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学者姓名：何金成
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Abstract ：
Achieving efficient and accurate detection and localization of duck eggs in the unstructured environment of free-range duck sheds is crucial for developing automated egg-picking robots. This paper proposes an improved YOLOv5s-based model (YOLOv5s-MNKS) designed to enhance detection performance, reduce model complexity, and improve the robot's adaptability and operational efficiency in complex environments. The model utilizes MobileNetV3 as the backbone network, reducing the number of parameters and increasing detection speed. The Squeeze-and-Excitation Module is replaced with a Normalization-based Attention Module to improve feature extraction capability. Group Shuffle Convolution and Bidirectional Feature Pyramid Network are introduced in the Neck layer, enhancing multi-scale feature fusion while reducing parameter count. A Soft-CIoU-NMS loss function is also designed, which improves detection accuracy in scenarios involving dense stacking and occlusion by lowering the confidence of overlapping bounding boxes instead of directly eliminating them. Experimental results demonstrate that the mAP of YOLOv5s-MNKS reaches 95.6%, representing a 0.3% improvement over the original model, while the model size is reduced to 5.7 MB, approximately 40% of the original size. When deployed on the Jetson Nano embedded platform with TensorRT acceleration, the model achieves a detection frame rate of 22.3 frames per second. In simulated and real-world duck shed scenarios, the improved model accurately and quickly identifies and locates duck eggs in complex environments, including occlusion, stacking, and low lighting, demonstrating strong robustness and applicability. This research provides technical support for the future development of duck egg-picking robots.
Keyword ：
Attention mechanism Attention mechanism Duck egg detection Duck egg detection Duck egg-picking robot Duck egg-picking robot Lightweight model Lightweight model YOLOv5s YOLOv5s
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| GB/T 7714 | Jie, Dengfei , Wang, Jun , Wang, Hao et al. Real-time recognition research for an automated egg-picking robot in free-range duck sheds [J]. | JOURNAL OF REAL-TIME IMAGE PROCESSING , 2025 , 22 (2) . |
| MLA | Jie, Dengfei et al. "Real-time recognition research for an automated egg-picking robot in free-range duck sheds" . | JOURNAL OF REAL-TIME IMAGE PROCESSING 22 . 2 (2025) . |
| APA | Jie, Dengfei , Wang, Jun , Wang, Hao , Lv, Huifang , He, Jincheng , Wei, Xuan . Real-time recognition research for an automated egg-picking robot in free-range duck sheds . | JOURNAL OF REAL-TIME IMAGE PROCESSING , 2025 , 22 (2) . |
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Passion fruit, a commercially significant fruit crop, is easily infected by anthracnose and scab, which declines it economic value. However, at the present time, passion fruit quality grading is mainly judged by manual assessment, with strong subjectivity, poor efficiency and low accuracy. Intelligent classification of postharvest passion fruit is essential, with skin disease being a critical factor in grading fruit quality. In view of the shortcomings in traditional deep learning model, such as weak multi-scale detection ability and low accuracy, we propose a YOLOv8-MDN-Tiny model to improve the ability of passion fruit small-scale disease detection. The backbone layer is replaced by the self-made MFSO structure to expand the feature pixels of small target information and enrich their feature expression. An improved DyRep module is proposed to realize the interactive fusion of disease features at different scales and depths. NWD loss function is introduced to accurately measure the overlap of two bounding boxes. Finally, Slimming pruning and CWD are used to compress the model. Compared with YOLOv8s, our improved lightweight model achieves more accurate localization of small passion fruit targets. Specifically, the mAP50 is increased by 2.2-94.8 %, the precision and recall are improved by 1.5% and 6.0%. Meanwhile, the number of model parameters and memory usage are decreased by 90.1 % and 88.9%. The results technically support the disease detection in postharvest passion fruit and real-time grading of their quality.
Keyword ：
Disease detection Disease detection Model compress Model compress Small-scale Small-scale YOLOv8 YOLOv8
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| GB/T 7714 | Chen, Dengjie , Lin, Fan , Lu, Caihua et al. YOLOv8-MDN-Tiny: A lightweight model for multi-scale disease detection of postharvest golden passion fruit [J]. | POSTHARVEST BIOLOGY AND TECHNOLOGY , 2025 , 219 . |
| MLA | Chen, Dengjie et al. "YOLOv8-MDN-Tiny: A lightweight model for multi-scale disease detection of postharvest golden passion fruit" . | POSTHARVEST BIOLOGY AND TECHNOLOGY 219 (2025) . |
| APA | Chen, Dengjie , Lin, Fan , Lu, Caihua , Zhuang, JunWei , Su, Hongjie , Zhang, Dehui et al. YOLOv8-MDN-Tiny: A lightweight model for multi-scale disease detection of postharvest golden passion fruit . | POSTHARVEST BIOLOGY AND TECHNOLOGY , 2025 , 219 . |
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Simultaneous localization and map building (SLAM) is crucial in autonomous robot navigation. However, existing SLAM systems generally assume a static environment, which makes it difficult to cope with the interference caused by moving objects in dynamic scenes, affecting the system's localization accuracy and robustness. To address this challenge, this paper proposes YKD-SLAM, a visual SLAM system for indoor dynamic environments, which is based on the ORB-SLAM2 framework and incorporates YOLOv8 target detection, RCF-KMeans (Region-ConstrainedFastK-Means), and epipolar geometric constraints to realize the accurate rejection of dynamic feature points and improve the localization performance in dynamic environments. YKD-SLAM first uses YOLOv8 to detect dynamic objects in the scene, generates a detection frame, optimizes the depth map through open operations, and performs multi-region segmentation of the region within the detection frame by combining RCF-KMeans. Subsequently, through the dynamic feature point rejection strategy based on epipolar geometric constraints, different regions in the detection frame are discriminated into dynamic and static regions, and the feature points in the dynamic region are rejected to improve the localization accuracy and robustness of the system in dynamic environments. The experimental results show that YKD-SLAM performs well in several dynamic scenes in the TUMRGB-D dataset. Compared with ORB-SLAM2, its ATE is reduced by 98.37%; compared with DynaSLAM, the system operation efficiency is improved by 95.35%. In addition, practical experiments conducted in indoor dynamic scenes further validate its potential in real applications.
Keyword ：
dynamic environments dynamic environments epipolar geometry constraint epipolar geometry constraint feature point culling feature point culling visual SLAM visual SLAM YOLOv8 YOLOv8
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| GB/T 7714 | Qiu, Haofeng , Wang, Jun , Lin, Zhipeng et al. YKD-SLAM: a visual SLAM system in dynamic environments based on object detection and region segmentation [J]. | MEASUREMENT SCIENCE AND TECHNOLOGY , 2025 , 36 (10) . |
| MLA | Qiu, Haofeng et al. "YKD-SLAM: a visual SLAM system in dynamic environments based on object detection and region segmentation" . | MEASUREMENT SCIENCE AND TECHNOLOGY 36 . 10 (2025) . |
| APA | Qiu, Haofeng , Wang, Jun , Lin, Zhipeng , Fan, Jiating , He, Jincheng , Jie, Dengfei . YKD-SLAM: a visual SLAM system in dynamic environments based on object detection and region segmentation . | MEASUREMENT SCIENCE AND TECHNOLOGY , 2025 , 36 (10) . |
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为满足异位发酵床垫料水分快速检测的需求，探讨基于可见—近红外光谱技术建立异位发酵床垫料水分预测模型的可行性。采集4～5个月的垫料样品，通过光谱仪获取400～990 nm光谱数据，并用CARS算法筛选关键特征波段。随后构建BP神经网络模型，并对比灰狼算法（GWO）、哈里斯鹰算法（HHO）、冠豪猪算法（CPO）三种优化算法，发现CPO算法优化效果最佳。通过Chebyshev混沌映射改进粒子群算法，形成CARS—ICPO模型。该模型在验证集和预测集上的决定系数R
Keyword ：
可见—近红外光谱 可见—近红外光谱 垫料 垫料 异位发酵床 异位发酵床 水分检测 水分检测 神经网络 神经网络 算法优化 算法优化
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| GB/T 7714 | 何金成 , 郑积祥 , 洪思思 . 可见—近红外光谱法异位发酵床垫料水分快速检测 [J]. | 中国农机化学报 , 2025 , 46 (07) : 281-287 . |
| MLA | 何金成 et al. "可见—近红外光谱法异位发酵床垫料水分快速检测" . | 中国农机化学报 46 . 07 (2025) : 281-287 . |
| APA | 何金成 , 郑积祥 , 洪思思 . 可见—近红外光谱法异位发酵床垫料水分快速检测 . | 中国农机化学报 , 2025 , 46 (07) , 281-287 . |
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Rheological properties play an important role in food production and quality control. This research explores the relationship between rheological parameters and quality characteristics of passion fruit and establishes a maturity classification model for passion fruit based on its rheological properties. Each sample undergoes a rheological test, texture profile test, puncture test, and physicochemical index test. These tests aim to gather precise mechanical and physiological information on passion fruit. We built a mechanical testing platform and used machine vision to analyse the micro-deformation of fruit. The platform can measure the real-time contact area and load value to obtain accurate stress values during compression. Non-destructive rheological tests were conducted on intact passion fruit to get the elastic modulus during the loading stage. It is highly consistent with the results of traditional Hertz contact theory. Additionally, the stress relaxation parameters were obtained by fitting the five elements Maxwell model during the holding stage. Notably, there are strong correlations between the rheological parameters and most texture parameters or physicochemical indicators, with the highest correlation coefficient reaching 0.703. Therefore, the rheological parameters were utilised as inputs for maturity classification models (GBDT, MLP, and AdaBoost). All models achieved satisfactory classification results. Particularly, the GBDT model demonstrated excellent classification performance and generalisation capability, with Precision, Recall, and F-Score of 80.44%, 80.08%, and 80.26%. The results show that it is feasible to determine the maturity of passion fruit based on non-destructive rheological characteristics.
Keyword ：
Hertz contact Hertz contact Image processing Image processing Machine learning Machine learning Maxwell model Maxwell model Viscoelastic Viscoelastic
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| GB/T 7714 | Lin, Fan , Chen, Dengjie , Lu, Caihua et al. Correlation between rheological properties and maturity of passion fruit based on machine vision [J]. | BIOSYSTEMS ENGINEERING , 2025 , 250 : 236-249 . |
| MLA | Lin, Fan et al. "Correlation between rheological properties and maturity of passion fruit based on machine vision" . | BIOSYSTEMS ENGINEERING 250 (2025) : 236-249 . |
| APA | Lin, Fan , Chen, Dengjie , Lu, Caihua , He, Jincheng . Correlation between rheological properties and maturity of passion fruit based on machine vision . | BIOSYSTEMS ENGINEERING , 2025 , 250 , 236-249 . |
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为提高藤蔓的粉碎效果、降低粉碎能耗，设计了一款藤蔓粉碎刀具。首先，利用ANSYS Workbench/Ls-Dyna对单把等滑切动刀与藤蔓的粉碎过程进行了仿真分析，以刀轴的转速、刀片类型及其等滑切角为影响因素，以比能耗为目标进行正交试验，得到最优的参数组合：刀具类型为弧形锯齿刀片，刀片滑切角为40°,刀轴转速为800 r/min。制作试验平台对单把等滑切动刀进行试验，试验结果与仿真结果基本一致。最后，对整个粉碎装置进行试验，将藤蔓含水率、刀盘转速、动定刀间隙作为考察因素，以比能耗为主要优化目标、粉碎合格率为次要目标，进行响应曲面试验。试验得到最佳参数组合为动定刀间隙2.44 mm、刀盘转速911.28 r/min、含水率52.02%,此时比能耗为0.140 0 kW·h/kg、碎合格率为97.5317%。
Keyword ：
LS-DYNA LS-DYNA 响应曲面 响应曲面 百香果藤蔓 百香果藤蔓 等滑切 等滑切 粉碎刀具 粉碎刀具
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| GB/T 7714 | 李俊平 , 张德晖 , 何金成 et al. 百香果藤蔓粉碎刀具的设计与试验 [J]. | 农机化研究 , 2025 , 47 (04) : 83-91 . |
| MLA | 李俊平 et al. "百香果藤蔓粉碎刀具的设计与试验" . | 农机化研究 47 . 04 (2025) : 83-91 . |
| APA | 李俊平 , 张德晖 , 何金成 , 贺平 , 李盼春 , 候志涛 . 百香果藤蔓粉碎刀具的设计与试验 . | 农机化研究 , 2025 , 47 (04) , 83-91 . |
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In pig farming, multi-object tracking (MOT) algorithms are effective tools for identifying individual pigs and monitoring their health, which enhances management efficiency and intelligence. However, due to the considerable variation in breeding environments across different pig farms, existing models often struggle to perform well in unfamiliar settings. To enhance the model's generalization in diverse tracking scenarios, we have innovatively proposed the SDGTrack method. This method improves tracking performance across various farming environments by enhancing the model's adaptability to different domains and integrating an optimized tracking strategy, significantly increasing the generalization of group pig tracking technology across different scenarios. To comprehensively evaluate the potential of the SDGTrack method, we constructed a multi-scenario dataset that includes both public and private data, spanning ten distinct pig farming environments. We only used a portion of the daytime scenes as the training set, while the remaining daytime and nighttime scenes were used as the validation set for evaluation. The experimental results demonstrate that SDGTrack achieved a MOTA score of 80.9%, an IDSW of 24, and an IDF1 score of 85.1% across various scenarios. Compared to the original CSTrack method, SDGTrack improved the MOTA and IDF1 scores by 16.7% and 33.3%, respectively, while significantly reducing the number of ID switches by 94.6%. These findings indicate that SDGTrack offers robust tracking capabilities in previously unseen farming environments, providing a strong technical foundation for monitoring pigs in different settings.
Keyword ：
computer vision computer vision group-housed pigs group-housed pigs multi-object tracking multi-object tracking multi-scene generalization multi-scene generalization
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| GB/T 7714 | Liu, Tao , Jie, Dengfei , Zhuang, Junwei et al. SDGTrack: A Multi-Target Tracking Method for Pigs in Multiple Farming Scenarios [J]. | ANIMALS , 2025 , 15 (11) . |
| MLA | Liu, Tao et al. "SDGTrack: A Multi-Target Tracking Method for Pigs in Multiple Farming Scenarios" . | ANIMALS 15 . 11 (2025) . |
| APA | Liu, Tao , Jie, Dengfei , Zhuang, Junwei , Zhang, Dehui , He, Jincheng . SDGTrack: A Multi-Target Tracking Method for Pigs in Multiple Farming Scenarios . | ANIMALS , 2025 , 15 (11) . |
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生猪养殖是我国农业经济发展的基础性产业，而生猪精准饲喂和种猪性能测定是生猪养殖的关键环节。传统饲喂养殖装备存在饲料浪费严重、人工劳动强度大、影响生猪生产力水平等问题。随着我国生猪养殖向着规模化、标准化和智能化方向快速发展，生猪电子饲喂站和种猪性能测定站的广泛应用提高了养殖管理水平和养殖场的生产效率。因此，生猪智能饲喂养殖装备已成为目前的研究热点。文章针对国内外不同饲养阶段的生猪电子饲喂站的机械结构、系统的功能特点进行对比分析；基于生猪科学育种的需求，阐述了国内外种猪性能测定站的工作原理和关键技术;最后，总结并展望了在新一代信息感知和人工智能技术的快速发展背景下，生猪电子饲喂站和种猪性能测定站发展趋势，以期为我国现代生猪智能饲喂养殖装备的完善提供重要参考。
Keyword ：
信息感知 信息感知 性能测定站 性能测定站 智能化管理 智能化管理 生猪养殖 生猪养殖 电子饲喂站 电子饲喂站
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| GB/T 7714 | 介邓飞 , 李天乐 , 姜朋辉 et al. 生猪智能饲喂养殖装备研究进展与展望 [J]. | 饲料工业 , 2025 , 46 (11) : 10-18 . |
| MLA | 介邓飞 et al. "生猪智能饲喂养殖装备研究进展与展望" . | 饲料工业 46 . 11 (2025) : 10-18 . |
| APA | 介邓飞 , 李天乐 , 姜朋辉 , 王杨 , 何金成 , 沈美雄 . 生猪智能饲喂养殖装备研究进展与展望 . | 饲料工业 , 2025 , 46 (11) , 10-18 . |
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针对红外热成像细节模糊、成像效果差且易受环境因素影响的问题，本文提出了一种基于红外与RGB图像配准的生猪体温检测方法。本研究收集了484组生猪直肠体温数据、面部红外与RGB图像以及环境信息（风速、温湿度和光照强度），通过相机坐标变换实现红外与RGB图像的配准并应用YOLO v8目标检测算法提取生猪耳朵、眼睛和鼻子区域的温度信息，基于这些重要特征变量建立以多个集成学习模型为底层、多层感知机神经网络为元模型的体温反演堆叠模型。结果显示：利用YOLO v8对RGB图像和红外热图像进行目标检测建模，RGB模型的平均精度均值（mAP）比红外模型高出31.66%，且基于相机坐标变换的红外与RGB图像配准技术的配准误差在1像素以内。此外，基于三层集成堆叠模型的体温预测均方根误差（RMSE）为0.19℃，平均绝对误差（MAE）为0.14℃，相比多元线性回归和多层感知机模型，MAE分别降低了48.33%和37.67%。因此，本研究通过红外与RGB图像配准有效消除了检测模糊问题，并利用改进的堆叠模型实现了更高的体温预测准确性，满足猪场测温要求。
Keyword ：
三层集成堆叠模型 三层集成堆叠模型 体温检测 体温检测 图像配准 图像配准 生猪 生猪 红外热成像 红外热成像
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| GB/T 7714 | 介邓飞 , 申恒尉 , 李家俊 et al. 基于红外与RGB图像配准的生猪体温检测方法研究 [J]. | 中国畜牧杂志 , 2025 , 61 (05) : 410-418 . |
| MLA | 介邓飞 et al. "基于红外与RGB图像配准的生猪体温检测方法研究" . | 中国畜牧杂志 61 . 05 (2025) : 410-418 . |
| APA | 介邓飞 , 申恒尉 , 李家俊 , 李天乐 , 何金成 , 沈美雄 . 基于红外与RGB图像配准的生猪体温检测方法研究 . | 中国畜牧杂志 , 2025 , 61 (05) , 410-418 . |
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Pig health and welfare concerns are gaining increasing attention, and accurately monitoring signs of pain has become a crucial tool for effective management and disease prevention. This paper proposes YOLOv8-SNMT, a two-stage lightweight pain recognition model focused on the facial regions of interest in pigs. In the object detection stage, the enhanced YOLOv8n-SlimNeck model optimises the Neck structure for rapid and precise localisation of pig facial regions, reducing interference from irrelevant areas. In the pain classification stage, the MobileNetV3-TACH model is introduced, replacing the traditional Squeeze-and-Excitation (SE) attention mechanism with a Triplet Attention module to form the TABneck structure. This enhancement improves cross-dimensional feature interactions, thereby enhancing the extraction of pain-related features and reducing model complexity. Additionally, the Classify-Head module of YOLOv8n is employed to further improve pain classification accuracy. On the self-constructed pig facial dataset, the proposed method achieves a facial region detection accuracy of 95.1% on the test set, surpassing YOLOv5n, YOLOv6n, and YOLOv8n by 2.4, 2.3, and 2.4 percentage points, respectively. Moreover, the model's parameter size and Floating-Point Operations (FLOPs) are significantly reduced. In the pain classification task, the MobileNetV3-TACH model achieves an accuracy of 99.7%, representing an average improvement of approximately 5.0, 3.6, 5.1, and 3.8 percentage points over EfficientNetV2, ResNet34, ShuffleNetV2, and MobileNetV3, respectively. The experimental results show that the proposed method achieves an optimal balance between classification accuracy and computational efficiency, meeting the requirements for real-time monitoring in resource-constrained environments. This offers crucial technical support for the intelligent monitoring of pig health and welfare. © 2025, The Authors. All rights reserved.
Keyword ：
Disease control Disease control mHealth mHealth
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| GB/T 7714 | Jie, Dengfei , Li, Tianle , Wang, Yang et al. A Two-Stage Lightweight Model for Pig Pain Recognition Based on Yolov8-Snmt [J]. | SSRN , 2025 . |
| MLA | Jie, Dengfei et al. "A Two-Stage Lightweight Model for Pig Pain Recognition Based on Yolov8-Snmt" . | SSRN (2025) . |
| APA | Jie, Dengfei , Li, Tianle , Wang, Yang , Jiang, Penghui , He, Jincheng , Shen, Hengwei et al. A Two-Stage Lightweight Model for Pig Pain Recognition Based on Yolov8-Snmt . | SSRN , 2025 . |
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