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学者姓名：叶大鹏
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Abstract ：
The flatness of the cut surface in enoki mushrooms (Flammulina filiformis Z.W. Ge, X.B. Liu & Zhu L. Yang) is a key factor in quality classification. However, conventional automatic cutting equipment struggles with deformation issues due to its inability to adjust the grasping force based on individual mushroom sizes. To address this, we propose an improved method that integrates visual feedback to dynamically adjust the execution end, enhancing cut precision. Our approach enhances YOLOv8n-seg with Star Net, SPPECAN (a reconstructed SPPF with efficient channel attention), and C2fDStar (C2f with Star Net and deformable convolution) to improve feature extraction while reducing computational complexity and feature loss. Additionally, we introduce a mask ownership judgment and merging optimization algorithm to correct positional offsets, internal disconnections, and boundary instabilities in grasping area predictions. Based on this, we optimize grasping parameters using an improved centroid-based region width measurement and establish a region width-to-PWM mapping model for the precise conversion from visual data to gripper control. Experiments in real-situation settings demonstrate the effectiveness of our method, achieving a mean average precision (mAP50:95) of 0.743 for grasping area segmentation, a 4.5% improvement over YOLOv8, with an average detection speed of 10.3 ms and a target width measurement error of only 0.14%. The proposed mapping relationship enables adaptive end-effector control, resulting in a 96% grasping success rate and a 98% qualified cutting surface rate. These results confirm the feasibility of our approach and provide a strong technical foundation for the intelligent automation of enoki mushroom cutting systems.
Keyword ：
Flammulina filiformis Flammulina filiformis machine vision machine vision multi-target recognition multi-target recognition serial communication serial communication servo control servo control YOLO v8 YOLO v8
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| GB/T 7714 | Xie, Limin , Jing, Jun , Wu, Haoyu et al. MPG-YOLO: Enoki Mushroom Precision Grasping with Segmentation and Pulse Mapping [J]. | AGRONOMY-BASEL , 2025 , 15 (2) . |
| MLA | Xie, Limin et al. "MPG-YOLO: Enoki Mushroom Precision Grasping with Segmentation and Pulse Mapping" . | AGRONOMY-BASEL 15 . 2 (2025) . |
| APA | Xie, Limin , Jing, Jun , Wu, Haoyu , Kang, Qinguan , Zhao, Yiwei , Ye, Dapeng . MPG-YOLO: Enoki Mushroom Precision Grasping with Segmentation and Pulse Mapping . | AGRONOMY-BASEL , 2025 , 15 (2) . |
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UAV image acquisition and deep learning techniques have been widely used in field hydrological monitoring to meet the increasing data volume demand and refined quality. However, manual parameter training requires trial-and-error costs (T&E), and existing auto-trainings adapt to simple datasets and network structures, which is low practicality in unstructured environments, e.g., dry thermal valley environment (DTV). Therefore, this research combined a transfer learning (MTPI, maximum transfer potential index method) and an RL (the MTSA reinforcement learning, Multi-Thompson Sampling Algorithm) in dataset auto-augmentation and networks auto-training to reduce human experience and T&E. Firstly, to maximize the iteration speed and minimize the dataset consumption, the best iteration conditions (MTPI conditions) were derived with the improved MTPI method, which shows that subsequent iterations required only 2.30% dataset and 6.31% time cost. Then, the MTSA was improved under MTPI conditions (MTSA-MTPI) to auto-augmented datasets, and the results showed a 16.0% improvement in accuracy (human error) and a 20.9% reduction in standard error (T&E cost). Finally, the MTPI-MTSA was used for four networks auto-training (e.g., FCN, Seg-Net, U-Net, and Seg-Res-Net 50) and showed that the best Seg-Res-Net 50 gained 95.2% WPA (accuracy) and 90.9% WIoU. This study provided an effective auto-training method for complex vegetation information collection, which provides a reference for reducing the manual intervention of deep learning.
Keyword ：
auto-DL method auto-DL method data augmentation automatic data augmentation automatic network training automatic network training automatic reinforcement learning for DL reinforcement learning for DL segmentation deep learning segmentation deep learning vegetation detection vegetation detection
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| GB/T 7714 | Chen, Yayong , Zhou, Beibei , Chen, Xiaopeng et al. A method of deep network auto-training based on the MTPI auto-transfer learning and a reinforcement learning algorithm for vegetation detection in a dry thermal valley environment [J]. | FRONTIERS IN PLANT SCIENCE , 2025 , 15 . |
| MLA | Chen, Yayong et al. "A method of deep network auto-training based on the MTPI auto-transfer learning and a reinforcement learning algorithm for vegetation detection in a dry thermal valley environment" . | FRONTIERS IN PLANT SCIENCE 15 (2025) . |
| APA | Chen, Yayong , Zhou, Beibei , Chen, Xiaopeng , Ma, Changkun , Cui, Lei , Lei, Feng et al. A method of deep network auto-training based on the MTPI auto-transfer learning and a reinforcement learning algorithm for vegetation detection in a dry thermal valley environment . | FRONTIERS IN PLANT SCIENCE , 2025 , 15 . |
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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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本发明提出一种确定冬笋探测装置最佳微波发射信号频段的方法，通过测定冬笋以及竹林内土壤的含水率和介电特性，分析不同微波信号频率对应冬笋和土壤介电特性的变化规律以及不同含水率下土壤的介电特性的变化情况，将冬笋以及土壤含水率之间的差异转化为介电特性的差异；根据测得的介电特性大小，分析冬笋与土壤之间介质损耗因素最大的频段范围，作为天线带宽；构建不同含水率下的冬笋‑土壤介电模型以分析对天线发射信号S参数的影响，以确定冬笋探测装置最佳微波发射信号频段的中心频率。并通过天线辐射方向图进一步验证最佳微波发射信号频段的中心频率。
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| GB/T 7714 | 谢立敏 , 赵杰 , 刘永强 et al. 一种确定冬笋探测装置最佳微波发射信号频段的方法 : CN202411951339.X[P]. | 2024-12-27 . |
| MLA | 谢立敏 et al. "一种确定冬笋探测装置最佳微波发射信号频段的方法" : CN202411951339.X. | 2024-12-27 . |
| APA | 谢立敏 , 赵杰 , 刘永强 , 叶大鹏 , 余锦旭 . 一种确定冬笋探测装置最佳微波发射信号频段的方法 : CN202411951339.X. | 2024-12-27 . |
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Firework oxidizer powders are inorganic compounds that supply active oxygen within pyrotechnic systems. Due to their fine particle size and tendency to leak during handling, accurately simulating their flow behavior is essential for optimizing powder-filling equipment. However, limited research exists on their discrete element method (DEM) parameters, and many key contact properties remain undocumented. This study systematically calibrated six critical DEM contact parameters for firework oxidizer powders-particle density, elastic modulus, Poisson's ratio, static friction coefficient, rolling friction coefficient, and shear modulus-using a combination of experimental measurements and numerical simulations. The Hertz-Mindlin contact model was adopted, and a Plackett-Burman design was used to identify the most influential parameters affecting the static angle of repose. Results showed that the static friction coefficient, rolling friction coefficient, and shear modulus had the most significant effects. Their optimal ranges were further refined via steepest ascent testing. A Box-Behnken design was employed to develop a regression model for the angle of repose, yielding final calibrated values of 0.288, 0.035, and 65.076 MPa for the three dominant parameters, respectively. These parameters were then applied in DEM simulations of powder flow involving a T-shaped scraper, which pushes the powder toward the dosing holes, allowing it to fall under gravity. Physical filling tests demonstrated that powder leakage was effectively controlled when the scraper speed did not exceed 0.3 m/s. This study provides valuable insights for equipment optimization and offers a reliable reference for DEM-based modeling and design of firework oxidizer powder handling systems.
Keyword ：
Angle of repose Angle of repose Firework oxidizer powder Firework oxidizer powder Parameter calibration Parameter calibration sDEM sDEM Simulation Simulation
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| GB/T 7714 | Fang, Yi , Zhang, Fayong , Ling, Liangcai et al. Calibration and verification of contact parameters for firework oxidizer powders based on DEM [J]. | GRANULAR MATTER , 2025 , 27 (4) . |
| MLA | Fang, Yi et al. "Calibration and verification of contact parameters for firework oxidizer powders based on DEM" . | GRANULAR MATTER 27 . 4 (2025) . |
| APA | Fang, Yi , Zhang, Fayong , Ling, Liangcai , Xie, Limin , Fang, Bing , Ye, Dapeng . Calibration and verification of contact parameters for firework oxidizer powders based on DEM . | GRANULAR MATTER , 2025 , 27 (4) . |
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针对四旋翼飞行器轨迹跟踪控制中模型预测控制(MPC)的标称模型不确定问题，提出了一种基于在线高斯过程回归模型增强的模型预测控制(OGP-MPC)方法，利用在线高斯过程回归(OGP)模型补偿标称模型的动力学误差。设计了一种新的在线GP模型更新框架，通过引入子GP模型对新数据进行预处理，提高数据质量，进而迭代更新主GP模型参数，以实现自适应动力学模型误差补偿。仿真结果表明，相比传统MPC和GP-MPC,所提方法在圆形轨迹下的模型精度和跟踪精度提升均超过16%,空间曲线轨迹下提升超过5%。
Keyword ：
四旋翼 四旋翼 数据驱动 数据驱动 模型预测控制 模型预测控制 轨迹跟踪 轨迹跟踪 高斯过程回归 高斯过程回归
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| GB/T 7714 | 叶大鹏 , 陈书达 , 张之得 . 基于在线高斯模型驱动MPC的四旋翼轨迹跟踪控制 [J]. | 飞行力学 , 2025 , 43 (01) : 56-62 . |
| MLA | 叶大鹏 et al. "基于在线高斯模型驱动MPC的四旋翼轨迹跟踪控制" . | 飞行力学 43 . 01 (2025) : 56-62 . |
| APA | 叶大鹏 , 陈书达 , 张之得 . 基于在线高斯模型驱动MPC的四旋翼轨迹跟踪控制 . | 飞行力学 , 2025 , 43 (01) , 56-62 . |
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设计了一款物料可翻堆的卧旋式生物有机肥发酵罐,为获得最优有机肥腐熟品质及效率的翻堆工艺参数,以Hertz-Mindlin with JKR作为物料与设备间的离散元接触模型,利用离差系数评价其混合均匀度。试验表明,当发酵罐转罐2 r时物料基本混合均匀,故将单次转罐翻堆的频次设置为2 r,在4种分阶段变频次的翻堆模式下,对发酵物料含水率、温度、腐熟度进行分析,结果表明,采用模式4(升温期不翻堆,高温期每天翻堆1次,保温期每天翻堆1次,翻堆处理7 d后静置陈化至15 d)得到的最高发酵温度可达68.4℃,有机肥处理的萝卜种子发芽指数为107.6%,达到腐熟要求且发酵过程稳定可靠。研究结果可为生物有机肥的制备提供装备及工艺支撑,保证有机肥腐熟品质,有效解决目前发酵工艺不明确的问题。
Keyword ：
卧旋式生物有机肥发酵罐 卧旋式生物有机肥发酵罐 工艺试验 工艺试验 混合均匀度 混合均匀度 离散元 离散元 翻堆 翻堆
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| GB/T 7714 | 叶大鹏 , 赵熠威 , 黄德耀 et al. 卧旋式生物有机肥发酵罐翻堆工艺设计与试验 [J]. | 农业机械学报 , 2025 , 56 (07) : 662-670 . |
| MLA | 叶大鹏 et al. "卧旋式生物有机肥发酵罐翻堆工艺设计与试验" . | 农业机械学报 56 . 07 (2025) : 662-670 . |
| APA | 叶大鹏 , 赵熠威 , 黄德耀 , 林炜域 , 周蓓蓓 , 曾阳灿 . 卧旋式生物有机肥发酵罐翻堆工艺设计与试验 . | 农业机械学报 , 2025 , 56 (07) , 662-670 . |
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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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In order to obtain the optimal composting process parameters of organic fertilizer decomposition quality and efficiency, Hertz — Mindlin with JKR was used as the discrete element contact model between the material and the equipment, and according to the simulation results of particle distribution, the material mixing during tank rotation was simulated and calculated by EDEM 2022 Rl software, and the difference of material mixing degree under different tank turns in a single pile turning was determined. The dispersion coefficient was used to evaluate the mixing uniformity. The test showed that when the fermentation tank was transferred to the tank for 2 r, the material was basically mixed evenly, so the frequency of a single turning tank was set to 2 r, the mixture of chicken manure and corn straw was selected as the raw material for the turnover frequency test, the temperature and moisture content of the fermentation material under different turning frequencies were analyzed, and the moisture content, temperature and maturity of the fermentation material were analyzed in four kinds of phased frequency conversion mode, and the results showed that mode 4 (no turning during the heating period, turning the pile once a day during the high temperature period, and turning the pile once a day during the holding period), the highest fermentation temperature was 68.4^, and the germination index of radish seeds treated with organic fertilizer was 107. 6%, which met the requirements of decomposition and the fermentation process was stable and reliable. The research results can provide equipment and process support for the preparation of bio-organic fertilizer, ensure the decomposition quality of organic fertilizer, and effectively solve the problem of unclear fermentation process. © 2025 Chinese Society of Agricultural Machinery. All rights reserved.
Keyword ：
Composting Composting Cultivation Cultivation Fermentation Fermentation Fertilizers Fertilizers Mixing Mixing Moisture Moisture Moisture determination Moisture determination Seed Seed Tanks (containers) Tanks (containers)
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| GB/T 7714 | Ye, Dapeng , Zhao, Yiwei , Huang, Deyao et al. Design and Experiment of Horizontal Rotating Bioorganic Fertilizer Fermentation Tank Flipping Process [J]. | Transactions of the Chinese Society for Agricultural Machinery , 2025 , 56 (7) : 662-670 . |
| MLA | Ye, Dapeng et al. "Design and Experiment of Horizontal Rotating Bioorganic Fertilizer Fermentation Tank Flipping Process" . | Transactions of the Chinese Society for Agricultural Machinery 56 . 7 (2025) : 662-670 . |
| APA | Ye, Dapeng , Zhao, Yiwei , Huang, Deyao , Lin, Weiyu , Zhou, Beibei , Zeng, Yangcan . Design and Experiment of Horizontal Rotating Bioorganic Fertilizer Fermentation Tank Flipping Process . | Transactions of the Chinese Society for Agricultural Machinery , 2025 , 56 (7) , 662-670 . |
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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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