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Article: A Forest Fire Identification System Based on Weighted Fusion Algorithm

Qian, Jingjing / Lin, Haifeng

Forests. 2022 Aug. 16, v. 13, no. 8

2022

Abstract: The occurrence of forest fires causes serious damage to ecological diversity and the safety of people’s property and life. However, due to the complex forest environment, the changeable shape of forest fires, and the uncertainty of flame color and ... ...

Abstract	The occurrence of forest fires causes serious damage to ecological diversity and the safety of people’s property and life. However, due to the complex forest environment, the changeable shape of forest fires, and the uncertainty of flame color and texture, forest fire detection becomes very difficult. Traditional image processing methods rely heavily on artificial features and are not generally applicable to different forest fire scenes. In order to solve the problem of inaccurate forest fire recognition caused by the manual extraction of features, some scholars use deep learning technology to adaptively learn and extract forest fire features, but they often use a single target detection model, and their lack of learning and perception makes it difficult for them to accurately identify forest fires in a complex forest fire environment. Therefore, in order to overcome the shortcomings of the manual extraction of features and achieve a higher accuracy of forest fire recognition, this paper proposes an algorithm based on weighted fusion to identify forest fire sources in different scenarios, fuses two independent weakly supervised models Yolov5 and EfficientDet, completes the training and prediction of data sets in parallel, and uses the weighted boxes fusion algorithm (WBF) to process the prediction results to obtain the fusion frame. Finally, the model is evaluated by Microsoft COCO standard. Experimental results show that compared with Yolov5 and EfficientDet, the proposed Y4SED improves the detection performance by 2.5% to 4.5%. The fused algorithm proposed in this paper has better feature extraction ability, can extract more forest fire feature information, and better balances the recognition accuracy and complexity of the model, which provides a reference for forest fire target detection in the real environment.
Keywords	algorithms ; color ; computer software ; fire detection ; forest fires ; forests ; models ; prediction ; texture ; uncertainty
Language	English
Dates of publication	2022-0816
Publishing place	Multidisciplinary Digital Publishing Institute
Document type	Article
ZDB-ID	2527081-3
ISSN	1999-4907
ISSN	1999-4907
DOI	10.3390/f13081301
Database	NAL-Catalogue (AGRICOLA)

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Article ; Online: Integrated Learning-Based Pest and Disease Detection Method for Tea Leaves

Wang, Yinkai / Xu, Renjie / Bai, Di / Lin, Haifeng

Forests. 2023 May 14, v. 14, no. 5

2023

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 ...

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 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.
Keywords	algorithms ; disease detection ; disease diagnosis ; gardens ; leaf blight ; models ; pests ; prediction ; tea
Language	English
Dates of publication	2023-0514
Publishing place	Multidisciplinary Digital Publishing Institute
Document type	Article ; Online
ZDB-ID	2527081-3
ISSN	1999-4907
ISSN	1999-4907
DOI	10.3390/f14051012
Database	NAL-Catalogue (AGRICOLA)

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Article ; Online: A Semi-Supervised Method for Real-Time Forest Fire Detection Algorithm Based on Adaptively Spatial Feature Fusion

Lin, Ji / Lin, Haifeng / Wang, Fang

Forests. 2023 Feb. 11, v. 14, no. 2

2023

Abstract: Forest fires occur frequently around the world, causing serious economic losses and human casualties. Deep learning techniques based on convolutional neural networks (CNN) are widely used in the intelligent detection of forest fires. However, CNN-based ... ...

Abstract	Forest fires occur frequently around the world, causing serious economic losses and human casualties. Deep learning techniques based on convolutional neural networks (CNN) are widely used in the intelligent detection of forest fires. However, CNN-based forest fire target detection models lack global modeling capabilities and cannot fully extract global and contextual information about forest fire targets. CNNs also pay insufficient attention to forest fires and are vulnerable to the interference of invalid features similar to forest fires, resulting in low accuracy of fire detection. In addition, CNN-based forest fire target detection models require a large number of labeled datasets. Manual annotation is often used to annotate the huge amount of forest fire datasets; however, this takes a lot of time. To address these problems, this paper proposes a forest fire detection model, TCA-YOLO, with YOLOv5 as the basic framework. Firstly, we combine the Transformer encoder with its powerful global modeling capability and self-attention mechanism with CNN as a feature extraction network to enhance the extraction of global information on forest fire targets. Secondly, in order to enhance the model’s focus on forest fire targets, we integrate the Coordinate Attention (CA) mechanism. CA not only acquires inter-channel information but also considers direction-related location information, which helps the model to better locate and identify forest fire targets. Integrated adaptively spatial feature fusion (ASFF) technology allows the model to automatically filter out useless information from other layers and efficiently fuse features to suppress the interference of complex backgrounds in the forest area for detection. Finally, semi-supervised learning is used to save a large amount of manual labeling effort. The experimental results show that the average accuracy of TCA-YOLO improves by 5.3 compared with the unimproved YOLOv5. TCA-YOLO also outperformed in detecting forest fire targets in different scenarios. The ability of TCA-YOLO to extract global information on forest fire targets was much improved. Additionally, it could locate forest fire targets more accurately. TCA-YOLO misses fewer forest fire targets and is less likely to be interfered with by forest fire-like targets. TCA-YOLO is also more focused on forest fire targets and better at small-target forest fire detection. FPS reaches 53.7, which means that the detection speed meets the requirements of real-time forest fire detection.
Keywords	algorithms ; data collection ; fire detection ; forest fires ; forests ; humans ; models
Language	English
Dates of publication	2023-0211
Publishing place	Multidisciplinary Digital Publishing Institute
Document type	Article ; Online
ZDB-ID	2527081-3
ISSN	1999-4907
ISSN	1999-4907
DOI	10.3390/f14020361
Database	NAL-Catalogue (AGRICOLA)

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Article ; Online: TSBA-YOLO: An Improved Tea Diseases Detection Model Based on Attention Mechanisms and Feature Fusion

Lin, Ji / Bai, Di / Xu, Renjie / Lin, Haifeng

Forests. 2023 Mar. 20, v. 14, no. 3

2023

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 ... ...

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 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.
Keywords	beverage industry ; data collection ; disease detection ; models ; tea ; China
Language	English
Dates of publication	2023-0320
Publishing place	Multidisciplinary Digital Publishing Institute
Document type	Article ; Online
ZDB-ID	2527081-3
ISSN	1999-4907
ISSN	1999-4907
DOI	10.3390/f14030619
Database	NAL-Catalogue (AGRICOLA)

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Article ; Online: YOLO-Tea: A Tea Disease Detection Model Improved by YOLOv5

Xue, Zhenyang / Xu, Renjie / Bai, Di / Lin, Haifeng

Forests. 2023 Feb. 17, v. 14, no. 2

2023

Abstract: Diseases and insect pests of tea leaves cause huge economic losses to the tea industry every year, so the accurate identification of them is significant. Convolutional neural networks (CNNs) can automatically extract features from images of tea leaves ... ...

Abstract	Diseases and insect pests of tea leaves cause huge economic losses to the tea industry every year, so the accurate identification of them is significant. Convolutional neural networks (CNNs) can automatically extract features from images of tea leaves suffering from insect and disease infestation. However, photographs of tea tree leaves taken in a natural environment have problems such as leaf shading, illumination, and small-sized objects. Affected by these problems, traditional CNNs cannot have a satisfactory recognition performance. To address this challenge, we propose YOLO-Tea, an improved model based on You Only Look Once version 5 (YOLOv5). Firstly, we integrated self-attention and convolution (ACmix), and convolutional block attention module (CBAM) to YOLOv5 to allow our proposed model to better focus on tea tree leaf diseases and insect pests. Secondly, to enhance the feature extraction capability of our model, we replaced the spatial pyramid pooling fast (SPPF) module in the original YOLOv5 with the receptive field block (RFB) module. Finally, we reduced the resource consumption of our model by incorporating a global context network (GCNet). This is essential especially when the model operates on resource-constrained edge devices. When compared to YOLOv5s, our proposed YOLO-Tea improved by 0.3%–15.0% over all test data. YOLO-Tea’s AP0.5, APTLB, and APGMB outperformed Faster R-CNN and SSD by 5.5%, 1.8%, 7.0% and 7.7%, 7.8%, 5.2%. YOLO-Tea has shown its promising potential to be applied in real-world tree disease detection systems.
Keywords	disease detection ; industry ; insects ; leaves ; lighting ; models ; tea ; trees
Language	English
Dates of publication	2023-0217
Publishing place	Multidisciplinary Digital Publishing Institute
Document type	Article ; Online
ZDB-ID	2527081-3
ISSN	1999-4907
ISSN	1999-4907
DOI	10.3390/f14020415
Database	NAL-Catalogue (AGRICOLA)

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Article ; Online: The complete chloroplast genome and phylogenetic analysis of

Sun, Changlan / Lin, Haifeng

Mitochondrial DNA. Part B, Resources

2021 Volume 6, Issue 10, Page(s) 2926–2927

Abstract: Citrus, which is widely cultivated in tropical and subtropical climates, is one of the most important crops in the world. Here, we assembled and annotated the complete chloroplast genome ... ...

Abstract	Citrus, which is widely cultivated in tropical and subtropical climates, is one of the most important crops in the world. Here, we assembled and annotated the complete chloroplast genome of
Language	English
Publishing date	2021-09-13
Publishing country	England
Document type	Journal Article
ISSN	2380-2359
ISSN (online)	2380-2359
DOI	10.1080/23802359.2021.1972860
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article ; Online: The complete chloroplast genome of

Xu, Qinghua / Lin, Haifeng

Mitochondrial DNA. Part B, Resources

2021 Volume 6, Issue 9, Page(s) 2752–2753

Abstract: Sisymbrium ... ...

Abstract	Sisymbrium altissimum
Language	English
Publishing date	2021-08-24
Publishing country	England
Document type	Journal Article
ISSN	2380-2359
ISSN (online)	2380-2359
DOI	10.1080/23802359.2021.1967800
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article ; Online: Introducing Br, K, and cyano group into carbon nitride for efficient photocatalytic hydrogen peroxide production then in situ tetracycline mineralization.

Fang, Huawei / Zhang, Yu / Xu, Jixiang / Xing, Jun / Lin, Haifeng / Wang, Lei

Journal of colloid and interface science

2024 Volume 667, Page(s) 433–440

Abstract: In this work, Br, K-doped and cyano group-rich carbon nitride (CN) were prepared via pyrolysis of molten urea and 6-Bromopyridine-3-carbaldehyde, followed by re-calcination with potassium thiocyanate. The hydrogen peroxide ( ... ...

Abstract	In this work, Br, K-doped and cyano group-rich carbon nitride (CN) were prepared via pyrolysis of molten urea and 6-Bromopyridine-3-carbaldehyde, followed by re-calcination with potassium thiocyanate. The hydrogen peroxide (H
Language	English
Publishing date	2024-04-16
Publishing country	United States
Document type	Journal Article
ZDB-ID	241597-5
ISSN	1095-7103 ; 0021-9797
ISSN (online)	1095-7103
ISSN	0021-9797
DOI	10.1016/j.jcis.2024.04.104
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article: Multi-Scale Feature Aggregation Network for Water Area Segmentation

Hu, Kai / Li, Meng / Xia, Min / Lin, Haifeng

Remote Sensing. 2022 Jan. 03, v. 14, no. 1

2022

Abstract: Water area segmentation is an important branch of remote sensing image segmentation, but in reality, most water area images have complex and diverse backgrounds. Traditional detection methods cannot accurately identify small tributaries due to incomplete ...

Abstract	Water area segmentation is an important branch of remote sensing image segmentation, but in reality, most water area images have complex and diverse backgrounds. Traditional detection methods cannot accurately identify small tributaries due to incomplete mining and insufficient utilization of semantic information, and the edge information of segmentation is rough. To solve the above problems, we propose a multi-scale feature aggregation network. In order to improve the ability of the network to process boundary information, we design a deep feature extraction module using a multi-scale pyramid to extract features, combined with the designed attention mechanism and strip convolution, extraction of multi-scale deep semantic information and enhancement of spatial and location information. Then, the multi-branch aggregation module is used to interact with different scale features to enhance the positioning information of the pixels. Finally, the two high-performance branches designed in the Feature Fusion Upsample module are used to deeply extract the semantic information of the image, and the deep information is fused with the shallow information generated by the multi-branch module to improve the ability of the network. Global and local features are used to determine the location distribution of each image category. The experimental results show that the accuracy of the segmentation method in this paper is better than that in the previous detection methods, and has important practical significance for the actual water area segmentation.
Keywords	branches ; branching ; design ; exhibitions ; extracts ; image analysis ; information processing ; mining ; remote sensing ; spatial data
Language	English
Dates of publication	2022-0103
Publishing place	Multidisciplinary Digital Publishing Institute
Document type	Article
ZDB-ID	2513863-7
ISSN	2072-4292
ISSN	2072-4292
DOI	10.3390/rs14010206
Database	NAL-Catalogue (AGRICOLA)

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Article ; Online: FCDM: An Improved Forest Fire Classification and Detection Model Based on YOLOv5

Xue, Qilin / Lin, Haifeng / Wang, Fang

Forests. 2022 Dec. 12, v. 13, no. 12

2022

Abstract: Intense, large-scale forest fires are damaging and very challenging to control. Locations, where various types of fire behavior occur, vary depending on environmental factors. According to the burning site of forest fires and the degree of damage, this ... ...

Abstract	Intense, large-scale forest fires are damaging and very challenging to control. Locations, where various types of fire behavior occur, vary depending on environmental factors. According to the burning site of forest fires and the degree of damage, this paper considers the classification and identification of surface fires and canopy fires. Deep learning-based forest fire detection uses convolutional neural networks to automatically extract multidimensional features of forest fire images with high detection accuracy. To accurately identify different forest fire types in complex backgrounds, an improved forest fire classification and detection model (FCDM) based on YOLOv5 is presented in this paper, which uses image-based data. By changing the YOLOv5 bounding box loss function to SIoU Loss and introducing directionality in the cost of the loss function to achieve faster convergence, the training and inference of the detection algorithm are greatly improved. The Convolutional Block Attention Module (CBAM) is introduced in the network to fuse channel attention and spatial attention to improve the classification recognition accuracy. The Path Aggregation Network (PANet) layer in the YOLOv5 algorithm is improved into a weighted Bi-directional Feature Pyramid Network (BiFPN) to fuse and filter forest fire features of different dimensions to improve the detection of different types of forest fires. The experimental results show that this improved forest fire classification and identification model outperforms the YOLOv5 algorithm in both detection performances. The mAP@0.5 of fire detection, surface fire detection, and canopy fire detection was improved by 3.9%, 4.0%, and 3.8%, respectively. Among them, the mAP@0.5 of surface fire reached 83.1%, and the canopy fire detection reached 90.6%. This indicates that the performance of our proposed improved model has been effectively improved and has some application prospects in forest fire classification and recognition.
Keywords	algorithms ; canopy ; fire behavior ; fire detection ; forest fires ; forests ; models
Language	English
Dates of publication	2022-1212
Publishing place	Multidisciplinary Digital Publishing Institute
Document type	Article ; Online
ZDB-ID	2527081-3
ISSN	1999-4907
ISSN	1999-4907
DOI	10.3390/f13122129
Database	NAL-Catalogue (AGRICOLA)

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