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学者姓名：翁海勇
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Abstract ：
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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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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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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利用可见/近红外光谱（Vis/NIR）,为菌草耐寒性的非破坏性快速评估提供有效的办法。以6种不同品种的菌草为试材，在低温胁迫5 d时，采集叶片的可见/近红外光谱和8个生理指标数据，分析低温胁迫后叶片反射率、光谱指数及生理指标的变化趋势，并应用主成分分析、隶属函数、聚类分析方法对菌草苗期的耐寒性进行综合评价。低温胁迫使叶片含水率及叶绿素a、叶绿素b、类胡萝卜素、总叶绿素含量下降，丙二醛含量增加，过氧化氢酶、超氧化物歧化酶活性升高，个别品种的酶活性受到抑制。低温胁迫导致菌草叶片红边（REP）蓝移，蓝边（BEP）、黄边（YEP）红移，整体反射率上升。光谱指数TCARI、MCARI上升，RARSb、CRI
Keyword ：
主成分分析 主成分分析 可见/近红外光谱 可见/近红外光谱 耐寒性 耐寒性 聚类分析 聚类分析 菌草 菌草 隶属函数 隶属函数
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| GB/T 7714 | 李慧琳 , 许金钗 , 张圣杰 et al. 基于可见/近红外光谱的菌草耐寒性快速评价方法构建 [J]. | 江苏农业科学 , 2025 , 53 (07) : 172-180 . |
| MLA | 李慧琳 et al. "基于可见/近红外光谱的菌草耐寒性快速评价方法构建" . | 江苏农业科学 53 . 07 (2025) : 172-180 . |
| APA | 李慧琳 , 许金钗 , 张圣杰 , 张博昱 , 谢夏仪 , 叶大鹏 et al. 基于可见/近红外光谱的菌草耐寒性快速评价方法构建 . | 江苏农业科学 , 2025 , 53 (07) , 172-180 . |
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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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During automated packaging of white tea, uneven tea pile thickness leads to reduced weighing accuracy, while traditional experimental methods struggle to reveal the underlying particle flow mechanisms, hindering equipment optimization. Addressing the lack of discrete element method (DEM) parameters for Baihao Yinzhen tea, this study calibrates its DEM parameters based on the DEM approach, providing input for virtual commissioning of packaging machinery. Through physical experiments, the static friction coefficient (0.546), restitution coefficient (0.326), and rolling friction coefficient (0.133) between tea leaves and steel plates were determined. A three-dimensional DEM model of tea leaves was established using slicing techniques and the multi-sphere aggregation method. The steepest-ascent method and Box-Behnken design were employed to optimize the simulation parameters, resulting in the following optimal parameter combination: inter-particle restitution coefficient (0.16), static friction coefficient (0.14), and rolling friction coefficient (0.15). Validation simulations demonstrated that the mean angle of repose of tea leaves under the optimized parameter combination was 22.51 degrees, with a relative error of only 1.29% compared to the actual experimental result of 22.80 degrees. The calibrated parameters can be directly applied to the simulation of the feeding system in white tea automatic packaging machines, enabling optimization of vibration parameters through prediction of pile behavior, thereby reducing weighing errors.
Keyword ：
angle of repose angle of repose discrete element method (DEM) discrete element method (DEM) parameter calibration parameter calibration white tea white tea
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| GB/T 7714 | Ye, Dapeng , Gao, Yuxuan , Qi, Yanlin et al. DEM Parameter Calibration and Experimental Definition for White Tea Granular Systems [J]. | AGRONOMY-BASEL , 2025 , 15 (8) . |
| MLA | Ye, Dapeng et al. "DEM Parameter Calibration and Experimental Definition for White Tea Granular Systems" . | AGRONOMY-BASEL 15 . 8 (2025) . |
| APA | Ye, Dapeng , Gao, Yuxuan , Qi, Yanlin , Wang, Hao , Wu, Renye , Weng, Haiyong . DEM Parameter Calibration and Experimental Definition for White Tea Granular Systems . | AGRONOMY-BASEL , 2025 , 15 (8) . |
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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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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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Citrus Huanglongbing (HLB) is highly contagious, and timely detection and removal of HLB-infected citrus trees is extremely important to prevent its spread. However, the robustness of optical imaging-based models remains limited by the variations in data due to different plant varieties, geospatial conditions, and data collection dates, etc. This study aimed to propose a method for robust HLB detection via transfer learning with multispectralmulticolor imaging. Four lightweight neural networks, namely Yolov7, Yolov7-tiny, Yolov4-tiny, and MaskRCNN were introduced for citrus HLB disease detection across different datasets. Transfer learning on the Orah mandarin dataset was conducted using the Navel orange dataset for pre-training. The results showed that Mask-RCNN achieved the best performance with an mAP@0.5 of 91.65%. By replacing the backbone of MaskRCNN with MobileNetV3-large, the model Mask-RCNNV3 was established, with an mAP@0.5 of 93.37% and then used for transfer learning for other datasts. Further optimizing the number of transferred layers and sample size, it revealed the most favorable sample size was 20 per class, and the mAP@0.5 gradually increased at the first 9 layers. Mask-RCNNV3 under the best transfer learning parameters, called Mask-RCNNV3_best, achieved the mAP@0.5 of 93.14% for Orah mandarin, 91.82% for Blood orange and 92.36% for Ponkan, respectively. Compared to the original Mask-RCNN model, the training parameters (Params) and GFLOPs were reduced by 82.95% and 96.57%, respectivley. It demonstrated that a limited amount of labeled data proved sufficient to achieve satisfactory performance across the tested cultivars and growing conditions. The FPS of the model was also improved by 4 times compared to Mask-RCNN, illustrating the potential of the model for edge deployment for practical applications. These findings would bridge the gap between research and practical implementation, reduce costly labeling for model training and provide practical tools for citrus growers to use.
Keyword ：
Citrus Huanglongbing Citrus Huanglongbing Domain adaptation Domain adaptation Mask-RCNN Mask-RCNN Transfer learning Transfer learning
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| GB/T 7714 | Huang, Deyao , Xiao, Kangsong , Luo, Hairong et al. Implementing transfer learning for citrus Huanglongbing disease detection across different datasets using neural network [J]. | COMPUTERS AND ELECTRONICS IN AGRICULTURE , 2025 , 238 . |
| MLA | Huang, Deyao et al. "Implementing transfer learning for citrus Huanglongbing disease detection across different datasets using neural network" . | COMPUTERS AND ELECTRONICS IN AGRICULTURE 238 (2025) . |
| APA | Huang, Deyao , Xiao, Kangsong , Luo, Hairong , Yang, Biyun , Lan, Shiying , Jiang, Yutong et al. Implementing transfer learning for citrus Huanglongbing disease detection across different datasets using neural network . | COMPUTERS AND ELECTRONICS IN AGRICULTURE , 2025 , 238 . |
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Gray mold is one of the major diseases affecting tomato production. Its early symptoms are often inconspicuous, yet the disease spreads rapidly, leading to severe economic losses. Therefore, the development of efficient and non-destructive early detection technologies is of critical importance. At present, multispectral imaging-based detection methods are constrained by two major bottlenecks: limited sample size and single modality, which hinder precise recognition at the early stage of infection. To address these challenges, this study explores a detection approach integrating multispectral fluorescence and reflectance imaging, combined with machine learning algorithms, to enhance early recognition of tomato gray mold. Particular emphasis is placed on evaluating the effectiveness of multimodal information fusion in extracting early disease features, and on elucidating the quantitative relationships between disease progression and key physiological indicators such as chlorophyll content, water content, malondialdehyde levels, and antioxidant enzyme activities. Furthermore, an improved WGAN-GP (Wasserstein Generative Adversarial Network with Gradient Penalty) is employed to alleviate data scarcity under small-sample conditions. The results demonstrate that multimodal data fusion significantly improves model sensitivity to early-stage disease detection, while WGAN-GP-based data augmentation effectively enhances learning performance with limited samples. The Random Forest model achieved an early recognition precision of 97.21% on augmented datasets, and transfer learning models attained an overall precision of 97.56% in classifying different disease stages. This study provides an effective approach for the early prediction of tomato gray mold, with potential application value in optimizing disease management strategies and reducing environmental impact.
Keyword ：
disease detection disease detection gray mold gray mold machine learning machine learning multispectral fluorescence-reflectance technology multispectral fluorescence-reflectance technology tomato tomato
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| GB/T 7714 | Zhong, Xiaohao , Li, Huicheng , Cai, Yixin et al. Early Detection of Tomato Gray Mold Based on Multispectral Imaging and Machine Learning [J]. | HORTICULTURAE , 2025 , 11 (9) . |
| MLA | Zhong, Xiaohao et al. "Early Detection of Tomato Gray Mold Based on Multispectral Imaging and Machine Learning" . | HORTICULTURAE 11 . 9 (2025) . |
| APA | Zhong, Xiaohao , Li, Huicheng , Cai, Yixin , Deng, Ying , Xu, Haobin , Tian, Jun et al. Early Detection of Tomato Gray Mold Based on Multispectral Imaging and Machine Learning . | HORTICULTURAE , 2025 , 11 (9) . |
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