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学者姓名：翁海勇
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本实用新型公开了一种茶叶视觉分析剔除装置，包括支架、输送平台、控制系统、摄像分析系统、升降结构、吸附结构和分离结构。所述控制系统、摄像分析系统、升降结构和吸附结构之间电性连接，所述升降结构设置在所述支架上，所述吸附结构包括负压吸附源和吸附枪，所述吸附枪设置在所述升降结构上，所述升降结构能驱动所述吸附枪垂直于所述输送平台升降，其特征在于，还包括分离结构，所述分离结构包括分离筒，所述分离筒套设在所述吸附枪外，当所述升降结构带动所述吸附枪下降时，所述分离筒的端面能先于所述吸附枪压紧在所述输送平台的表面。本茶叶视觉分析剔除装置，可减少分析剔除过程中合格嫩芽误剔除数量，从而降低生产成本。
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| GB/T 7714 | 叶大鹏 , 高宇轩 , 翁海勇 et al. 一种茶叶视觉分析剔除装置 : CN202420828912.7[P]. | 2024-04-19 . |
| MLA | 叶大鹏 et al. "一种茶叶视觉分析剔除装置" : CN202420828912.7. | 2024-04-19 . |
| APA | 叶大鹏 , 高宇轩 , 翁海勇 , 陈明夏 , 黄德耀 . 一种茶叶视觉分析剔除装置 : CN202420828912.7. | 2024-04-19 . |
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Dissolved Oxygen (DO) is a pivotal indicator for sustaining the vitality and productivity of aquatic ecosystems. To empower sophisticated aquaculture management, a novel approach of feature reconstruction integrated with deep neural networks was proposed to predict the future DO trends within fish pond aquaculture with exceptional precision and reliability. The time series data of water quality factors including pH, water temperature, conductivity, turbidity, air temperature, and humidity were obtained synchronously by sensing devices. The sequence of Spearman correlation analysis (SCA), variational mode decomposition (VMD), and convolutional neural networks (CNN) formed the feature reconstruction method (SCA-VMD-CNN, SVC) for feature optimization, decomposition, and spatiotemporal feature extraction, addressing the nonlinear and temporal features of DO data in aquaculture. The Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU) networks were established based on the SVC features for multi-step predicting of DO. Compared with other state-of-the-art methods, the results showed that SVC effectively improved the accuracy of the DNNs by 16.8 %similar to 19.5 % for multi-step prediction of future DO trends within fish pond aquaculture. The SVC-BiGRU obtained the highest predictive performances with R-2 of 0.962, 0.934, 0.940 for predicting 1-step, 2-step, and 3-step DO content in the next 15, 30, and 45 min. Our proposed methodology paves a pathway toward dynamic monitoring of DO trends, aimed at improving aquaculture efficiency and reducing risks. It may play an essential role in the near future for time-series analysis in precision aquaculture.
Keyword ：
Deep neural networks Deep neural networks Dissolved oxygen Dissolved oxygen Fish pond aquaculture Fish pond aquaculture Multi-step prediction Multi-step prediction Time series feature reconstruction Time series feature reconstruction
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| GB/T 7714 | Jiang, Yilun , Zhang, Lintong , Wang, Chuxin et al. Multi-step prediction of dissolved oxygen in fish pond aquaculture using feature reconstruction-based deep neural network [J]. | COMPUTERS AND ELECTRONICS IN AGRICULTURE , 2025 , 232 . |
| MLA | Jiang, Yilun et al. "Multi-step prediction of dissolved oxygen in fish pond aquaculture using feature reconstruction-based deep neural network" . | COMPUTERS AND ELECTRONICS IN AGRICULTURE 232 (2025) . |
| APA | Jiang, Yilun , Zhang, Lintong , Wang, Chuxin , Chen, Linjie , Zhang, Wenqing , Weng, Haiyong et al. Multi-step prediction of dissolved oxygen in fish pond aquaculture using feature reconstruction-based deep neural network . | COMPUTERS AND ELECTRONICS IN AGRICULTURE , 2025 , 232 . |
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Liquid culture is a nutrient-rich liquid medium used to grow and study microorganisms, yet it is highly vulnerable to contamination, which only can be observed at the symptomatic stage when turbidity appears, leaving limited opportunities for timely intervention. Optical chemometrics has been extensively explored for determination of contamination by analyzing the absorption spectra of specific molecular bands. However, its effectiveness is often constrained in complex solutions due to water's strong absorption. To this end, this study presents a rapid, non-invasive optical approach that combines micro-hyperspectral imaging with machine learning to explore scattering characteristics for the early detection of microbial contamination. First, micro-hyperspectral imaging data were collected using the transmittance mode. Secondly, hyperspectral data was processed with feature selection algorithms-Random Frog (RF), Competitive Adaptive Reweighted Sampling (CARS), and Uninformative Variable Elimination (UVE) to reduce the dimensionality of the original hyperspectral data. Thirdly, machine learning models, including Neural Networks (NN), k-Nearest Neighbors (kNN), Support Vector Machine (SVM), and Partial Least Squares Discriminant Analysis (PLSDA), were trained using the selected feature vectors. The experimental results demonstrated that the combination of PLSDA and CARS achieved the highest accuracy of 89.4% in detecting asymptomatic microbial contamination, 99.2% in uncontaminated and 99.7% in contaminated liquid culture. Finally, Mie scattering simulations were conducted to confirm the characteristic scattering patterns associated with early microbial contamination. Overall, this study highlights the effectiveness of integrating Mie scattering, hyperspectral imaging, and machine learning to enable a rapid optical approach for early detection of microbial contamination in liquid cultures. © 2025 ASABE. All rights reserved.
Keyword ：
Brillouin scattering Brillouin scattering Contamination Contamination Data handling Data handling Discriminant analysis Discriminant analysis Feature extraction Feature extraction Hyperspectral imaging Hyperspectral imaging Learning systems Learning systems Least squares approximations Least squares approximations Motion compensation Motion compensation Nearest neighbor search Nearest neighbor search Neural networks Neural networks Support vector machines Support vector machines Water absorption Water absorption
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| GB/T 7714 | Wu, Libin , Weng, Haiyong , Sun, Shangpeng et al. Integrating Micro-Hyperspectral Imaging and Mie Scattering for Early Detection of Microbial Contamination in Liquid Culture [C] . 2025 . |
| MLA | Wu, Libin et al. "Integrating Micro-Hyperspectral Imaging and Mie Scattering for Early Detection of Microbial Contamination in Liquid Culture" . (2025) . |
| APA | Wu, Libin , Weng, Haiyong , Sun, Shangpeng , Ye, Dapeng . Integrating Micro-Hyperspectral Imaging and Mie Scattering for Early Detection of Microbial Contamination in Liquid Culture . (2025) . |
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Anthocyanin is a crucial reference indicator for evaluating the quality of rice varieties, making it significant to rapidly establish a non-destructive detection method for anthocyanin in rice grains. This study constructs a 1DDCGAN (One-dimensional deep convolutional generative adversarial network) strategy optimized for one dimensional spectral data and a 1D-CNN (One-dimensional convolutional neural network) model, achieving high-quality generated sample effects and more accurate anthocyanin predictions within a limited dataset. The SG (Savitzky-Golay)-1D-CNN significantly outperforms LSR (Least squares regression), SVM (Support vector machine) and BPNN (Backpropagation neural network) in the test set, with R2 (Determination coefficient), RMSE (Root mean square error) and RPD (Residual predictive deviation) values of 0.83, 10.99, and 2.45, respectively. Furthermore, using DCGAN-generated samples to train the SG-1D-CNN by adding a certain number of generated samples can enhance the model's performance in the test set. When the number of added samples is 60 (75% of the original training set sample size), the SG-DCGAN-1D-CNN (Savitzky-Golay deep convolutional generative adversarial network one dimensional convolutional neural network) exhibits the best performance, with R2, RMSE, and RPD reaching 0.87, 9.40, and 2.88, respectively. The DCGAN-1D-CNN (Deep convolutional generative adversarial network one dimensional convolutional neural network) method based on this strategy is expected to provide new insights into precise prediction for multi-variety rice seeds.
Keyword ：
Anthocyanin Anthocyanin Generative adversarial network Generative adversarial network Hyperspectral Hyperspectral Rice Rice
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| GB/T 7714 | Bao, Xingsheng , Huang, Deyao , Yang, Biyun et al. Combining deep convolutional generative adversarial networks with visible-near infrared hyperspectral reflectance to improve prediction accuracy of anthocyanin content in rice seeds [J]. | FOOD CONTROL , 2025 , 174 . |
| MLA | Bao, Xingsheng et al. "Combining deep convolutional generative adversarial networks with visible-near infrared hyperspectral reflectance to improve prediction accuracy of anthocyanin content in rice seeds" . | FOOD CONTROL 174 (2025) . |
| APA | Bao, Xingsheng , Huang, Deyao , Yang, Biyun , Li, Jiayi , Opeyemi, Atoba Tolulope , Wu, Renye et al. Combining deep convolutional generative adversarial networks with visible-near infrared hyperspectral reflectance to improve prediction accuracy of anthocyanin content in rice seeds . | FOOD CONTROL , 2025 , 174 . |
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BackgroundRice blast is one of the most destructive diseases in rice cultivation, significantly threatening global food security. Timely and precise detection of rice panicle blast is crucial for effective disease management and prevention of crop losses. This study introduces ConvGAM, a novel semantic segmentation model leveraging the ConvNeXt-Large backbone network and the Global Attention Mechanism (GAM). This design aims to enhance feature extraction and focus on critical image regions, addressing the challenges of detecting small and complex disease patterns in UAV-captured imagery. Furthermore, the model incorporates advanced loss functions to handle data imbalances effectively, supporting accurate classification across diverse disease severities.ResultsThe ConvGAM model, leveraging the ConvNeXt-Large backbone network and the Global Attention Mechanism (GAM), achieves outstanding performance in feature extraction, crucial for detecting small and complex disease patterns. Quantitative evaluation demonstrates that the model achieves an overall accuracy of 91.4%, a mean IoU of 79%, and an F1 score of 82% on the test set. The incorporation of Focal Tversky Loss further enhances the model's ability to handle imbalanced datasets, improving detection accuracy for rare and severe disease categories. Correlation coefficient analysis across disease severity levels indicates high consistency between predictions and ground truth, with values ranging from 0.962 to 0.993. These results confirm the model's reliability and robustness, highlighting its effectiveness in rice panicle blast detection under challenging conditions.ConclusionThe ConvGAM model demonstrates strong qualitative advantages in detecting rice panicle blast disease. By integrating advanced feature extraction with the ConvNeXt-Large backbone and GAM, the model achieves precise detection and classification across varying disease severities. The use of Focal Tversky Loss ensures robustness against dataset imbalances, enabling accurate identification of rare disease categories. Despite these strengths, future efforts should focus on improving classification accuracy and adapting the model to diverse environmental conditions. Additionally, optimizing model parameters and exploring advanced data augmentation techniques could further enhance its detection capabilities and expand its applicability to broader agricultural scenarios.
Keyword ：
ConvNeXt ConvNeXt FocalTverskyLoss FocalTverskyLoss GAM GAM Rice blast Rice blast Semantic segmentation Semantic segmentation
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| GB/T 7714 | Lin, Shaodan , Huang, Deyao , Wu, Libin et al. UAV rice panicle blast detection based on enhanced feature representation and optimized attention mechanism [J]. | PLANT METHODS , 2025 , 21 (1) . |
| MLA | Lin, Shaodan et al. "UAV rice panicle blast detection based on enhanced feature representation and optimized attention mechanism" . | PLANT METHODS 21 . 1 (2025) . |
| APA | Lin, Shaodan , Huang, Deyao , Wu, Libin , Cheng, Zuxin , Ye, Dapeng , Weng, Haiyong . UAV rice panicle blast detection based on enhanced feature representation and optimized attention mechanism . | PLANT METHODS , 2025 , 21 (1) . |
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Biomass monitoring of mushroom liquid strains during the fermentation process demands real-time analysis with minimal manual intervention, highlighting the urgent need for intelligent surveillance. This study introduced a soft sensor method based on edge computing machine vision, termed Edge CV, for in situ non-invasive estimation of biomass. In our experiment, the hardware of the Edge CV system includes the Jetson Nano with 4 GB RAM, 64 GB ROM, and a 128-core Maxwell GPU for executing intelligent machine vision tasks, along with embedded cameras for image data acquisition. Furthermore, a cascaded machine vision model was developed to enable biomass evaluation on the Edge CV system. The cascaded machine vision model mainly consists of three steps: first, the object detection task to locate the observation window, achieving a mean Average Precision (mAP50:95) of 82.3% with 78.7 GFLOPs; then, the segmentation task to extract liquid strain data within the observation window, yielding a mean intersection over union (MIoU) of 85.9% with 110.4 GFLOPs; and finally, calculating mycelium biomass indices via the morphological image processing task. The correlation between Edge CV inference and manual measurement showed an R2 of 0.963 and an RMSE of 0.027 for normalized biomass indices, demonstrating a robust and consistent trend. Therefore, this study illustrates the practical application of edge computing-based machine vision for biomass soft sensing during the fermentation process.
Keyword ：
biomass biomass edge computing edge computing liquid strain liquid strain machine vision machine vision soft sensing soft sensing
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| GB/T 7714 | Wu, Libin , Xiao, Guimiao , Huang, Deyao et al. Edge Computing-Based Machine Vision for Non-Invasive and Rapid Soft Sensing of Mushroom Liquid Strain Biomass [J]. | AGRONOMY-BASEL , 2025 , 15 (1) . |
| MLA | Wu, Libin et al. "Edge Computing-Based Machine Vision for Non-Invasive and Rapid Soft Sensing of Mushroom Liquid Strain Biomass" . | AGRONOMY-BASEL 15 . 1 (2025) . |
| APA | Wu, Libin , Xiao, Guimiao , Huang, Deyao , Zhang, Xiandong , Ye, Dapeng , Weng, Haiyong . Edge Computing-Based Machine Vision for Non-Invasive and Rapid Soft Sensing of Mushroom Liquid Strain Biomass . | AGRONOMY-BASEL , 2025 , 15 (1) . |
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针对类芦缺少切割粉碎仿真模型,以类芦为研究对象,建立茎秆的离散元模型并完成具体参数测定与标定。首先,搭建测试平台,测定了类芦茎秆与钢的碰撞恢复系数、静摩擦因数与滚动摩擦因数等接触参数,建立了类芦茎秆模型并开展仿真堆积角试验,根据Box-Behnken试验方案以及物理堆积角（23.47°）,标定了茎秆间接触参数。其次,开展物理剪切与压缩试验,得到茎秆最大剪切力Fd=155.18 N,最大压缩破坏力F
Keyword ：
剪切试验 剪切试验 压缩试验 压缩试验 参数标定 参数标定 堆积角试验 堆积角试验 离散元模型 离散元模型 类芦茎秆 类芦茎秆
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| GB/T 7714 | 叶大鹏 , 青家兴 , 林志强 et al. 类芦茎秆离散元模型建立与参数标定 [J]. | 农业机械学报 , 2025 , 56 (07) : 139-149 . |
| MLA | 叶大鹏 et al. "类芦茎秆离散元模型建立与参数标定" . | 农业机械学报 56 . 07 (2025) : 139-149 . |
| APA | 叶大鹏 , 青家兴 , 林志强 , 吴逸腾 , 赖鸿康 , 翁海勇 . 类芦茎秆离散元模型建立与参数标定 . | 农业机械学报 , 2025 , 56 (07) , 139-149 . |
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Liquid culture is a nutrient-rich liquid medium used to grow microorganisms, yet it is vulnerable to contamination, which can only be observed at the symptomatic stage when turbidity appears, leaving limited opportunities for timely intervention. Optical chemometrics has been widely explored for early detection of contamination. However, its effectiveness is often constrained in complex solutions due to water's strong absorption. To this end, this study presents a novel optical approach that combines micro-hyperspectral imaging with machine learning to explore scattering characteristics for the early detection of microbial contamination. First, micro-hyperspectral data were collected from uncontaminated, asymptomatic, and symptomatically contaminated cultures. Three feature selection methods-Random Frog (RF), Competitive Adaptive Reweighted Sampling (CARS), and Uninformative Variable Elimination (UVE)-were followed by model training with neural networks (NN), k-nearest neighbors (kNN), and partial least squares discriminant analysis (PLS-DA). Confusion matrix analysis demonstrated that combining PLS-DA with CARS achieved accuracies of 89.4% for asymptomatic contamination, 99.1% for uncontaminated, and 99.3% for contaminated liquid cultures. Moreover, Mie scattering simulations were conducted to confirm the characteristic scattering patterns. The results indicate that the spectra of contaminated liquid cultures exhibit both molecular absorption and scattering, with scattering dynamics becoming more pronounced and variable during the contamination process, which provides a distinctive pattern for early contamination detection. Overall, the study demonstrates the effectiveness of integrating Mie scattering theory, hyperspectral imaging, and machine learning to establish a rapid optical approach for early microbial contamination detection in liquid cultures.
Keyword ：
Accuracy Accuracy CARS-PLS model detects asymptomatic CARS-PLS model detects asymptomatic contamination with 89.4 contamination with 89.4 Liquid culture Liquid culture Machine learning Machine learning Mie scattering Mie scattering
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| GB/T 7714 | Wu, Libin , Zhang, Shenghang , Weng, Haiyong et al. Integrating micro-hyperspectral imaging and machine learning to investigate mie scattering for early detection of microbial contamination in liquid fermentation cultures [J]. | MEASUREMENT , 2025 , 257 . |
| MLA | Wu, Libin et al. "Integrating micro-hyperspectral imaging and machine learning to investigate mie scattering for early detection of microbial contamination in liquid fermentation cultures" . | MEASUREMENT 257 (2025) . |
| APA | Wu, Libin , Zhang, Shenghang , Weng, Haiyong , Sun, Shangpeng , Ye, Dapeng . Integrating micro-hyperspectral imaging and machine learning to investigate mie scattering for early detection of microbial contamination in liquid fermentation cultures . | MEASUREMENT , 2025 , 257 . |
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| GB/T 7714 | Wu, Libin , Zhang, Shenghang , Weng, Haiyong et al. Integrating micro-hyperspectral imaging and machine learning to investigate mie scattering for early detection of microbial contamination in liquid fermentation cultures (vol 257, 118620, 2026) [J]. | MEASUREMENT , 2025 , 256 . |
| MLA | Wu, Libin et al. "Integrating micro-hyperspectral imaging and machine learning to investigate mie scattering for early detection of microbial contamination in liquid fermentation cultures (vol 257, 118620, 2026)" . | MEASUREMENT 256 (2025) . |
| APA | Wu, Libin , Zhang, Shenghang , Weng, Haiyong , Sun, Shangpeng , Ye, Dapeng . Integrating micro-hyperspectral imaging and machine learning to investigate mie scattering for early detection of microbial contamination in liquid fermentation cultures (vol 257, 118620, 2026) . | MEASUREMENT , 2025 , 256 . |
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盐胁迫会导致棉花纤维品质及产量下降，尤其在苗期时其遭受盐胁迫影响最大。为了实现棉苗盐胁迫的快速诊断，本文利用快速叶绿素荧光技术获取了不同盐胁迫程度下棉苗冠层叶片的OJIP曲线，并结合深度残差网络(Deep residual network, ResNet)和空洞卷积(Dilated convolution)结构构建了基于“叶位-通道”荧光数据融合的1D-DRDC-Net(1D-deep residual dilated convolutional neural network)棉苗盐胁迫深度学习诊断模型。结果表明，盐胁迫导致棉苗体内含水率下降，丙二醛(Malondialdehyde, MDA)含量、超氧化物歧化酶(Superoxide dismutase, SOD)活性、过氧化物酶(Peroxidase, POD)活性升高；在垂直方向上盐胁迫对棉苗的影响趋势表现为植株上部分叶片各参数变化明显，其中对胁迫最敏感的叶位为L1,而成熟叶片受到的影响相对较小。相比于其它模型，1D-DRDC-Net对棉苗不同胁迫时间下3个盐浓度梯度(0、100、200 mmol/L)的诊断精度为76.67%, F1值为76.48%,比支持向量机(Support vector machine, SVM)、反向传播神经网络(Back propagation neural network, BPNN)准确率均提高5个百分点，比随机森林(Random forest, RF)提高14.45个百分点，比双向长短期记忆网络(Bidirectional long short-term memory, Bi-LSTM)提高3.34个百分点。基于“叶位-通道”的荧光信息融合策略准确率优于仅使用单一敏感叶位荧光信息8.89个百分点，其鲁棒性和泛化能力均优于只采用普通卷积核和取消“跳跃连接”的模型。最终，建立的1D-DRDC-Net模型在棉苗受到胁迫7、14、21 d后，对植株是否受到盐胁迫的诊断准确率分别达到83.33%、88.33%和95.00%,研究结果可为棉花栽培管理提供理论依据。
Keyword ：
1D-DRDC-Net 1D-DRDC-Net 垂直异质性分布 垂直异质性分布 快速叶绿素荧光 快速叶绿素荧光 棉苗盐胁迫 棉苗盐胁迫
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| GB/T 7714 | 翁海勇 , 曾海燕 , 雷庆元 et al. 基于多叶位快速叶绿素荧光和1D-DRDC-Net的棉苗盐胁迫诊断方法 [J]. | 农业机械学报 , 2025 , 56 (03) : 476-484,493 . |
| MLA | 翁海勇 et al. "基于多叶位快速叶绿素荧光和1D-DRDC-Net的棉苗盐胁迫诊断方法" . | 农业机械学报 56 . 03 (2025) : 476-484,493 . |
| APA | 翁海勇 , 曾海燕 , 雷庆元 , 周蓓蓓 , 李佳怿 , 徐洪烟 . 基于多叶位快速叶绿素荧光和1D-DRDC-Net的棉苗盐胁迫诊断方法 . | 农业机械学报 , 2025 , 56 (03) , 476-484,493 . |
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