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Artificial Intelligence and Machine Learning Applications in Forestry
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Artificial Intelligence and Machine Learning Applications in Forestry

A special issue of Forests (ISSN 1999-4907). This special issue belongs to the section "Forest Inventory, Modeling and Remote Sensing".

Deadline for manuscript submissions: closed (30 August 2024) | Viewed by 52337

Special Issue Editors

Prof. Dr. Yunfei Liu

E-Mail Website
Guest Editor
College of Information Science and Technology, Nanjing Forestry University, Nanjing 210037, China
Interests: forestry non-destructive detection; forestry Internet of things technology; microwave and optical technology
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Ling Jiang

E-Mail Website
Guest Editor
College of Information Science and Technology, Nanjing Forestry University, Nanjing 210037, China
Interests: Internet of Things in forestry; multispectral remote sensing; intelligence systems
Special Issues, Collections and Topics in MDPI journals
Dr. Haifeng Lin

E-Mail Website
Guest Editor
College of Information Science and Technology, Nanjing Forestry University, Nanjing 210037, China
Interests: wireless sensor network; forestry Internet of Things
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues, 

With the improvements in computer hardware performance and deep learning, the new generation of information technology has been continuously integrated into the core business of forestry, and the application of artificial intelligence and machine learning technology in the field of forestry has become increasingly widespread. Countries around the world have paid much attention to the development of intelligent forestry. Smart forestry integrates the key technologies in digital forestry with the new generation of information technology, such as artificial intelligence, the Internet of Things, big data, cloud computing and mobile Internet, and forestry intelligent equipment, forming a multidisciplinary deep integration of forestry production and management, including intelligent breeding, cultivation, monitoring, operation management, protection, etc., and helping to realize three-dimensional perception, precise cultivation, real-time monitoring intelligent management, intelligent decision making, and other new models of forestry information development. The development of intelligent forestry needs to further promote the research, development, and application of intelligent algorithms such as multisource heterogeneous data fusion and machine vision, integrated data mining, model simulation, and intelligent analysis technology into forestry, promote forestry scientific and technological innovation, and achieve high-quality development of forestry on the basis of the continuous improvement of forestry theoretical research. This Special Issue aims to cover the whole range of applications, including typical applications of forestry and forestry engineering. One or more of these problems can be solved through high-quality research or review papers:

  • Applications of AI tools and applications in forestry；
  • Forestry deep learning models；
  • Recent forestry development trends and application status of information technology in forestry；
  • Intelligent sensing technology for forestry；
  • Forestry pattern recognition；
  • Multimedia and cognitive informatics in forestry；
  • Application of remote sensing big data and cloud computing in forestry；
  • Forestry virtual reality;
  • Intelligent forestry equipment.

Prof. Dr. Yunfei Liu
Prof. Dr. Ling Jiang
Dr. Haifeng Lin
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Forests is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • artificial intelligence
  • machine learning
  • deep learning
  • forest management
  • forest protection

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Published Papers (26 papers)

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Research

27 pages, 14999 KiB  
Article
Lightweight Implementation of the Signal Enhancement Model for Early Wood-Boring Pest Monitoring
by Juhu Li, Xue Li, Mengwei Ju, Xuejing Zhao, Yincheng Wang and Feng Yang
Forests 2024, 15(11), 1903; https://doi.org/10.3390/f15111903 - 29 Oct 2024
Viewed by 504
Abstract
Wood-boring pests are one of the most destructive forest pests. However, the early detection of wood-boring pests is extremely difficult because their larvae live in tree trunks and have high invisibility. Borehole listening technology is a new and effective method to detect the [...] Read more.
Wood-boring pests are one of the most destructive forest pests. However, the early detection of wood-boring pests is extremely difficult because their larvae live in tree trunks and have high invisibility. Borehole listening technology is a new and effective method to detect the larvae of insect pests. It identifies infested trees by analyzing wood-boring vibration signals. However, the collected wood-boring vibration signals are often disturbed by various noises existing in the field environment, which reduces the accuracy of pest detection. Therefore, it is necessary to filter out the noise and enhance the wood-boring vibration signals to facilitate the subsequent identification of pests. The current signal enhancement models are all designed based on deep learning models, which have complex scales, a large number of parameters, high demands for storage resources, large computational complexity, and high time costs. They often run on resource-rich computers or servers, and they are difficult to deploy to resource-limited field environments to realize the real-time monitoring of pests; as well, they have low practicability. Therefore, this study designs and implements two model lightweight optimization algorithms, one is a pre-training pruning algorithm based on masks, and the other is a knowledge distillation algorithm based on the separate transfer of vibration signal knowledge and noise signal knowledge. We apply the two lightweight optimization algorithms to the signal enhancement model T-CENV with good performance outcomes and conduct a series of ablation experiments. The experimental results show that the proposed methods effectively reduce the volume of the T-CENV model, which make them useful for the deployment of signal enhancement models on embedded devices, improve the usability of the model, and help to realize the real-time monitoring of wood-boring pest larvae. Full article
(This article belongs to the Special Issue Artificial Intelligence and Machine Learning Applications in Forestry)
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22 pages, 11728 KiB  
Article
Mcan-YOLO: An Improved Forest Fire and Smoke Detection Model Based on YOLOv7
by Hongying Liu, Jun Zhu, Yiqing Xu and Ling Xie
Forests 2024, 15(10), 1781; https://doi.org/10.3390/f15101781 - 10 Oct 2024
Viewed by 870
Abstract
Forest fires pose a significant threat to forest resources and wildlife. To balance accuracy and parameter efficiency in forest fire detection, this study proposes an improved model, Mcan-YOLO, based on YOLOv7. In the Neck section, the asymptotic feature pyramid network (AFPN) was employed [...] Read more.
Forest fires pose a significant threat to forest resources and wildlife. To balance accuracy and parameter efficiency in forest fire detection, this study proposes an improved model, Mcan-YOLO, based on YOLOv7. In the Neck section, the asymptotic feature pyramid network (AFPN) was employed to effectively capture multi-scale information, replacing the traditional module. Additionally, the content-aware reassembly of features (CARAFE) replaced the conventional upsampling method, further reducing the number of parameters. The normalization-based attention module (NAM) was integrated after the ELAN-T module to enhance the recognition of various fire smoke features, and the Mish activation function was used to optimize model convergence. A real fire smoke dataset was constructed using the mean structural similarity (MSSIM) algorithm for model training and validation. The experimental results showed that, compared to YOLOv7-tiny, Mcan-YOLO improved precision by 4.6%, recall by 6.5%, and mAP50 by 4.7%, while reducing the number of parameters by 5%. Compared with other mainstream algorithms, Mcan-YOLO achieved better precision with fewer parameters. Full article
(This article belongs to the Special Issue Artificial Intelligence and Machine Learning Applications in Forestry)
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17 pages, 2488 KiB  
Article
Timber Harvest Planning Using Reinforcement Learning: A Feasibility Study
by Hyo-Vin Ji, Sang-Kyun Han and Jin-Woo Park
Forests 2024, 15(10), 1725; https://doi.org/10.3390/f15101725 - 29 Sep 2024
Viewed by 741
Abstract
This study developed a forest management plan model using reinforcement learning (Q-learning) to optimize both the economic and ecological functions of forests. Management objectives for national forests were established, and forest conditions were analyzed using GIS spatial data and administrative records. A 60-year [...] Read more.
This study developed a forest management plan model using reinforcement learning (Q-learning) to optimize both the economic and ecological functions of forests. Management objectives for national forests were established, and forest conditions were analyzed using GIS spatial data and administrative records. A 60-year forest management plan was formulated to predict timber production and management performance across different regions and time periods. Our analysis revealed that Scenario 3 (Carbon Storage Priority) demonstrated the highest economic value, starting at approximately KRW 576.2 billion in the initial period and escalating to KRW 775.7 billion over six 10-year periods, totaling 60 years. In addition to its economic performance, Scenario 3 effectively improved forest age class structure and ensured a stable timber supply, making it the most balanced approach for sustainable forest management. By focusing on carbon storage as a key management goal, this approach highlights the potential for achieving both economic and environmental benefits concurrently. These results suggest that reinforcement learning is a powerful tool for developing long-term forest management strategies that address multiple objectives, including economic viability, ecological sustainability, and resource optimization. Full article
(This article belongs to the Special Issue Artificial Intelligence and Machine Learning Applications in Forestry)
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26 pages, 29060 KiB  
Article
LD-YOLO: A Lightweight Dynamic Forest Fire and Smoke Detection Model with Dysample and Spatial Context Awareness Module
by Zhenyu Lin, Bensheng Yun and Yanan Zheng
Forests 2024, 15(9), 1630; https://doi.org/10.3390/f15091630 - 15 Sep 2024
Cited by 1 | Viewed by 1282
Abstract
The threat of forest fires to human life and property causes significant damage to human society. Early signs, such as small fires and smoke, are often difficult to detect. As a consequence, early detection of smoke and fires is crucial. Traditional forest fire [...] Read more.
The threat of forest fires to human life and property causes significant damage to human society. Early signs, such as small fires and smoke, are often difficult to detect. As a consequence, early detection of smoke and fires is crucial. Traditional forest fire detection models have shortcomings, including low detection accuracy and efficiency. The YOLOv8 model exhibits robust capabilities in detecting forest fires and smoke. However, it struggles to balance accuracy, model complexity, and detection speed. This paper proposes LD-YOLO, a lightweight dynamic model based on the YOLOv8, to detect forest fires and smoke. Firstly, GhostConv is introduced to generate more smoke feature maps in forest fires through low-cost linear transformations, while maintaining high accuracy and reducing model parameters. Secondly, we propose C2f-Ghost-DynamicConv as an effective tool for increasing feature extraction and representing smoke from forest fires. This method aims to optimize the use of computing resources. Thirdly, we introduce DySample to address the loss of fine-grained detail in initial forest fire images. A point-based sampling method is utilized to enhance the resolution of small-target fire images without imposing an additional computational burden. Fourthly, the Spatial Context Awareness Module (SCAM) is introduced to address insufficient feature representation and background interference. Also, a lightweight self-attention detection head (SADH) is designed to capture global forest fire and smoke features. Lastly, Shape-IoU, which emphasizes the importance of boundaries’ shape and scale, is used to improve smoke detection in forest fires. The experimental results show that LD-YOLO realizes an mAP0.5 of 86.3% on a custom forest fire dataset, which is 4.2% better than the original model, with 36.79% fewer parameters, 48.24% lower FLOPs, and 15.99% higher FPS. Therefore, LD-YOLO indicates forest fires and smoke with high accuracy, fast detection speed, and a low model complexity. This is crucial to the timely detection of forest fires. Full article
(This article belongs to the Special Issue Artificial Intelligence and Machine Learning Applications in Forestry)
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25 pages, 4813 KiB  
Article
Land Cover Classification of Remote Sensing Imagery with Hybrid Two-Layer Attention Network Architecture
by Xiangsuo Fan, Xuyang Li and Jinlong Fan
Forests 2024, 15(9), 1504; https://doi.org/10.3390/f15091504 - 28 Aug 2024
Viewed by 778
Abstract
In remote sensing image processing, when categorizing images from multiple remote sensing data sources, the deepening of the network hierarchy is prone to the problems of feature dispersion, as well as the loss of semantic information. In order to solve this problem, this [...] Read more.
In remote sensing image processing, when categorizing images from multiple remote sensing data sources, the deepening of the network hierarchy is prone to the problems of feature dispersion, as well as the loss of semantic information. In order to solve this problem, this paper proposes to integrate a parallel network architecture HDAM-Net algorithm with a hybrid dual attention mechanism Hybrid dual attention mechanism for forest land cover change. Firstly, we propose a fusion MCA + SAM (MS) attention mechanism to improve VIT network, which can capture the correlation information between features; secondly, we propose a multilayer residual cascade convolution (MSCRC) network model using Double Cross-Attention Module (DCAM) attention mechanism, which is able to efficiently utilize the spatial dependency between multiscale encoder features: the spatial dependency between multiscale encoder features. Finally, the dual-channel parallel architecture is utilized to solve the structural differences and realize the enhancement of forestry image classification differentiation and effective monitoring of forest cover changes. In order to compare the performance of HDAM-Net, mountain urban forest types are classified based on multiple remote sensing data sources, and the performance of the model is evaluated. The experimental results show that the overall accuracy of the algorithm proposed in this paper is 99.42%, while the Transformer (ViT) is 96.92%, which indicates that the proposed classifier is able to accurately determine the cover type.The HDAM-Net model emphasizes the effectiveness in terms of accurately classifying the land, as well as the forest types by using multiple remote sensing data sources for predicting the future trend of the forest ecosystem. In addition, the land utilization rate and land cover change can clearly show the forest cover change and support the data to predict the future trend of the forest ecosystem so that the forest resource survey can effectively monitor deforestation and evaluate forest restoration projects. Full article
(This article belongs to the Special Issue Artificial Intelligence and Machine Learning Applications in Forestry)
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22 pages, 1644 KiB  
Article
Predicting Forest Fire Area Growth Rate Using an Ensemble Algorithm
by Long Zhang, Changjiang Shi and Fuquan Zhang
Forests 2024, 15(9), 1493; https://doi.org/10.3390/f15091493 - 26 Aug 2024
Viewed by 1187
Abstract
Due to its unique geographical and climatic conditions, the Liangshan Prefecture region is highly prone to large fires. There is an urgent need to study the growth rate of fire-burned areas to fill the research gap in this region. To address this issue, [...] Read more.
Due to its unique geographical and climatic conditions, the Liangshan Prefecture region is highly prone to large fires. There is an urgent need to study the growth rate of fire-burned areas to fill the research gap in this region. To address this issue, this study uses the Grey Wolf Optimizer (GWO) algorithm to optimize the hyperparameters in the eXtreme Gradient Boosting (XGBoost) model, constructing a GWO-XGBoost model. Finally, the optimized ensemble model (GWO-XGBoost) is used to create a fire growth rate warning map for the Liangshan Prefecture in Sichuan Province, China, filling the research gap in forest fire studies in this area. This study comprehensively selects factors such as monthly climate, monthly vegetation, terrain, and socio–economic aspects and incorporates monthly reanalysis data from forest fire assessment systems in Canada, the United States, and Australia as features to construct the forest fire dataset. After collinearity tests to filter redundant features and Pearson correlation analysis to explore features related to the burned area growth rate, the Synthetic Minority Oversampling Technique (SMOTE) is used to oversample the positive class samples. The GWO algorithm is used to optimize the hyperparameters in the XGBoost model, constructing the GWO-XGBoost model, which is then compared with XGBoost, Random Forest (RF), and Logistic Regression (LR) models. Model evaluation results showed that the GWO-XGBoost model, with an AUC value of 0.8927, is the best-performing model. Using the SHapley Additive exPlanations (SHAP) value analysis method to quantify the contribution of each influencing factor indicates that the Ignition Component (IC) value from the United States National Fire Danger Rating System contributes the most, followed by the average monthly temperature and the population density. The growth rate warning map results indicate that the southern part of the study area is the key fire prevention area. Full article
(This article belongs to the Special Issue Artificial Intelligence and Machine Learning Applications in Forestry)
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19 pages, 10282 KiB  
Article
SmokeFireNet: A Lightweight Network for Joint Detection of Forest Fire and Smoke
by Yi Chen and Fang Wang
Forests 2024, 15(9), 1489; https://doi.org/10.3390/f15091489 - 25 Aug 2024
Viewed by 848
Abstract
In recent years, forest fires have been occurring frequently around the globe, affected by extreme weather and dry climate, causing serious economic losses and environmental pollution. In this context, timely detection of forest fire smoke is crucial for realizing real-time early warning of [...] Read more.
In recent years, forest fires have been occurring frequently around the globe, affected by extreme weather and dry climate, causing serious economic losses and environmental pollution. In this context, timely detection of forest fire smoke is crucial for realizing real-time early warning of fires. However, fire and smoke from forest fires can spread to cover large areas and may affect distant areas. In this paper, a lightweight joint forest fire and smoke detection network, SmokeFireNet, is proposed, which employs ShuffleNetV2 as the backbone for efficient feature extraction, effectively addressing the computational efficiency challenges of traditional methods. To integrate multi-scale information and enhance the semantic feature extraction capability, a feature pyramid network (FPN) and path aggregation network (PAN) are introduced in this paper. In addition, the FPN network is optimized by a lightweight DySample upsampling operator. The model also incorporates efficient channel attention (ECA), which can pay more attention to the detection of forest fires and smoke regions while suppressing irrelevant features. Finally, by embedding the receptive field block (RFB), the model further improves its ability to understand contextual information and capture detailed features of fire and smoke, thus improving the overall detection accuracy. The experimental results show that SmokeFireNet is better than other mainstream target detection algorithms in terms of average APall of 86.2%, FPS of 114, and GFLOPs of 8.4, and provides effective technical support for forest fire prevention work in terms of average precision, frame rate, and computational complexity. In the future, the SmokeFireNet model is expected to play a greater role in the field of forest fire prevention and make a greater contribution to the protection of forest resources and the ecological environment. Full article
(This article belongs to the Special Issue Artificial Intelligence and Machine Learning Applications in Forestry)
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14 pages, 16501 KiB  
Article
Mapping Forest Growing Stock and Its Current Annual Increment Using Random Forest and Remote Sensing Data in Northeast Italy
by Luca Cadez, Antonio Tomao, Francesca Giannetti, Gherardo Chirici and Giorgio Alberti
Forests 2024, 15(8), 1356; https://doi.org/10.3390/f15081356 - 3 Aug 2024
Viewed by 1274
Abstract
The role of forests in providing multiple goods and services has been recognized worldwide. In such a context, reliable spatial predictions of forest attributes such as tree volume and current increment are fundamental for conducting forest monitoring, improving restoration programs, and supporting decision-making [...] Read more.
The role of forests in providing multiple goods and services has been recognized worldwide. In such a context, reliable spatial predictions of forest attributes such as tree volume and current increment are fundamental for conducting forest monitoring, improving restoration programs, and supporting decision-making processes. This article presents the methodology and the results of the wall-to-wall spatialization of the growing stock volume and the current annual increment measured in 273 plots of data of the Italian National Forest Inventory over an area of more than 3260 km2 in the Friuli Venezia Giulia region (Northeast Italy). To this aim, a random forest model was tested using as predictors 4 spectral indices from Sentinel-2, a high-resolution Canopy Height Model derived from LiDAR, and geo-morphological data. According to the Leave One Out cross-validation procedure, the model for the growing stock shows an R2 and an RMSE% of 0.67 and 41%, respectively. Instead, an R2 of 0.47 and an RMSE% of 57% were obtained for the current annual increment. The validation with an independent dataset further improved the models’ performances, yielding significantly higher R2 values of 0.84 and 0.83 for volume and for increment, respectively. Our results underline a relatively higher importance of LiDAR-derived metrics compared to other covariates in estimating both attributes, as they were even twice as important as vegetation indices for growing stock. Therefore, these metrics are promising for the development of a national LiDAR-based model. Full article
(This article belongs to the Special Issue Artificial Intelligence and Machine Learning Applications in Forestry)
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29 pages, 15222 KiB  
Article
Detection Model and Spectral Disease Indices for Poplar (Populus L.) Anthracnose Based on Hyperspectral Reflectance
by Zhicheng Jia, Qifeng Duan, Yue Wang, Ke Wu and Hongzhe Jiang
Forests 2024, 15(8), 1309; https://doi.org/10.3390/f15081309 - 26 Jul 2024
Viewed by 824
Abstract
Poplar (Populus L.) anthracnose is an infectious disease that seriously affects the growth and yields of poplar trees, and large-scale poplar infections have led to huge economic losses in the Chinese poplar industry. To efficiently and accurately detect poplar anthracnose for improved [...] Read more.
Poplar (Populus L.) anthracnose is an infectious disease that seriously affects the growth and yields of poplar trees, and large-scale poplar infections have led to huge economic losses in the Chinese poplar industry. To efficiently and accurately detect poplar anthracnose for improved prevention and control, this study collected hyperspectral data from the leaves of four types of poplar trees, namely healthy trees and those with black spot disease, early-stage anthracnose, and late-stage anthracnose, and constructed a poplar anthracnose detection model based on machine learning and deep learning. We then comprehensively analyzed poplar anthracnose using advanced hyperspectral-based plant disease detection methodologies. Our research focused on establishing a detection model for poplar anthracnose based on small samples, employing the Design of Experiments (DoE)-based entropy weight method to obtain the best preprocessing combination to improve the detection model’s overall performance. We also analyzed the spectral characteristics of poplar anthracnose by comparing typical feature extraction methods (principal component analysis (PCA), variable combination population analysis (VCPA), and the successive projection algorithm (SPA)) with the vegetation index (VI) method (spectral disease indices (SDIs)) for data dimensionality reduction. The results showed notable improvements in the SDI-based model, which achieved 89.86% accuracy. However, this was inferior to the model based on typical feature extraction methods. Nevertheless, it achieved 100% accuracy for early-stage anthracnose and black spot disease in a controlled environment respectively. We conclude that the SDI-based model is suitable for low-cost detection tasks and is the best poplar anthracnose detection model. These findings contribute to the timely detection of poplar growth and will greatly facilitate the forestry sector’s development. Full article
(This article belongs to the Special Issue Artificial Intelligence and Machine Learning Applications in Forestry)
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21 pages, 6467 KiB  
Article
Detection of Mulberry Leaf Diseases in Natural Environments Based on Improved YOLOv8
by Ming Zhang, Chang Yuan, Qinghua Liu, Hongrui Liu, Xiulin Qiu and Mengdi Zhao
Forests 2024, 15(7), 1188; https://doi.org/10.3390/f15071188 - 9 Jul 2024
Viewed by 1261
Abstract
Mulberry leaves, when infected by pathogens, can suffer significant yield loss or even death if early disease detection and timely spraying are not performed. To enhance the detection performance of mulberry leaf diseases in natural environments and to precisely locate early small lesions, [...] Read more.
Mulberry leaves, when infected by pathogens, can suffer significant yield loss or even death if early disease detection and timely spraying are not performed. To enhance the detection performance of mulberry leaf diseases in natural environments and to precisely locate early small lesions, we propose a high-precision, high-efficiency disease detection algorithm named YOLOv8-RFMD. Based on improvements to You Only Look Once version 8 (YOLOv8), we first proposed the Multi-Dimension Feature Attention (MDFA) module, which integrates important features at the pixel-level, spatial, and channel dimensions. Building on this, we designed the RFMD Module, which consists of the Conv-BatchNomalization-SiLU (CBS) module, Receptive-Field Coordinated Attention (RFCA) Conv, and MDFA, replacing the Bottleneck in the model’s Residual block. We then employed the ADown down-sampling structure to reduce the model size and computational complexity. Finally, to improve the detection precision of small lesion features, we replaced the Complete Intersection over Union (CIOU) loss function with the Normalized Wasserstein Distance (NWD) loss function. Results show that the YOLOv8-RFMD model achieved a mAP50 of 94.3% and a mAP50:95 of 67.8% on experimental data, representing increases of 2.9% and 4.3%, respectively, compared to the original model. The model size was reduced by 0.53 MB to just 5.45 MB, and the GFLOPs were reduced by 0.3 to only 7.8. YOLOv8-RFMD has displayed great potential for application in real-world mulberry leaf disease detection systems and automatic spraying operations. Full article
(This article belongs to the Special Issue Artificial Intelligence and Machine Learning Applications in Forestry)
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20 pages, 5436 KiB  
Article
FSNet: Enhancing Forest-Fire and Smoke Detection with an Advanced UAV-Based Network
by Donghua Wu, Zhongmin Qian, Dongyang Wu and Junling Wang
Forests 2024, 15(5), 787; https://doi.org/10.3390/f15050787 - 30 Apr 2024
Viewed by 1336
Abstract
Forest fires represent a significant menace to both the ecological equilibrium of forests and the safety of human life and property. Upon ignition, fires frequently generate billowing smoke. The prompt identification and management of fire sources and smoke can efficiently avert the occurrence [...] Read more.
Forest fires represent a significant menace to both the ecological equilibrium of forests and the safety of human life and property. Upon ignition, fires frequently generate billowing smoke. The prompt identification and management of fire sources and smoke can efficiently avert the occurrence of extensive forest fires, thereby safeguarding both forest resources and human well-being. Although drone patrols have emerged as a primary method for forest-fire prevention, the unique characteristics of forest-fire images captured from high altitudes present challenges. These include remote distances, small fire points, smoke targets with light hues, and complex, ever-changing background environments. Consequently, traditional target-detection networks frequently exhibit diminished accuracy when handling such images. In this study, we introduce a cutting-edge drone-based network designed for the detection of forest fires and smoke, named FSNet. To begin, FSNet employs the YOCO data-augmentation method to enhance image processing, thereby augmenting both local and overall diversity within forest-fire images. Next, building upon the transformer framework, we introduce the EBblock attention module. Within this module, we introduce the notion of “groups”, maximizing the utilization of the interplay between patch tokens and groups to compute the attention map. This approach facilitates the extraction of correlations among patch tokens, between patch tokens and groups, and among groups. This approach enables the comprehensive feature extraction of fire points and smoke within the image, minimizing background interference. Across the four stages of the EBblock, we leverage a feature pyramid to integrate the outputs from each stage, thereby mitigating the loss of small target features. Simultaneously, we introduce a tailored loss function, denoted as Lforest, specifically designed for FSNet. This ensures the model’s ability to learn effectively and produce high-quality prediction boxes. We assess the performance of the FSNet model across three publicly available forest-fire datasets, utilizing mAP, Recall, and FPS as evaluation metrics. The outcomes reveal that FSNet achieves remarkable results: on the Flame, Corsican, and D-Fire datasets, it attains mAP scores of 97.2%, 87.5%, and 94.3%, respectively, with Recall rates of 93.9%, 87.3%, and 90.8%, respectively, and FPS values of 91.2, 90.7, and 92.6, respectively. Furthermore, extensive comparative and ablation experiments validate the superior performance of the FSNet model. Full article
(This article belongs to the Special Issue Artificial Intelligence and Machine Learning Applications in Forestry)
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13 pages, 2629 KiB  
Article
Fire in Focus: Advancing Wildfire Image Segmentation by Focusing on Fire Edges
by Guodong Wang, Fang Wang, Hongping Zhou and Haifeng Lin
Forests 2024, 15(1), 217; https://doi.org/10.3390/f15010217 - 22 Jan 2024
Cited by 7 | Viewed by 1816
Abstract
With the intensification of global climate change and the frequent occurrence of forest fires, the development of efficient and precise forest fire monitoring and image segmentation technologies has become increasingly important. In dealing with challenges such as the irregular shapes, sizes, and blurred [...] Read more.
With the intensification of global climate change and the frequent occurrence of forest fires, the development of efficient and precise forest fire monitoring and image segmentation technologies has become increasingly important. In dealing with challenges such as the irregular shapes, sizes, and blurred boundaries of flames and smoke, traditional convolutional neural networks (CNNs) face limitations in forest fire image segmentation, including flame edge recognition, class imbalance issues, and adapting to complex scenarios. This study aims to enhance the accuracy and efficiency of flame recognition in forest fire images by introducing a backbone network based on the Swin Transformer and combined with an adaptive multi-scale attention mechanism and focal loss function. By utilizing a rich and diverse pre-training dataset, our model can more effectively capture and understand key features of forest fire images. Through experimentation, our model achieved an intersection over union (IoU) of 86.73% and a precision of 91.23%. This indicates that the performance of our proposed wildfire segmentation model has been effectively enhanced. A series of ablation experiments validate the importance of these technological improvements in enhancing model performance. The results show that our approach achieves significant performance improvements in forest fire image segmentation tasks compared to traditional models. The Swin Transformer provides more refined feature extraction capabilities, the adaptive multi-scale attention mechanism helps the model focus better on key areas, and the focal loss function effectively addresses the issue of class imbalance. These innovations make the model more precise and robust in handling forest fire image segmentation tasks, providing strong technical support for future forest fire monitoring and prevention. Full article
(This article belongs to the Special Issue Artificial Intelligence and Machine Learning Applications in Forestry)
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24 pages, 9990 KiB  
Article
SWVR: A Lightweight Deep Learning Algorithm for Forest Fire Detection and Recognition
by Li Jin, Yanqi Yu, Jianing Zhou, Di Bai, Haifeng Lin and Hongping Zhou
Forests 2024, 15(1), 204; https://doi.org/10.3390/f15010204 - 19 Jan 2024
Cited by 14 | Viewed by 2555
Abstract
The timely and effective detection of forest fires is crucial for environmental and socio-economic protection. Existing deep learning models struggle to balance accuracy and a lightweight design. We introduce SWVR, a new lightweight deep learning algorithm. Utilizing the Reparameterization Vision Transformer (RepViT) and [...] Read more.
The timely and effective detection of forest fires is crucial for environmental and socio-economic protection. Existing deep learning models struggle to balance accuracy and a lightweight design. We introduce SWVR, a new lightweight deep learning algorithm. Utilizing the Reparameterization Vision Transformer (RepViT) and Simple Parameter-Free Attention Module (SimAM), SWVR efficiently extracts fire-related features with reduced computational complexity. It features a bi-directional fusion network combining top-down and bottom-up approaches, incorporates lightweight Ghost Shuffle Convolution (GSConv), and uses the Wise Intersection over Union (WIoU) loss function. SWVR achieves 79.6% accuracy in detecting forest fires, which is a 5.9% improvement over the baseline, and operates at 42.7 frames per second. It also reduces the model parameters by 11.8% and the computational cost by 36.5%. Our results demonstrate SWVR’s effectiveness in achieving high accuracy with fewer computational resources, offering practical value for forest fire detection. Full article
(This article belongs to the Special Issue Artificial Intelligence and Machine Learning Applications in Forestry)
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21 pages, 6716 KiB  
Article
QPWS Feature Selection and CAE Fusion of Visible/Near-Infrared Spectroscopy Data for the Identification of Salix psammophila Origin
by Yicheng Ma, Ying Li, Xinkai Peng, Congyu Chen, Hengkai Li, Xinping Wang, Weilong Wang, Xiaozhen Lan, Jixuan Wang and Zhiyong Pei
Forests 2024, 15(1), 6; https://doi.org/10.3390/f15010006 - 19 Dec 2023
Viewed by 1575
Abstract
Salix psammophila, classified under the Salicaceae family, is a deciduous, densely branched, and erect shrub. As a leading pioneer tree species in windbreak and sand stabilization, it has played a crucial role in combating desertification in northwestern China. However, different genetic sources [...] Read more.
Salix psammophila, classified under the Salicaceae family, is a deciduous, densely branched, and erect shrub. As a leading pioneer tree species in windbreak and sand stabilization, it has played a crucial role in combating desertification in northwestern China. However, different genetic sources of Salix psammophila exhibit significant variations in their effectiveness for windbreak and sand stabilization. Therefore, it is essential to establish a rapid and reliable method for identifying different Salix psammophila varieties. Visible and near-infrared (Vis-NIR) spectroscopy is currently a reliable non-destructive solution for origin traceability. This study introduced a novel feature selection strategy, called qualitative percentile weighted sampling (QPWS), based on the principle of the long tail effect for Vis-NIR spectroscopy. The core idea of QPWS combines weighted sampling and percentage wavelength selection to identify key wavelengths. By employing a multi-threaded parallel execution of multiple QPWS instances, we aimed to search for the optimal feature bands to address the instability issues that can arise during the feature selection process. To address the problem of reduced prediction performance in one-dimensional convolutional neural network (1D-CNN) models after feature selection, we have introduced convolutional autoencoders (CAEs) to reduce the dimensions of wavelengths that are discarded during feature selection. Subsequently, these reduced dimensions are fused with the selected wavelengths, thereby enhancing the model’s performance. With our completed model, we selected outstanding models for model fusion and established a decision system for Salix psammophila. It is worth noting that all 1D-CNN models in this study were developed using Bayesian optimization methods. In comparison with principal component analysis (PCA) and full spectrum methods, QPWS exhibits superior predictive performance in the field of machine learning. In the realm of deep learning, the fusion of data combining QPWS with CAE demonstrated even greater potential with an improvement of average accuracy of approximately 2.13% when compared to QPWS alone and a 228% increase in operational speed compared to a model with full spectra. These results indicated that the combination of CAE with QPWS can be an effective tool for identifying the origin of Salix psammophila. Full article
(This article belongs to the Special Issue Artificial Intelligence and Machine Learning Applications in Forestry)
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29 pages, 7642 KiB  
Article
Reinforcement Learning for Stand Structure Optimization of Pinus yunnanensis Secondary Forests in Southwest China
by Shuai Xuan, Jianming Wang and Yuling Chen
Forests 2023, 14(12), 2456; https://doi.org/10.3390/f14122456 - 17 Dec 2023
Cited by 1 | Viewed by 1490
Abstract
Aiming to enhance the efficiency and precision of multi-objective optimization in southwestern secondary growth of Pinus yunnanensis forests, this study integrated spatial and non-spatial structural indicators to establish objective functions and constraints for assessing forest structure. Felling decisions were made using the random [...] Read more.
Aiming to enhance the efficiency and precision of multi-objective optimization in southwestern secondary growth of Pinus yunnanensis forests, this study integrated spatial and non-spatial structural indicators to establish objective functions and constraints for assessing forest structure. Felling decisions were made using the random selection method (RSM), Q-value method (QVM), and V-map method (VMM). Actions taken to optimize the forest stand structure (FSS) through tree selection were approached as decisions by a reinforcement learning (RL) agent. Leveraging RL’s trial-and-error strategy, we continually refined the agent’s decision-making process, applying it to multi-objective optimization. Simulated felling experiments conducted across circular sample plots (P1–P4) compared RL, Monte Carlo (MC), and particle swarm optimization (PSO) in FSS optimization. Notable enhancements in the values of the objective function (VOFs) were observed across all plots. RL-based strategies exhibited improvements, achieving VOF increases of 17.24%, 44.92%, 34.66%, and 17.10% for P1–P4, respectively, outperforming MC-based (10.73%, 41.54%, 30.39%, and 15.07%, respectively) and PSO-based (14.08%, 37.78%, 26.17%, and 16.23%, respectively) approaches. The hybrid M7 scheme, integrating RL with the RSM, consistently outperformed other schemes across all plots, yielding an average 26.81% increase in VOF compared to the average enhancement of all schemes (17.42%). This study significantly advances the efficacy and precision of multi-objective optimization strategies for Pinus yunnanensis secondary forests, emphasizing RL’s superior optimization performance, particularly when combined with the RSM, highlighting its potential for optimizing sustainable forest management strategies. Full article
(This article belongs to the Special Issue Artificial Intelligence and Machine Learning Applications in Forestry)
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18 pages, 6855 KiB  
Article
An Efficient Forest Fire Detection Algorithm Using Improved YOLOv5
by Pei Shi, Jun Lu, Quan Wang, Yonghong Zhang, Liang Kuang and Xi Kan
Forests 2023, 14(12), 2440; https://doi.org/10.3390/f14122440 - 14 Dec 2023
Cited by 3 | Viewed by 2528
Abstract
Forest fires result in severe disaster, causing significant ecological damage and substantial economic losses. Flames and smoke represent the predominant characteristics of forest fires. However, these flames and smoke often exhibit irregular shapes, rendering them susceptible to erroneous positive or negative identifications, consequently [...] Read more.
Forest fires result in severe disaster, causing significant ecological damage and substantial economic losses. Flames and smoke represent the predominant characteristics of forest fires. However, these flames and smoke often exhibit irregular shapes, rendering them susceptible to erroneous positive or negative identifications, consequently compromising the overall performance of detection systems. To enhance the average precision and recall rates of detection, this paper introduces an enhanced iteration of the You Only Look Once version 5 (YOLOv5) algorithm. This advanced algorithm aims to achieve more effective fire detection. First, we use Switchable Atrous Convolution (SAC) in the backbone network of the traditional YOLOv5 to enhance the capture of a larger receptive field. Then, we introduce Polarized Self-Attention (PSA) to improve the modeling of long-range dependencies. Finally, we incorporate Soft Non-Maximum Suppression (Soft-NMS) to address issues related to missed detections and repeated detections of flames and smoke by the algorithm. Among the plethora of models explored, our proposed algorithm achieves a 2.0% improvement in mean Average [email protected] (mAP50) and a 3.1% enhancement in Recall when compared with the YOLOv5 algorithm. The integration of SAC, PSA, and Soft-NMS significantly enhances the precision and efficiency of the detection algorithm. Moreover, the comprehensive algorithm proposed here can identify and detect key changes in various monitoring scenarios. Full article
(This article belongs to the Special Issue Artificial Intelligence and Machine Learning Applications in Forestry)
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13 pages, 3657 KiB  
Article
ForestFireDetector: Expanding Channel Depth for Fine-Grained Feature Learning in Forest Fire Smoke Detection
by Long Sun, Yidan Li and Tongxin Hu
Forests 2023, 14(11), 2157; https://doi.org/10.3390/f14112157 - 30 Oct 2023
Cited by 1 | Viewed by 1602
Abstract
Wildfire is a pressing global issue that transcends geographic boundaries. Many areas, including China, are trying to cope with the threat of wildfires and manage limited forest resources. Effective forest fire detection is crucial, given its significant implications for ecological balance, social well-being [...] Read more.
Wildfire is a pressing global issue that transcends geographic boundaries. Many areas, including China, are trying to cope with the threat of wildfires and manage limited forest resources. Effective forest fire detection is crucial, given its significant implications for ecological balance, social well-being and economic stability. In light of the problems of noise misclassification and manual design of the components in the current forest fire detection model, particularly the limited capability to identify subtle and unnoticeable smoke within intricate forest environments, this paper proposes an improved smoke detection model for forest fires utilizing YOLOv8 as its foundation. We expand the channel depth for fine-grain feature learning and retain more feature information. At the same time, lightweight convolution reduces the parameters of the model. This model enhances detection accuracy for smoke targets of varying scales and surpasses the accuracy of mainstream models. The outcomes of experiments demonstrate that the improved model exhibits superior performance, and the mean average precision is improved by 3.3%. This model significantly enhances the detection ability while also optimizing the neural network to make it more lightweight. These advancements position the model as a promising solution for early-stage forest fire smoke detection. Full article
(This article belongs to the Special Issue Artificial Intelligence and Machine Learning Applications in Forestry)
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12 pages, 17124 KiB  
Article
SegForest: A Segmentation Model for Remote Sensing Images
by Hanzhao Wang, Chunhua Hu, Ranyang Zhang and Weijie Qian
Forests 2023, 14(7), 1509; https://doi.org/10.3390/f14071509 - 24 Jul 2023
Cited by 5 | Viewed by 2407
Abstract
The accurate estimation of forest area is of paramount importance for carbon sequestration projects, ecotourism and ecological safety. Forest segmentation using remote sensing images is a crucial technique for estimating forest area. However, due to the complex features, such as the size, shape [...] Read more.
The accurate estimation of forest area is of paramount importance for carbon sequestration projects, ecotourism and ecological safety. Forest segmentation using remote sensing images is a crucial technique for estimating forest area. However, due to the complex features, such as the size, shape and color of forest plots, traditional segmentation algorithms struggle to achieve accurate segmentation. Therefore, this study proposes a remote sensing image forest segmentation model named SegForest. To enhance the model, we introduce three new modules: multi-feature fusion (MFF), multi-scale multi-decoder (MSMD) and weight-based cross entropy loss function (WBCE) in the decoder. In addition, we propose two new forest remote sensing image segmentation binary datasets: DeepGlobe-Forest and Loveda-Forest. SegForest is compared with multiple advanced segmentation algorithms on these two datasets. On the DeepGlobe-Forest dataset, SegForest achieves a mean intersection over union (mIoU) of 83.39% and a mean accuracy (mAcc) of 91.00%. On the Loveda-Forest dataset, SegForest achieves a mIoU of 73.71% and a mAcc of 85.06%. These metrics outperform other algorithms in the comparative experiments. The experimental results of this paper demonstrate that by incorporating the three proposed modules, the SegForest model has strong performance and generalization ability in forest remote sensing image segmentation tasks. Full article
(This article belongs to the Special Issue Artificial Intelligence and Machine Learning Applications in Forestry)
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17 pages, 1863 KiB  
Article
A Forest Fire Susceptibility Modeling Approach Based on Integration Machine Learning Algorithm
by Changjiang Shi and Fuquan Zhang
Forests 2023, 14(7), 1506; https://doi.org/10.3390/f14071506 - 24 Jul 2023
Cited by 12 | Viewed by 2453
Abstract
The subjective and empirical setting of hyperparameters in the random forest (RF) model may lead to decreased model performance. To address this, our study applies the particle swarm optimization (PSO) algorithm to select the optimal parameters of the RF model, with the goal [...] Read more.
The subjective and empirical setting of hyperparameters in the random forest (RF) model may lead to decreased model performance. To address this, our study applies the particle swarm optimization (PSO) algorithm to select the optimal parameters of the RF model, with the goal of enhancing model performance. We employ the optimized ensemble model (PSO-RF) to create a fire risk map for Jiushan National Forest Park in Anhui Province, China, thereby filling the research gap in this region’s forest fire studies. Based on collinearity tests and previous research results, we selected eight fire driving factors, including topography, climate, human activities, and vegetation for modeling. Additionally, we compare the logistic regression (LR), support vector machine (SVM), and RF models. Lastly, we select the optimal model to evaluate feature importance and generate the fire risk map. Model evaluation results demonstrate that the PSO-RF model performs best (AUC = 0.908), followed by RF (0.877), SVM (0.876), and LR (0.846). In the fire risk map created by the PSO-RF model, 70.73% of the area belongs to the normal management zone, while 15.23% is classified as a fire alert zone. The feature importance analysis of the PSO-RF model reveals that the NDVI is the key fire driving factor in this study area. Through utilizing the PSO algorithm to optimize the RF model, we have addressed the subjective and empirical problems of the RF model hyperparameter setting, thereby enhancing the model’s accuracy and generalization ability. Full article
(This article belongs to the Special Issue Artificial Intelligence and Machine Learning Applications in Forestry)
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12 pages, 2912 KiB  
Article
Quantitative Analysis of Forest Water COD Value Based on UV–vis and FLU Spectral Information Fusion
by Chun Li, Xin Ma, Yan Teng, Shaochen Li, Yuanyin Jin, Jie Du and Ling Jiang
Forests 2023, 14(7), 1361; https://doi.org/10.3390/f14071361 - 2 Jul 2023
Cited by 4 | Viewed by 1808
Abstract
As an important ecosystem on the earth, forests not only provide habitat and food for organisms but also play an important role in regulating environmental elements such as water, atmosphere, and soil. The quality of forest waters directly affects the health and stability [...] Read more.
As an important ecosystem on the earth, forests not only provide habitat and food for organisms but also play an important role in regulating environmental elements such as water, atmosphere, and soil. The quality of forest waters directly affects the health and stability of aquatic ecosystems. Chemical oxygen demand (COD) is commonly used to assess the concentration of organic matter and the pollution status of water bodies, which is helpful in assessing the impact of human activities on forest ecosystems. To effectively measure the COD value, water samples were prepared from Purple Mountain in Nanjing and nearby rivers and lakes. Using ultraviolet–visible (UV–vis) and fluorescence (FLU) spectroscopy combined with data fusion, the COD values of the forest water were accurately measured. Due to the large dimensionality of spectral data, the successive projections algorithm (SPA) and competitive adaptive reweighted sampling (CARS) were applied to the selection of characteristic wavelengths. By establishing a discriminant model for single-level data and using the voting mechanism to fuse the output results of different models, a relatively high determination coefficient (R2) of 0.9932 and a low root-mean-square error (RMSE) of 0.4582 were obtained based on the decision-level data fusion model. Compared with the single-spectrum and feature-level fusion models, the decision-level fusion scheme achieves an efficient, comprehensive, and accurate quantification of the water COD value. This study has important applications in forest protection, water resources management, sewage treatment, and the food processing field. Full article
(This article belongs to the Special Issue Artificial Intelligence and Machine Learning Applications in Forestry)
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18 pages, 8042 KiB  
Article
Tree Species Classification Based on PointNet++ and Airborne Laser Survey Point Cloud Data Enhancement
by Zhongmou Fan, Jinhuang Wei, Ruiyang Zhang and Wenxuan Zhang
Forests 2023, 14(6), 1246; https://doi.org/10.3390/f14061246 - 15 Jun 2023
Cited by 5 | Viewed by 2455
Abstract
Compared with ground-based light detection and ranging (LiDAR) data, the differential distribution of the quantity and quality of point cloud data from airborne LiDAR poses difficulties for tree species classification. To verify the feasibility of using the PointNet++ algorithm for point cloud tree [...] Read more.
Compared with ground-based light detection and ranging (LiDAR) data, the differential distribution of the quantity and quality of point cloud data from airborne LiDAR poses difficulties for tree species classification. To verify the feasibility of using the PointNet++ algorithm for point cloud tree species classification with airborne LiDAR data, we selected 11 tree species from the Minjiang River Estuary Wetland Park in Fuzhou City and Sanjiangkou Ecological Park. Training and testing sets were constructed through pre-processing and segmentation, and direct and enhanced down-sampling methods were used for tree species classification. Experiments were conducted to adjust the hyperparameters of the proposed algorithm. The optimal hyperparameter settings used the multi-scale sampling and grouping (MSG) method, down-sampling of the point cloud to 2048 points after enhancement, and a batch size of 16, which resulted in 91.82% classification accuracy. PointNet++ could be used for tree species classification using airborne LiDAR data with an insignificant impact on point cloud quality. Considering the differential distribution of the point cloud quantity, enhanced down-sampling yields improved the classification results compared to direct down-sampling. The MSG classification method outperformed the simplified sampling and grouping classification method, and the number of epochs and batch size did not impact the results. Full article
(This article belongs to the Special Issue Artificial Intelligence and Machine Learning Applications in Forestry)
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25 pages, 9598 KiB  
Article
Leaves and Twigs Image Recognition Based on Deep Learning and Combined Classifier Algorithms
by Xiaobo Sun, Lin Xu, Yufeng Zhou and Yongjun Shi
Forests 2023, 14(6), 1083; https://doi.org/10.3390/f14061083 - 24 May 2023
Cited by 8 | Viewed by 1802
Abstract
In recent years, the automatic recognition of tree species based on images taken by digital cameras has been widely applied. However, many problems still exist, such as insufficient tree species image acquisition, uneven distribution of image categories, and low recognition accuracy. Tree leaves [...] Read more.
In recent years, the automatic recognition of tree species based on images taken by digital cameras has been widely applied. However, many problems still exist, such as insufficient tree species image acquisition, uneven distribution of image categories, and low recognition accuracy. Tree leaves can be used to differentiate and classify tree species due to their cognitive signatures in color, vein texture, shape contour, and edge serration. Moreover, the way the leaves are arranged on the twigs has strong characteristics. In this study, we first built an image dataset of 21 tree species based on the features of the twigs and leaves. The tree species feature dataset was divided into the training set and test set, with a ratio of 8:2. Feature extraction was performed after training the convolutional neural network (CNN) using the k-fold cross-validation (K-Fold–CV) method, and tree species classification was performed with classifiers. To improve the accuracy of tree species identification, we combined three improved CNN models with three classifiers. Evaluation indicators show that the overall accuracy of the designed composite model was 1.76% to 9.57% higher than other CNN models. Furthermore, in the MixNet XL CNN model, combined with the K-nearest neighbors (KNN) classifier, the highest overall accuracy rate was obtained at 99.86%. In the experiment, the Grad-CAM heatmap was used to analyze the distribution of feature regions that play a key role in classification decisions. Observation of the Grad-CAM heatmap illustrated that the main observation area of SE-ResNet50 was the most accurately positioned, and was mainly concentrated in the interior of small twigs and leaflets. Our research showed that modifying the training method and classification module of the CNN model and combining it with traditional classifiers to form a composite model can effectively improve the accuracy of tree species recognition. Full article
(This article belongs to the Special Issue Artificial Intelligence and Machine Learning Applications in Forestry)
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14 pages, 9814 KiB  
Article
Integrated Learning-Based Pest and Disease Detection Method for Tea Leaves
by Yinkai Wang, Renjie Xu, Di Bai and Haifeng Lin
Forests 2023, 14(5), 1012; https://doi.org/10.3390/f14051012 - 14 May 2023
Cited by 16 | Viewed by 3088
Abstract
Currently, the detection of tea pests and diseases remains a challenging task due to the complex background and the diverse spot patterns of tea leaves. Traditional methods of tea pest detection mainly rely on the experience of tea farmers and experts in specific [...] Read more.
Currently, the detection of tea pests and diseases remains a challenging task due to the complex background and the diverse spot patterns of tea leaves. Traditional methods of tea pest detection mainly rely on the experience of tea farmers and experts in specific fields, which is complex and inefficient and can easily lead to misclassification and omission of diseases. Currently, a single detection model is often used for tea pest and disease identification; however, its learning and perception capabilities are insufficient to complete target detection of pests and diseases in complex tea garden environments. To address the problem that existing target detection algorithms are difficult to identify in the complex environment of tea plantations, an integrated learning-based pest detection method is proposed to detect one disease (Leaf blight) and one pest (Apolygus lucorμm), and to perform adaptive learning and extraction of tea pests and diseases. In this paper, the YOLOv5 weakly supervised model is selected, and it is found through experiments that the GAM attention mechanism’s introduction on the basis of YOLOv5’s network can better identify the Apolygus lucorμm; the introduction of CBAM attention mechanism significantly enhances the effect of identifying Leaf blight. After integrating the two modified YOLOv5 models, the prediction results were processed using the weighted box fusion (WBF) algorithm. The integrated model made full use of the complementary advantages among the models, improved the feature extraction ability of the model and enhanced the detection capability of the model. The experimental findings demonstrate that the tea pest detection algorithm effectively enhances the detection ability of tea pests and diseases with an average accuracy of 79.3%. Compared with the individual models, the average accuracy improvement was 8.7% and 9.6%, respectively. The integrated algorithm, which may serve as a guide for tea disease diagnosis in field environments, has improved feature extraction capabilities, can extract more disease feature information, and better balances the model’s recognition accuracy and model complexity. Full article
(This article belongs to the Special Issue Artificial Intelligence and Machine Learning Applications in Forestry)
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23 pages, 6161 KiB  
Article
Omni-Dimensional Dynamic Convolution Meets Bottleneck Transformer: A Novel Improved High Accuracy Forest Fire Smoke Detection Model
by Jingjing Qian, Ji Lin, Di Bai, Renjie Xu and Haifeng Lin
Forests 2023, 14(4), 838; https://doi.org/10.3390/f14040838 - 19 Apr 2023
Cited by 18 | Viewed by 3069
Abstract
The frequent occurrence of forest fires in recent years has not only seriously damaged the forests’ ecological environments but also threatened the safety of public life and property. Smoke, as the main manifestation of the flame before it is produced, has the advantage [...] Read more.
The frequent occurrence of forest fires in recent years has not only seriously damaged the forests’ ecological environments but also threatened the safety of public life and property. Smoke, as the main manifestation of the flame before it is produced, has the advantage of a wide diffusion range that is not easily obscured. Therefore, timely detection of forest fire smoke with better real-time detection for early warnings of forest fires wins valuable time for timely firefighting and also has great significance and applications for the development of forest fire detection systems. However, existing forest fire smoke detection methods still have problems, such as low detection accuracy, slow detection speed, and difficulty detecting smoke from small targets. In order to solve the aforementioned problems and further achieve higher accuracy in detection, this paper proposes an improved, new, high-accuracy forest fire detection model, the OBDS. Firstly, to address the problem of insufficient extraction of effective features of forest fire smoke in complex forest environments, this paper introduces the SimAM attention mechanism, which makes the model pay more attention to the feature information of forest fire smoke and suppresses the interference of non-targeted background information. Moreover, this paper introduces Omni-Dimensional Dynamic Convolution instead of static convolution and adaptively and dynamically adjusts the weights of the convolution kernel, which enables the network to better extract the key features of forest fire smoke of different shapes and sizes. In addition, to address the problem that traditional convolutional neural networks are not capable of capturing global forest fire smoke feature information, this paper introduces the Bottleneck Transformer Net (BoTNet) to fully extract global feature information and local feature information of forest fire smoke images while improving the accuracy of small target forest fire target detection of smoke, effectively reducing the model’s computation, and improving the detection speed of model forest fire smoke. Finally, this paper introduces the decoupling head to further improve the detection accuracy of forest fire smoke and speed up the convergence of the model. Our experimental results show that the model OBDS for forest fire smoke detection proposed in this paper is significantly better than the mainstream model, with a computational complexity of 21.5 GFLOPs (giga floating-point operations per second), an improvement of 4.31% compared with the YOLOv5 (YOLO, you only look once) model [email protected], reaching 92.10%, and an FPS (frames per second) of 54, which is conducive to the realization of early warning of forest fires. Full article
(This article belongs to the Special Issue Artificial Intelligence and Machine Learning Applications in Forestry)
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13 pages, 5095 KiB  
Article
An Improved Forest Fire and Smoke Detection Model Based on YOLOv5
by Junhui Li, Renjie Xu and Yunfei Liu
Forests 2023, 14(4), 833; https://doi.org/10.3390/f14040833 - 18 Apr 2023
Cited by 19 | Viewed by 3733
Abstract
Forest fires are destructive and rapidly spreading, causing great harm to forest ecosystems and humans. Deep learning techniques can adaptively learn and extract features of forest fires and smoke. However, the complex backgrounds and different forest fire and smoke features in captured forest [...] Read more.
Forest fires are destructive and rapidly spreading, causing great harm to forest ecosystems and humans. Deep learning techniques can adaptively learn and extract features of forest fires and smoke. However, the complex backgrounds and different forest fire and smoke features in captured forest fire images make detection difficult. Facing the complex background of forest fire smoke, it is difficult for traditional machine learning methods to design a general feature extraction module for feature extraction. Deep learning methods are effective in many fields, so this paper improves on the You Only Look Once v5 (YOLOv5s) model, and the improved model has better detection performance for forest fires and smoke. First, a coordinate attention (CA) model is integrated into the YOLOv5 model to highlight fire smoke targets and improve the identifiability of different smoke features. Second, we replaced YOLOv5s original spatial pyramidal ensemble fast (SPPF) module with a receptive field block (RFB) module to enable better focus on the global information of different fires. Third, the path aggregation network (PANet) of the neck structure in the YOLOv5s model is improved to a bi-directional feature pyramid network (Bi-FPN). Compared with the YOLOv5 model, our improved forest fire and smoke detection model at [email protected] improves by 5.1%. Full article
(This article belongs to the Special Issue Artificial Intelligence and Machine Learning Applications in Forestry)
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24 pages, 8473 KiB  
Article
TSBA-YOLO: An Improved Tea Diseases Detection Model Based on Attention Mechanisms and Feature Fusion
by Ji Lin, Di Bai, Renjie Xu and Haifeng Lin
Forests 2023, 14(3), 619; https://doi.org/10.3390/f14030619 - 20 Mar 2023
Cited by 27 | Viewed by 4496
Abstract
Tea diseases have a significant impact on the yield and quality of tea during the growth of tea trees. The shape and scale of tea diseases are variable, and the tea disease targets are usually small, with the intelligent detection processes of tea [...] Read more.
Tea diseases have a significant impact on the yield and quality of tea during the growth of tea trees. The shape and scale of tea diseases are variable, and the tea disease targets are usually small, with the intelligent detection processes of tea diseases also easily disturbed by the complex background of the growing region. In addition, some tea diseases are concentrated in the entire area of the leaves, needing to be inferred from global information. Common target detection models are difficult to solve these problems. Therefore, we proposed an improved tea disease detection model called TSBA-YOLO. We use the dataset of tea diseases collected at the Maoshan Tea Factory in China. The self-attention mechanism was used to enhance the ability of the model to obtain global information on tea diseases. The BiFPN feature fusion network and adaptively spatial feature fusion (ASFF) technology were used to improve the multiscale feature fusion of tea diseases and enhance the ability of the model to resist complex background interference. We integrated the Shuffle Attention mechanism to solve the problem of difficult identifications of small-target tea diseases. In addition, we used data-enhancement methods and transfer learning to expand the dataset and relocate the parameters learned from other plant disease datasets to enhance tea diseases detection. Finally, SIoU was used to further improve the accuracy of the regression. The experimental results show that the proposed model is good at solving a series of problems encountered in the intelligent recognition of tea diseases. The detection accuracy is ahead of the mainstream target detection models, and the detection speed reaches the real-time level. Full article
(This article belongs to the Special Issue Artificial Intelligence and Machine Learning Applications in Forestry)
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