Estimation of Damaged Regions by the Bark Beetle in a Mexican Forest Using UAV Images and Deep Learning DOI Open Access
Gildardo Godinez-Garrido, Juan-Carlos González-Islas, Angelina González-Rosas

et al.

Sustainability, Journal Year: 2024, Volume and Issue: 16(23), P. 10731 - 10731

Published: Dec. 6, 2024

Sustainable forestry for the management of forest resources is more important today than ever before because keeping forests healthy has an impact on human health. Recent advances in Unmanned Aerial Vehicles (UAVs), computer vision, and Deep Learning (DL) models make remote sensing Forest Insect Pest Disease (FIPD) possible. In this work, a UAV-based process, framework are used to automatically efficiently detect map areas damaged by bark beetles Mexican located Hidalgo State. First, image dataset region interest (ROI) acquired UAV open hardware platform. To determine trees, we use tree crown detection prebuilt Deepforest model, trees diseased pests recognized using YOLOv5. area region, propose method based morphological operations. The system generates comprehensive report detailing location affected zones, total regions, GPS co-ordinates, both locations. overall accuracy rates were 88% 90%, respectively. results obtained from 8.2743 ha revealed that 16.8% surface was and, 455 evaluated, 34.95% damaged. These findings provide evidence fast reliable tool early evaluation beetle impact, which could be expanded other insect species.

Language: Английский

A Novel Fusion Perception Algorithm of Tree Branch/Trunk and Apple for Harvesting Robot Based on Improved YOLOv8s DOI Creative Commons
Bin Yan, Yang Liu,

Wenhui Yan

et al.

Agronomy, Journal Year: 2024, Volume and Issue: 14(9), P. 1895 - 1895

Published: Aug. 24, 2024

Aiming to accurately identify apple targets and achieve segmentation the extraction of branch trunk areas trees, providing visual guidance for a picking robot actively adjust its posture avoid trunks obstacle avoidance fruit picking, spindle-shaped which are widely planted in standard modern orchards, were focused on, an algorithm tree detection robots was proposed based on improved YOLOv8s model design. Firstly, image data trees orchards collected, annotations object pixel-level conducted data. Training set then augmented improve generalization performance algorithm. Secondly, original network architecture’s design by embedding SE module attention mechanism after C2f Backbone architecture. Finally, dynamic snake convolution embedded into Neck structure architecture better extract feature information different branches. The experimental results showed that can effectively recognize images segment branches trunks. For recognition, precision 99.6%, recall 96.8%, mAP value 98.3%. 81.6%. compared with YOLOv8s, YOLOv8n, YOLOv5s algorithms recognition test images. other three algorithms, increased 1.5%, 2.3%, 6%, respectively. 3.7%, 15.4%, 24.4%, fruits, branches, is great significance ensuring success rate harvesting, provide technical support development intelligent harvesting robot.

Language: Английский

Citations

4
Forestry Segmentation Using Depth Information: A Method for Cost Saving, Preservation, and Accuracy DOI Open Access
Krzysztof Wołk, Jacek Niklewski, Marek S. Tatara

et al.

Forests, Journal Year: 2025, Volume and Issue: 16(3), P. 431 - 431

Published: Feb. 27, 2025

Forests are critical ecosystems, supporting biodiversity, economic resources, and climate regulation. The traditional techniques applied in forestry segmentation based on RGB photos struggle challenging circumstances, such as fluctuating lighting, occlusions, densely overlapping structures, which results imprecise tree detection categorization. Despite their effectiveness, semantic models have trouble recognizing trees apart from background objects cluttered surroundings. In order to overcome these restrictions, this study advances management by integrating depth information into the YOLOv8 model using FinnForest dataset. Results show significant improvements accuracy, particularly for spruce trees, where mAP50 increased 0.778 0.848 mAP50-95 0.472 0.523. These findings demonstrate potential of depth-enhanced limitations RGB-based segmentation, complex forest environments with structures. Depth-enhanced enables precise mapping species, health, spatial arrangements, habitat analysis, wildfire risk assessment, sustainable resource management. By addressing challenges size, distance, lighting variations, approach supports accurate monitoring, improved conservation, automated decision-making forestry. This research highlights transformative integration models, laying a foundation broader applications environmental conservation. Future studies could expand dataset diversity, explore alternative technologies like LiDAR, benchmark against other architectures enhance performance adaptability further.

Language: Английский

Citations

0
Refined Classification of Mountainous Vegetation Based on Multi-Source and Multi-Temporal High-Resolution Images DOI Open Access
Dan Chen, Xianyun Fei, Jing Li

et al.

Forests, Journal Year: 2025, Volume and Issue: 16(4), P. 707 - 707

Published: April 21, 2025

Distinguishing vegetation types from satellite images has long been a goal of remote sensing, and the combination multi-source multi-temporal sensing for classification is currently hot topic in field. In species-rich mountainous environments, this study selected four different seasons (two aerial images, one WorldView-2 image, UAV image) proposed method integrating hierarchical extraction object-oriented approaches 11 types. This innovatively combines Random Forest algorithm with decision tree model, constructing strategy based on feature combinations to progressively address challenge distinguishing similar spectral characteristics. Compared traditional single-temporal methods, our approach significantly enhances accuracy through fusion comparative experimental validation, offering novel technical framework fine-grained under complex land cover conditions. To validate effectiveness features, we additionally performed classifications individual images. The results indicate that (1) classification, best performance was achieved autumn reaching an overall 72.36%, while spring had worst performance, only 58.79%; (2) features reached 89.10%, which improvement 16.74% compared (autumn). Notably, producer species such as Quercus acutissima Carr., Tea plantations, Camellia sinensis (L.) Kuntze, Pinus taeda L., Phyllostachys spectabilis C.D.Chu et C.S.Chao, thunbergii Parl., Castanea mollissima Blume all exceeded 90%, indicating relatively ideal outcome.

Language: Английский

Citations

0
The Role of RPAS in Vegetation Height Estimation: Challenges and Future Perspectives in the Forestry Context DOI
Francisco Moreira,

Ivana Pires de Sousa-Baracho,

Maria Luiza de Azevedo

et al.

Current Forestry Reports, Journal Year: 2025, Volume and Issue: 11(1)

Published: April 22, 2025

Language: Английский

Citations

0
Optimization of Sassafras tzumu leaves color quantification with UAV RGB imaging and Sassafras-net DOI Creative Commons
Qingwei Meng, Wei Yan, Cong Xu

et al.

Information Processing in Agriculture, Journal Year: 2025, Volume and Issue: unknown

Published: Feb. 1, 2025

Language: Английский

Citations

0
Detección Automática De Palmas Ceroxylon Mediante Aprendizaje Profundo En Un Área Protegida Del Amazonas (No Perú) DOI

J. Vega,

Jhonsy O. Silva-López, Rolando Salas López

et al.

Published: Jan. 1, 2025

Citations

0
Precise identification of individual tree species in urban areas with high canopy density by multi-sensor UAV data in two seasons DOI Creative Commons

Qixia Man,

Pinliang Dong, Baolei Zhang

et al.

International Journal of Digital Earth, Journal Year: 2025, Volume and Issue: 18(1)

Published: April 25, 2025

Language: Английский

Citations

0
An open dataset for individual tree detection in UAV LiDAR point clouds and RGB orthophotos in dense mixed forests DOI Creative Commons
Ivan Dubrovin, Clément Fortin, Alexander Kedrov

et al.

Scientific Reports, Journal Year: 2024, Volume and Issue: 14(1)

Published: Sept. 20, 2024

We present an open access dataset for development, evaluation, and comparison of algorithms individual tree detection in dense mixed forests. The consists a detailed field inventory overlapping UAV LiDAR RGB orthophoto, which make it possible to develop that fuse multimodal data improve results. Along with the dataset, we describe implement basic local maxima filtering baseline algorithm automatically matching results ground truth trees evaluation.

Language: Английский

Citations

1
Estimation of Damaged Regions by the Bark Beetle in a Mexican Forest Using UAV Images and Deep Learning DOI Open Access
Gildardo Godinez-Garrido, Juan-Carlos González-Islas, Angelina González-Rosas

et al.

Sustainability, Journal Year: 2024, Volume and Issue: 16(23), P. 10731 - 10731

Published: Dec. 6, 2024

Sustainable forestry for the management of forest resources is more important today than ever before because keeping forests healthy has an impact on human health. Recent advances in Unmanned Aerial Vehicles (UAVs), computer vision, and Deep Learning (DL) models make remote sensing Forest Insect Pest Disease (FIPD) possible. In this work, a UAV-based process, framework are used to automatically efficiently detect map areas damaged by bark beetles Mexican located Hidalgo State. First, image dataset region interest (ROI) acquired UAV open hardware platform. To determine trees, we use tree crown detection prebuilt Deepforest model, trees diseased pests recognized using YOLOv5. area region, propose method based morphological operations. The system generates comprehensive report detailing location affected zones, total regions, GPS co-ordinates, both locations. overall accuracy rates were 88% 90%, respectively. results obtained from 8.2743 ha revealed that 16.8% surface was and, 455 evaluated, 34.95% damaged. These findings provide evidence fast reliable tool early evaluation beetle impact, which could be expanded other insect species.

Language: Английский

Citations

1