twins define the basic infrastructure of smart forestry. By connecting sensors, drones and satellites, IoT allows for continuous monitoring of forest ecosystems, risk anticipation and decision optimization in real-time. The purpose of this study is to perform a comprehensive narrative analysis of the relevant scientific literature from the recent period (2020–2025) regarding the application of IoT in forestry, highlighting the conceptual, technological and institutional developments. Based on a selection of Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) (29 full-text articles), four major axes are analyzed: (A) forest fire detection and prevention; (B) climate-smart forestry and carbon accounting; (C) forest digitalization through the concepts of Forest 4.0, Forest 5.0 and Digital Twins; (D) sustainability and digital forest policies. The results show that IoT is a catalyst for the sustainable transformation of the forest sector, supporting carbon accounting, climate-risk reduction and data-driven governance. The analysis highlights four major developments: the consolidation of IoT–AI architectures, the integration of IoT and remote sensing, the emergence of Forest 4.0/5.0 and Digital Twins and the growing role of governance and data standards. These findings align with the objectives of the EU Forest Strategy 2030 and the European Green Deal. Full article