Deep learning combined with Balance Mixup for the detection of pine wilt disease using multispectral imagery

Rao, Deshen; Zhang, Derong; Lu, Huishan; Yang, Yong; Qiu, Yi; Ding, Menghan; Yu, Xinjie

doi:10.1016/j.compag.2023.107778

Cited by 4 publications

(2 citation statements)

References 27 publications

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“…The number of sample data ob-tained from the experiment was not enough to obtain a good training model and prediction results. The existing data set was expanded using DA (Data Augmentation) [42,43], adding random noise, random fluctuations, etc., to generate some extended data containing the existing data features, thereby increasing the number of samples, eliminating the common machine learning problem of over-fitting caused by a small data set training model [44], and improving the model effect and prediction results. The Mixup enhancement algorithm in many DA methods is introduced in [45,46].…”

Section: Data Augmentationmentioning

confidence: 99%

Multi-Objective Prediction of the Sound Insulation Performance of a Vehicle Body System Using Multiple Kernel Learning–Support Vector Regression

Sun,

Dai,

et al. 2024

Electronics

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The sound insulation performance of an electric vehicle’s body system serves as a critical metric for evaluating the noise, vibration, and harshness (NVH) quality of the vehicle. The accurate and efficient prediction of sound insulation performance is foundational for undertaking noise reduction design and optimization. Current engineering practices predominantly rely on Computer-Aided Engineering (CAE) methodologies to address this challenge. However, inherent shortcomings such as low modeling efficiency and difficulty in ensuring prediction accuracy often characterize these approaches. In an effort to overcome these limitations, we propose a decomposition framework for predicting the sound insulation performance of the electric vehicle body system. This framework is established based on a comprehensive analysis of the noise transmission paths within the system. Subsequently, the support vector regression (SVR) method is introduced to construct a machine learning model specifically designed for predicting the sound insulation performance of the body system. This approach aims to mitigate the inherent weaknesses associated with the conventional CAE processes using a ‘data-driven’ paradigm. Furthermore, the Multiple Kernel Learning (MKL) method is used to enhance the processing efficacy of the SVR model. The proposed method is validated using practical application and testing on a specific electric vehicle. The results demonstrate commendable performance in terms of prediction accuracy and robustness. This research contributes to advancing the field by presenting a more effective and reliable approach to predicting the sound insulation performance of electric vehicle body systems, offering valuable insights for noise reduction strategies and optimization efforts in the automotive industry.

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Section: Data Augmentationmentioning

confidence: 99%

Multi-Objective Prediction of the Sound Insulation Performance of a Vehicle Body System Using Multiple Kernel Learning–Support Vector Regression

Sun,

Dai,

et al. 2024

Electronics

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“…It is a common practice to employ diverse methods to enhance remote sensing images, such as mirroring, flipping, adding noise, rotating, scaling, etc. [16][17][18]. Cai et al [19] proposed an effective data augmentation method based on Sentinel-2 satellite data and UAV images to efficiently detect PWD.…”

Section: Introductionmentioning

confidence: 99%

Pine-YOLO: A Method for Detecting Pine Wilt Disease in Unmanned Aerial Vehicle Remote Sensing Images

Yao,

Song,

Chen

et al. 2024

Forests

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Pine wilt disease is a highly contagious forest quarantine ailment that spreads rapidly. In this study, we designed a new Pine-YOLO model for pine wilt disease detection by incorporating Dynamic Snake Convolution (DSConv), the Multidimensional Collaborative Attention Mechanism (MCA), and Wise-IoU v3 (WIoUv3) into a YOLOv8 network. Firstly, we collected UAV images from Beihai Forest and Linhai Park in Weihai City to construct a dataset via a sliding window method. Then, we used this dataset to train and test Pine-YOLO. We found that DSConv adaptively focuses on fragile and curved local features and then enhances the perception of delicate tubular structures in discolored pine branches. MCA strengthens the attention to the specific features of pine trees, helps to enhance the representational capability, and improves the generalization to diseased pine tree recognition in variable natural environments. The bounding box loss function has been optimized to WIoUv3, thereby improving the overall recognition accuracy and robustness of the model. The experimental results reveal that our Pine-YOLO model achieved the following values across various evaluation metrics: MAP@0.5 at 90.69%, mAP@0.5:0.95 at 49.72%, precision at 91.31%, recall at 85.72%, and F1-score at 88.43%. These outcomes underscore the high effectiveness of our model. Therefore, our newly designed Pine-YOLO perfectly addresses the disadvantages of the original YOLO network, which helps to maintain the health and stability of the ecological environment.

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Using machine learning algorithms to cluster and classify stone pine (Pinus pinea L.) populations based on seed and seedling characteristics

Caliskan,

Kartal,

Balekoglu

et al. 2024

Eur J Forest Res

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The phenotype of a woody plant represents its unique morphological properties. Population discrimination and individual classification are crucial for breeding populations and conserving genetic diversity. Machine Learning (ML) algorithms are gaining traction as powerful tools for predicting phenotypes. The present study is focused on classifying and clustering the seeds and seedlings in terms of morphological characteristics using ML algorithms. In addition, the k-means algorithm is used to determine the ideal number of clusters. The results obtained from the k-means algorithm were then compared with reality. The best classification performance achieved by the Random Forest algorithm was an accuracy of 0.648 and an F1-Score of 0.658 for the seed traits. Also, the best classification performance for stone pine seedlings was observed for the k-Nearest Neighbors algorithm (k = 18), for which the accuracy and F1-Score were 0.571 and 0.582, respectively. The best clustering performance was achieved with k = 2 for the seed (average Silhouette index = 0.48) and seedling (average Silhouette Index = 0.51) traits. According to the principal component analysis, two dimensions accounted for 97% and 63% of the traits of seeds and seedlings, respectively. The most important features between the seed and seedling traits were cone weight and bud set, respectively. This study will provide a foundation and motivation for future efforts in forest management practices, particularly regarding reforestation, yield optimization, and breeding programs.

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Deep learning combined with Balance Mixup for the detection of pine wilt disease using multispectral imagery

Cited by 4 publications

References 27 publications

Multi-Objective Prediction of the Sound Insulation Performance of a Vehicle Body System Using Multiple Kernel Learning–Support Vector Regression

Multi-Objective Prediction of the Sound Insulation Performance of a Vehicle Body System Using Multiple Kernel Learning–Support Vector Regression

Pine-YOLO: A Method for Detecting Pine Wilt Disease in Unmanned Aerial Vehicle Remote Sensing Images

Using machine learning algorithms to cluster and classify stone pine (Pinus pinea L.) populations based on seed and seedling characteristics

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