Biosystems Engineering最新文献_第4页

Advancing pig lameness detection with a multi-task framework: Leveraging multi-view 3D pose estimation and wavelet convolution 用多任务框架推进猪跛足检测：利用多视图三维姿态估计和小波卷积

IF 5.3 1区农林科学 Q1 AGRICULTURAL ENGINEERING

Biosystems Engineering

Pub Date : 2026-03-01 Epub Date: 2026-01-15 DOI: 10.1016/j.biosystemseng.2026.104393

Li Xiang , Haidong Wang , Zixuan Hu , Tomas Norton , Tian Jiang , Yueju Xue

Lameness in pigs presents significant challenges to animal welfare and livestock productivity, necessitating precise detection of both lameness severity and the affected limb. Conventional single-view 2D methods are often constrained by occlusions and variability in viewing angles within dynamic farm environments. Although 3D camera-based approaches provide higher accuracy, their high costs and inability to support long-term monitoring limit their practicality. To address these challenges, this study employs multi-view 3D pose estimation to reconstruct 3D skeletal of pigs from multi-view 2D video, which effectively mitigates issues related to occlusion and viewpoint dependency. Building on this, a novel multi-task classification framework, PoseGait-MT, is proposed to simultaneously detect lameness severity and identify affected limbs. The framework extracts 3D gait spatiotemporal features, including spatial tracking distance (sTRK), head bobbing amplitude (HBA), and joint flexion angles (JFA). These features are integrated into a unified low-dimensional feature map that incorporates both 3D global and relative motion trajectories. To enhance the extraction of low-frequency features and suppress high-frequency noise, wavelet convolution (WTConv) is incorporated. Experimental results demonstrated that PoseGait-MT achieved an average accuracy of 94.7 % for lameness severity classification and 95.7 % for affected limb identification with a 5-fold cross-validation, confirming its effectiveness. Additionally, on an independent test set collected from a separate pen, the model achieved 89 % and 91.4 % accuracy for the respective tasks, highlighting its robustness in real-world conditions. The proposed approach provides a practical and efficient solution for automated lameness detection in livestock farming.

猪的跛行对动物福利和牲畜生产力提出了重大挑战，需要精确检测跛行严重程度和受影响的肢体。传统的单视图2D方法经常受到遮挡和动态农场环境中视角变化的限制。尽管基于3D摄像机的方法提供了更高的精度，但它们的高成本和无法支持长期监测限制了它们的实用性。为了解决这些问题，本研究采用多视角3D姿态估计从多视角2D视频中重建猪的3D骨骼，有效地缓解了与遮挡和视点依赖相关的问题。在此基础上，提出了一种新的多任务分类框架——PoseGait-MT，以同时检测跛行严重程度和识别影响肢体。该框架提取三维步态时空特征，包括空间跟踪距离（sTRK）、头部摆动幅度（HBA）和关节屈曲角度（JFA）。这些特征被集成到一个统一的低维特征映射中，该映射包含了3D全局和相对运动轨迹。为了增强低频特征的提取和抑制高频噪声，引入了小波卷积（WTConv）。实验结果表明，经过5次交叉验证，posegat - mt对跛行严重程度分类的平均准确率为94.7%，对患肢识别的平均准确率为95.7%，证实了其有效性。此外，在从单独的笔收集的独立测试集上，该模型在各自的任务上达到了89%和91.4%的准确率，突出了其在现实世界条件下的鲁棒性。本文提出的方法为畜牧业的自动化跛行检测提供了一种实用、高效的解决方案。

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引用次数: 0

Establishment and correction of temperature-insensitive prediction model for soluble solid content of apples based on external parameter orthogonal algorithm 基于外部参数正交算法的苹果可溶性固形物含量温度不敏感预测模型的建立与校正

IF 5.3 1区农林科学 Q1 AGRICULTURAL ENGINEERING

Biosystems Engineering

Pub Date : 2026-02-01 Epub Date: 2025-12-24 DOI: 10.1016/j.biosystemseng.2025.104367

Chang Zhou, Runze Wang, Zelin Ji, Xiyin Wang, Xingshu Li, Xiufang Xiong

Temperature changes will cause fluctuations in the visible near-infrared spectra of apples, which will affect the models’ robustness and accuracy in predicting the soluble solids content (SSC) of apples. By collecting the spectra and SSC data of apples at different temperatures, this study established common single temperature models, the mixed temperature model, and the models based on the external parameter orthogonalisation (EPO) algorithm. The results indicated that EPO algorithm can effectively eliminate the influence of temperature on the spectra, and does not affect the results of the characteristic wavelengths selection. Most of the selected characteristic wavelengths were distributed in the wavelength range of 700–900 nm. The models based on the EPO algorithm were employed to predict the samples at different temperatures. The concordance correlation coefficient (CCC) ranged between 0.865 and 0.882, and the ratio of performance of deviation (RPD) was in the range of 2.045–2.191. The models based on EPO algorithm achieved high prediction accuracy and were insensitive to temperature changes. The model established by using the spectral data in the 700–900 nm band processed by EPO algorithm had high prediction accuracy and low complexity. Finally, 60 samples were used to verify the performance of the model. It was found that there was no significant difference between the predicted values and the measured values (CCC = 0.892, RMSEP = 0.543 %, RPD = 2.173). The results of the study have practical implications for improving the economic efficiency of the apple industry, including the subsequent development of portable field testing equipment.

温度变化会引起苹果可见近红外光谱的波动，影响模型预测苹果可溶性固形物含量（SSC）的稳健性和准确性。本研究通过收集苹果在不同温度下的光谱和SSC数据，建立了常见的单一温度模型、混合温度模型和基于外部参数正交化（EPO）算法的模型。结果表明，EPO算法能有效消除温度对光谱的影响，且不影响特征波长选择的结果。选取的特征波长大部分分布在700 ~ 900 nm的波长范围内。采用基于EPO算法的模型对不同温度下的样品进行预测。一致性相关系数（CCC）在0.865 ~ 0.882之间，偏离绩效比（RPD）在2.045 ~ 2.191之间。基于EPO算法的模型预测精度高，且对温度变化不敏感。利用EPO算法处理的700 ~ 900 nm波段光谱数据建立的模型预测精度高，复杂度低。最后，用60个样本来验证模型的性能。预测值与实测值无显著性差异（CCC = 0.892, RMSEP = 0.543 %, RPD = 2.173）。研究结果对提高苹果产业的经济效益具有实际意义，包括后续便携式田间测试设备的开发。

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引用次数: 0

Continuous welfare assessment of dairy cows at individual level: A farmer-oriented tool based on normal daily ranges of sensor-recorded traits 奶牛个体水平的连续福利评估：基于传感器记录特征的正常每日范围的面向农民的工具

IF 5.3 1区农林科学 Q1 AGRICULTURAL ENGINEERING

Biosystems Engineering

Pub Date : 2026-02-01 Epub Date: 2025-12-18 DOI: 10.1016/j.biosystemseng.2025.104365

Y. Gómez , N. Blasco-Andreo , A. Llabrés-Brustenga , K. Chow , J. Serra-Sagrista , G.V. Berteselli , E. Canali , X. Manteca , P. Llonch

Animal welfare on farms is currently assessed using human-evaluation protocols, which provide a single record of herd condition at a specific moment. Integrating sensor-based data and farm records, continuous information on each animal's welfare can be obtained. This study aims to create an algorithm to assess individual dairy cow welfare, contributing to the goal of building a platform to inform producers and consumers about dairy cattle welfare. It was built based on the Five Domains model of animal welfare. 221 cows from four commercial free-stall barn farms in Spain and Italy were fitted with accelerometry collars and rumen boluses and monitored for 92 days. Individual data were collected daily. Accelerometers recorded time spent ruminating, eating, lying, walking, and standing within a 24-h interval. Boluses recorded rumen pH and temperature every 10 min, averaged over 24 h. Farm records included parity, veterinary treatments, and milk conductivity. The model provides a daily global welfare index per cow, categorised into health, nutrition, and environment scores. Behaviour and mental state were not included due to a lack of relevant sensor data. Scores range from 0 to 10, indicating the likelihood of the cow experiencing welfare-compromising conditions. Normal thresholds, based on scientific literature, were set for each trait. The algorithm detected daily deviations in traits, assuming that cows with welfare issues deviate from normal behavioural and physiological patterns. When a cow's welfare index decreased, affected domains could be identified, enabling farmers to address potential welfare issues and implement corrective measures.

农场的动物福利目前使用人类评估协议进行评估，该协议提供了特定时刻畜群状况的单一记录。整合基于传感器的数据和农场记录，可以获得关于每只动物福利的连续信息。本研究旨在创建一种评估奶牛个体福利的算法，为建立一个向生产者和消费者通报奶牛福利的平台做出贡献。它是基于动物福利的五域模型建立的。对来自西班牙和意大利4个商业散养牛场的221头奶牛进行了为期92天的监测，并安装了加速计项圈和瘤胃丸。每天收集个人数据。加速度计记录了24小时间隔内反刍、进食、躺卧、行走和站立的时间。boles每10分钟记录一次瘤胃pH和温度，平均记录时间为24小时。农场记录包括胎次、兽医处理和乳传导率。该模型提供每头奶牛的每日全球福利指数，分为健康、营养和环境得分。由于缺乏相关的传感器数据，行为和精神状态未包括在内。得分范围从0到10，表示奶牛遭受福利损害的可能性。在科学文献的基础上，为每个特征设定了正常阈值。该算法检测到性状的日常偏差，假设有福利问题的奶牛偏离了正常的行为和生理模式。当奶牛的福利指数下降时，可以识别受影响的领域，使农民能够解决潜在的福利问题并实施纠正措施。

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引用次数: 0

Quantitative alignment and study of low-frequency excitation parameters based on walnut tree (Juglans regia L.) shape characteristics 基于核桃树形状特征的低频激励参数定量比对与研究

IF 5.3 1区农林科学 Q1 AGRICULTURAL ENGINEERING

Biosystems Engineering

Pub Date : 2026-02-01 Epub Date: 2025-12-11 DOI: 10.1016/j.biosystemseng.2025.104355

Jiale Cao , Heng Chen , Jianshuo An , Jingwei Wang , Latai Ga , Daochun Xu , Xiaopeng Bai , Wenbin Li

Walnut trees vary greatly in shape, which results in large differences in the optimal excitation parameters between trees. By clarifying the correlation between the tree shape characteristic parameters and the optimal excitation parameters, the excitation parameters can be matched according to the tree shape characteristics. The purpose of this is to improve picking efficiency and simultaneously reduce damage to the tree. In this study, a field experiment was conducted using a low-frequency shaker (Trunk shakers) and the vibration response under different excitation parameters was analysed using statistical methods. Finally, the range of excitation parameters based on the quantitative matching of tree feature parameters was obtained through linear regression and a comparison of feature importance. It was found that the tree height and crown projection area were the two most important factors affecting the acceleration of the tree vibration response, and they were significantly negatively correlated with each other. The optimal intervals of the excitation frequency and amplitude were successfully predicted from the tree height and canopy projection area as (ω₀η_min, ω₀η_max) and (A₀η_min/β(ω_min), A_maxη_max/β(ω_max)), respectively. Validated tests showed a prediction accuracy of 93 % for this range. This study provides a reliable range of excitation parameters for future walnut harvesting operations, which can guide the development of low-frequency intelligent shakers, in addition to field operations.

核桃树的形状差异很大，这导致不同树间的最优激励参数差异很大。通过理清树形特征参数与最优激励参数之间的相关性，可以根据树形特征匹配激励参数。这样做的目的是提高采摘效率，同时减少对树木的伤害。本研究采用低频振动筛（Trunk shakers）进行了现场试验，采用统计方法分析了不同激励参数下的振动响应。最后，通过线性回归和特征重要度比较，得到基于树形特征参数定量匹配的激励参数范围。结果表明，树高和树冠投影面积是影响树木振动响应加速的两个最重要因素，且两者之间呈显著负相关。由树高和树冠投影面积分别预测出激励频率和振幅的最佳区间为（ω ω ηmin, ω ω ηmax）和(ω ω ηmin/β(ωmin), Amaxηmax/β(ωmax))。经过验证的测试表明，该范围的预测精度为93%。该研究为未来核桃收获作业提供了可靠的激励参数范围，可以指导低频智能激振器的开发，以及现场作业。

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引用次数: 0

A tractor tyre rut depth model based on DEM-MBD coupling simulation for improved tillage depth measurement 基于DEM-MBD耦合仿真的拖拉机轮胎车辙深度模型改进耕作深度测量

IF 5.3 1区农林科学 Q1 AGRICULTURAL ENGINEERING

Biosystems Engineering

Pub Date : 2026-02-01 Epub Date: 2025-12-16 DOI: 10.1016/j.biosystemseng.2025.104361

Xiaoxiang Weng, Xiaobo Xi, Yangjie Shi, Ruihong Zhang

Real-time tillage depth information is crucial for ensuring normal seed development. However, existing measurement devices often overlook the effect of tyre rut depth on measurement errors. Taking the alluvial soil in the rice-wheat rotation area of Yangzhou, China as the study area, parameters of the Edinburgh Elasto-Plastic Adhesion (EEPA) contact model were calibrated through uniaxial compression experiments, and a soil-straw discrete element soil bin model was established. The Discrete Element Method-Multibody Dynamics (DEM-MBD) coupled simulation was employed to analyse the effects of forward speed, soil penetration resistance, and straw mulching amount on tyre rut depth. The results showed that rut depth decreased by 5.2 mm as forward speed increased from 1.5 to 7.5 km h⁻¹, by 43.8 mm as soil penetration resistance increased from 1 to 3 MPa, and by 3.9 mm as straw mulching amount increased from 0 to 1.2 kg m⁻². These results indicated that soil penetration resistance is the dominant factor affecting rut depth, while forward speed and straw mulching amount have relatively minor effects. A mathematical model was established to quantitatively predict rut depth based on soil penetration resistance, and its reliability was validated through field compaction experiments. Incorporating the rut depth model into the tillage depth measurement system significantly reduced measurement errors. Field experiments showed that higher forward speeds led to increased measurement errors and reduced operation quality. An optimal forward speed of 4.5 km h⁻¹ was recommended, at which the tillage depth measurement error was ±8.2 mm, meeting the requirements for tillage depth measurement.

实时耕作深度信息对确保种子正常发育至关重要。然而，现有的测量装置往往忽略了轮胎车辙深度对测量误差的影响。以扬州稻麦轮作区冲积土为研究区，通过单轴压缩试验对爱丁堡弹塑性粘着（EEPA）接触模型参数进行了标定，建立了土壤-秸秆离散元土仓模型。采用离散元法-多体动力学（DEM-MBD）耦合仿真分析了前进速度、土壤穿透阻力和秸秆覆盖量对轮胎车辙深度的影响。结果表明：当前进速度从1.5 km h−1增加到7.5 km h−1时，车辙深度减少5.2 mm；土壤穿透阻力从1 MPa增加到3 MPa时，车辙深度减少43.8 mm；秸秆覆盖量从0 kg m−2增加到1.2 kg m−2时，车辙深度减少3.9 mm。结果表明，土壤穿透阻力是影响车辙深度的主要因素，而前进速度和秸秆覆盖量的影响相对较小。建立了基于土壤穿透阻力定量预测车辙深度的数学模型，并通过现场压实试验验证了模型的可靠性。将车辙深度模型纳入耕深测量系统，显著降低了测量误差。现场实验表明，较高的前向速度会增加测量误差，降低操作质量。建议最佳前进速度为4.5 km h−1，在此速度下，耕深测量误差为±8.2 mm，满足耕深测量要求。

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引用次数: 0

Corrigendum to “Correlation between rheological properties and maturity of passion fruit based on machine vision” [Biosystems Engineering 250 (2025) 236–249/ 008] “基于机器视觉的百香果流变特性和成熟度之间的相关性”的勘误表[生物系统工程250 (2025)236-249 / 008]

IF 5.3 1区农林科学 Q1 AGRICULTURAL ENGINEERING

Biosystems Engineering

Pub Date : 2026-02-01 Epub Date: 2025-12-24 DOI: 10.1016/j.biosystemseng.2025.104371

Fan Lin, Dengjie Chen, Caihua Lu, Jincheng He

引用次数: 0

Influence of angle of attack on hydrodynamic performance and its implications for designing semi-mobile aquaculture structures: A CFD study 攻角对水动力性能的影响及其对半移动式水产养殖结构设计的启示：CFD研究

IF 5.3 1区农林科学 Q1 AGRICULTURAL ENGINEERING

Biosystems Engineering

Pub Date : 2026-02-01 Epub Date: 2025-12-22 DOI: 10.1016/j.biosystemseng.2025.104363

Duc Nguyen , Si Thu Paing , Sarah Wakes , Ross Vennell , Scott Rhone , Louise Kregting , Suzy Black

This study examines the influence of various angles of attack of current flow (0° to 90° with 10° intervals) on the hydrodynamic performance, surface shear stress and drag force of various designs of semi-mobile aquaculture structures. The structures were cylinder- and cone-shaped, with the porosity on the body varying from 0 % to 15 %. Computational Fluid Dynamics (CFD) was employed, and the modelled flow speed and drag force were validated with flume-based experiments on a scaled model, 2.16 m in length and 0.45 m³ in volume. It was found that when the angle of attack was 30° and 60°, flow acceleration, from 1 to 1.3

m s^{- 1}

, and low-speed zones (<0.1

m s^{- 1}

) occurred in the cylinder-shaped structure with an impermeable body (design 1A). However, flow speed was relatively uniform in the cone-shaped structure with porosity of 15 % (design 2A). When the angle of attack was 90°, flow speed in the structure increased from 0

m s^{- 1}

in design 1A to 0.4

m s^{- 1}

in design 2A. This indicated that hydrodynamics in design 2A could be more beneficial for fish welfare (increased water flow maintains dissolved oxygen) than design 1A, particularly during oblique flow conditions. However, the drag force exerted on design 2A is greater than those on design 1A. These differences in drag force increase when the flow speed increases and the volume of the structure increases, which could increase the operational costs. Overall, the findings of this work imply that designing aquaculture structures that benefit both fish and farmers is challenging, and balancing fish requirements with operational costs is crucial to select appropriate designs.

本研究考察了不同的水流攻角（0°~ 90°，间隔10°）对不同设计的半移动式水产养殖结构的水动力性能、表面剪应力和阻力的影响。结构为圆柱形和锥形，孔隙率为0% ~ 15%。采用计算流体力学（CFD）方法，在长度为2.16 m，体积为0.45 m3的比例模型上，通过基于水槽的实验验证了模型的流速和阻力。研究发现，当迎角为30°和60°时，具有不透水体的圆柱形结构（设计1A）中出现了1 ~ 1.3 ms−1的流动加速度和0.1 ms−1的低速区。而在孔隙率为15%的锥形结构中，流速相对均匀（设计2A）。当迎角为90°时，结构内的流速从设计1A的0 ms−1增加到设计2A的0.4 ms−1。这表明，与设计1A相比，设计2A中的水动力学对鱼类的福利更有利（增加水流维持溶解氧），特别是在斜流条件下。但是，设计2A的阻力大于设计1A。随着流速的增加和结构体积的增大，阻力的差异也会增大，这可能会增加操作成本。总的来说，这项工作的发现意味着设计对鱼类和农民都有利的水产养殖结构是具有挑战性的，平衡鱼类需求和运营成本对于选择适当的设计至关重要。

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引用次数: 0

Erratum to “Mitigation of greenhouse gas and ammonia emissions due to livestock housing management practices: Analysis of the DATAMAN database” [Biosystems engineering (258), September 2025, 104260] “减少牲畜饲养管理实践造成的温室气体和氨排放：DATAMAN数据库的分析”[生物系统工程（258），2025年9月，104260]

IF 5.3 1区农林科学 Q1 AGRICULTURAL ENGINEERING

Biosystems Engineering

Pub Date : 2026-02-01 Epub Date: 2025-12-19 DOI: 10.1016/j.biosystemseng.2025.104360

Vasileios Anestis , Wajid Umar , Federico Dragoni , Tony J. van der Weerden , Mélynda Hassouna , Alasdair Noble , Thomas Bartzanas , Barbara Amon

引用次数: 0

Research on a vision navigation system for rice transplanters based on machine vision fused with GNSS 基于机器视觉与GNSS融合的插秧机视觉导航系统研究

IF 5.3 1区农林科学 Q1 AGRICULTURAL ENGINEERING

Biosystems Engineering

Pub Date : 2026-02-01 Epub Date: 2025-12-11 DOI: 10.1016/j.biosystemseng.2025.104354

Zhaopeng Liu , Xulong Wu , Weiwei Gao , Fengpeng Ning , Wenyu Zhang , Jie He , Muhua Liu , Xiongfei Chen , Peng Fang

To achieve the high-precision and continuous autonomous operation of rice transplanters in complex paddy field environments, this study proposes a visual navigation system based on machine vision and GNSS. First, using the Jetson AGX Xavier platform, the BiSeNet multi-class semantic segmentation model identified side and headland ridges. A three-dimensional dynamic inverse perspective transformation and a homogeneous coordinate transformation with integrated 3D camera pose data and GNSS positioning were used to extract the navigation line coordinates of paddy ridges for machine guidance. Subsequently, the side ridge coordinates were offset to construct the first-row dynamic path planning unit, enabling the real-time tracking of ridge boundaries, while the headland ridge distance threshold triggered autonomous steering and row-shifting path planning units, allowing the rice transplanter to make decisions for continuous autonomous operation in complex paddy fields. The navigation controller was developed through secondary development, using a custom communication protocol, integrating hardware and software for the rice transplanter's autonomous operation. Finally, field tests were conducted at a speed of 0.7 m s⁻¹, demonstrating the rice transplanter's ability to autonomously travel along ridge boundaries and shift rows for continuous operation. The experimental results showed that the visual algorithm took approximately 100 ms, with an average error of 0.071 m in navigation line extraction, 0.089 m in headland distance detection, and 0.061 m in path tracking. The results indicated that the proposed visual navigation system enabled the continuous autonomous operation of the rice transplanter and thus provide significant technical support for the development of agricultural robots.

为了实现水稻插秧机在复杂水田环境下的高精度连续自主操作，本研究提出了一种基于机器视觉和GNSS的视觉导航系统。首先，利用Jetson AGX Xavier平台，BiSeNet多类语义分割模型识别了侧脊和海陆脊；利用三维动态反透视变换和三维相机位姿数据与GNSS定位相结合的齐次坐标变换，提取水稻垄导航线坐标，用于机器制导。随后，偏移侧脊坐标，构建第一行动态路径规划单元，实现对脊边界的实时跟踪，而海陆脊距离阈值触发自主转向和行移路径规划单元，使插秧机能够在复杂水田中进行连续自主作业决策。导航控制器通过二次开发，采用自定义通信协议，将硬件和软件集成在一起，实现插秧机的自主操作。最后，以0.7 m s - 1的速度进行了田间试验，证明了水稻插秧机能够自主沿着山脊边界移动并连续移动行。实验结果表明，视觉算法耗时约100 ms，导航线提取平均误差为0.071 m，海岬距离检测平均误差为0.089 m，路径跟踪平均误差为0.061 m。结果表明，该视觉导航系统实现了水稻插秧机的连续自主操作，为农业机器人的发展提供了重要的技术支持。

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引用次数: 0

An enhanced cow-teat-detection model for automatic teat-cup-attachment devices 一种用于自动奶杯附着装置的增强奶牛奶头检测模型

IF 5.3 1区农林科学 Q1 AGRICULTURAL ENGINEERING

Biosystems Engineering

Pub Date : 2026-02-01 Epub Date: 2025-12-23 DOI: 10.1016/j.biosystemseng.2025.104374

Mengjie Li , Juan Wang , Na Li , Yaju Liu , Xue Cheng , Xinpei Fu , Sirui Li

The production mode in which a milking device is manually set on the teats of dairy cows is inefficient and labour-intensive. Automatic teat-cup-attachment technology can reduce the labour-intensive and increase milking efficient. In order to automatically attach the teat cup more accurately and quickly, an enhanced cow-teat-detection model was proposed, which was utilized in an automatic teat-cup-attachment device. Based on the YOLOv7, the Wise-IoU loss function was introduced, which enhanced the fitting ability and convergence speed of the bounding box regression. To optimize the captures pertaining to the features of cow teats in a complex background and to reduce the loss of the features from a small target, a fourth feature scale was constructed in the Neck network. To make the model focus more on the detailed information related to the target and enhance the target-recognition accuracy, a Coordinate Attention (CA) mechanism was added to the Backbone network and to the Neck network. The comparison experiment and ablation experiment were performed on the self-made dataset. The results revealed that the mAP_0.5, Precision, and Recall of the improved YOLOv7 were 98.18 %, 97.50 %, and 96.15 %, respectively, which were 1.96, 2.07, and 2.67 percentage points higher than those of the baseline YOLOv7 model, respectively. The detection performance was significantly enhanced. This study provides the technical support for the milking device to achieve accurate positioning and rapid automatic teat-cup-attachment.

在奶牛的乳头上手动设置挤奶设备的生产模式效率低下，劳动密集型。自动贴杯技术，减少劳动强度，提高挤奶效率。为了更准确、快速地自动贴贴奶杯，提出了一种增强的奶牛奶头检测模型，并将其应用于自动贴贴奶杯装置中。在YOLOv7的基础上，引入Wise-IoU损失函数，增强了边界盒回归的拟合能力和收敛速度。为了优化复杂背景下奶牛乳头特征的捕获，减少小目标特征的丢失，在颈部网络中构建了第四个特征尺度。为了使模型更加关注与目标相关的详细信息，提高目标识别的精度，在主干网络和颈部网络中加入了坐标注意（CA）机制。在自制数据集上进行了对比实验和烧蚀实验。结果表明，改进后的YOLOv7模型的mAP0.5、Precision和Recall分别为98.18%、97.50%和96.15%，分别比基线YOLOv7模型提高了1.96、2.07和2.67个百分点。检测性能显著提高。本研究为挤奶装置实现准确定位和快速自动贴杯提供了技术支持。

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引用次数: 0