Biosystems Engineering最新文献_第8页

Design and performance analysis of an electric threshing drum and its speed control system for combine harvester 联合收割机电动脱粒鼓及其调速系统的设计与性能分析

IF 5.3 1区农林科学 Q1 AGRICULTURAL ENGINEERING

Biosystems Engineering

Pub Date : 2025-10-18 DOI: 10.1016/j.biosystemseng.2025.104313

Zhihao Zhu , Xiaoyu Chai , Lizhang Xu , Li Quan , Chaochun Yuan , En Lu , Shuofeng Weng

In response to the challenges encountered by traditional combine harvester threshing drums, such as the difficulty in maintaining speed stability amidst load fluctuations leading to increased entrainment loss, and the difficulty in adjusting speed across a wide range with varying crop types and properties, this study introduces an electric threshing drum (ETD) for combine harvesters. A motor control strategy leveraging a fractional-order non-singular sliding mode observer (FONSSMO) and model predictive control (MPC) is developed to achieve precise speed control of the ETD under load fluctuations and disturbances, with its parameters optimised using a genetic algorithm. Simulation results demonstrate that the ETD can achieve swift and accurate speed regulation. In the presence of torque interference and load transients, the proposed control method reduces the RMS value of the threshing drum's speed error by 73.96 % and the maximum speed error by 75.10 %, significantly enhancing the system's robustness. The bench test validated the efficacy and feasibility of the control strategy. Furthermore, field test results from rice harvesting showed that the combine harvester equipped with the ETD can mitigate entrainment loss rate fluctuations due to changes in vehicle speed and load compared to conventional models. At a vehicle speed of 2 m s⁻¹, it can reduce entrainment loss by ∼19–20 %, effectively enhancing harvesting efficiency.

针对传统联合收割机脱粒鼓所面临的挑战，如在负荷波动中难以保持速度稳定，导致携带损失增加，以及难以在不同作物类型和特性的大范围内调节速度，本研究介绍了一种用于联合收割机的电动脱粒鼓（ETD）。利用分数阶非奇异滑模观测器（FONSSMO）和模型预测控制（MPC）的电机控制策略，利用遗传算法对参数进行优化，实现了负载波动和干扰下ETD的精确速度控制。仿真结果表明，ETD可以实现快速、准确的调速。在存在转矩干扰和负载瞬变的情况下，该控制方法使脱粒滚筒转速误差的均方根值降低了73.96%，最大转速误差降低了75.10%，显著增强了系统的鲁棒性。台架试验验证了控制策略的有效性和可行性。此外，水稻收获的田间试验结果表明，与传统模式相比，配备ETD的联合收割机可以减轻由于车辆速度和负载变化而导致的携载损失率波动。在车速为2 m s−1时，它可以减少约19 - 20%的携带损失，有效提高收获效率。

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引用次数: 0

Assessment of light environment conditions for reconstruction of multispectral images by conditional adversarial networks 基于条件对抗网络的多光谱图像重建光环境条件评估

IF 5.3 1区农林科学 Q1 AGRICULTURAL ENGINEERING

Biosystems Engineering

Pub Date : 2025-10-15 DOI: 10.1016/j.biosystemseng.2025.104305

Tito Arevalo-Ramirez , Oswaldo Menéndez , Juan Villacrés , Javier Guevara , Robert Guamán-Rivera , Rodrigo Demarco , Fernando Auat Cheein

Understanding vegetation through its reflectance in the visible and near-infrared spectrum is vital for gaining biophysical and structural insights about vegetation. However, the spectral reflectance on meaningful bands (e.g., red-edge, near-infrared) is not always available because of the camera's spectral response restrictions. In this context, previous research addresses the lack of multispectral information by reconstructing it using deep-learning approaches. Although there are promising outcomes, the influence of varying illumination conditions on this process still needs to be explored. Thus, this work examines if conditional Generative Adversarial Networks (cGANs) can infer environment illumination for achieving an appropriate multispectral image reconstruction. In particular, the spectral reconstruction performance of cGANs is investigated under six different scenarios with different illumination (occurring over a whole day), focusing on green, red-edge, and near-infrared bands. Note that the dataset used for this research has become publicly available. These results indicated that illumination conditions influenced the performance of cGAN models in generating spectral images. Specifically, the cGANs could not infer the source image illumination to output a reliable reconstructed spectral image. Furthermore, although including samples under different illumination improved cGANs' performance, the generated multispectral images tended to be darker than actual images.

通过植被在可见光和近红外光谱中的反射率来了解植被对于获得植被的生物物理和结构知识至关重要。然而，由于相机的光谱响应限制，在有意义的波段（如红边、近红外）的光谱反射率并不总是可用的。在此背景下，先前的研究通过使用深度学习方法重建多光谱信息来解决多光谱信息的缺乏。虽然有很好的结果，但不同的光照条件对这一过程的影响仍然需要探索。因此，这项工作考察了条件生成对抗网络（cgan）是否可以推断环境照明，以实现适当的多光谱图像重建。特别地，研究了cgan在6种不同光照情况下（全天发生）的光谱重建性能，重点研究了绿色、红边和近红外波段。请注意，本研究使用的数据集已经公开可用。这些结果表明，光照条件会影响cGAN模型生成光谱图像的性能。具体来说，cgan无法推断出源图像的照明来输出可靠的重建光谱图像。此外，尽管包含不同光照下的样本可以提高cgan的性能，但生成的多光谱图像往往比实际图像更暗。

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引用次数: 0

Expert Opinion (∗): Improving process knowledge about ammonia emission from field applied liquid manure 专家意见（*）：提高有关田间施用液体肥料排放氨的工艺知识

IF 5.3 1区农林科学 Q1 AGRICULTURAL ENGINEERING

Biosystems Engineering

Pub Date : 2025-10-13 DOI: 10.1016/j.biosystemseng.2025.104304

Sven G. Sommer , Shabtai Bittman , Andreas Pacholski

Agriculture contributes about 80 % of global ammonia (NH₃) emissions which must therefore be reduced to tackle issues of global human health as well as biodiversity and fertilizer loss. We carried out a review of current knowledge about processes of NH₃ emissions from field applied liquid manure, which is the basis for our opinion about the need for better process understanding. Detailed information on the reviewed literature is given (supplementary material). We recommend process based research about NH₃ emission effects of a. soil surface pH and temperature on concentration of dissolved NH₃(aq); b. Liquid manure dry matter concentration (DM) and soil cation exchange; c. Liquid manure viscosity and hydrophobicity interacting with soil water content and texture; and d. Effects of weather conditions as solar radiation, wind, canopy and rainfall/irrigation.

农业贡献了全球约80%的氨（NH3）排放量，因此必须减少，以解决全球人类健康以及生物多样性和肥料损失问题。我们对目前关于田间施用液肥NH3排放过程的知识进行了回顾，这是我们认为需要更好地了解过程的基础。给出了所审查文献的详细信息（补充材料）。建议基于过程研究土壤表面pH和温度对NH3溶解浓度（aq）的影响；b.液肥干物质浓度（DM）与土壤阳离子交换；c.粪液粘度和疏水性与土壤含水量和质地的相互作用；d.天气条件的影响，如太阳辐射、风、树冠和降雨/灌溉。

引用次数: 0

Assessing thermal comfort for day-old broilers: A novel thermal stress index using computer vision 日龄肉鸡热舒适评估：一种基于计算机视觉的新型热应激指数

IF 5.3 1区农林科学 Q1 AGRICULTURAL ENGINEERING

Biosystems Engineering

Pub Date : 2025-10-10 DOI: 10.1016/j.biosystemseng.2025.104302

Cui Gao , Özge Günaydın , Mina Mounir , Hilke Willemsen , Fien Vanlerberghe , Eduardo Romanini , Tomas Norton

Thermal stress (heat and cold stress) poses a significant challenge to day-old broilers during temporary post-hatch storage before transportation. This study presents a computer vision-based approach for automatic assessment of flock-level behavioural responses. Two behavioural indices were developed: an improved Cluster Index (CI) that incorporates convex hull area and temporal variability to quantify aggregation under cold stress, and a Temperature Stress Index (TSI) that integrates CI with an Activity Index (AI) from Gaussian Mixture Model analysis to capture activity responses to thermal stress. Experiments were conducted under controlled cold-to-hot (CH) and hot-to-cold (HC) temperature profiles using thermal and night vision cameras, with 48 chicks across four trials. For thermal image segmentation, an HRNet model achieved a final mean IoU of 93.37 %. Linear mixed models showed significant temperature effects on all three indices (p < 0.01): CI increased significantly under extreme cold (31.7 °C, p < 0.01) and semi-cold (34.4 °C_1, p < 0.01), while AI increased significantly in semi-hot (40.0 °C_1, p < 0.01) and extreme hot (42.8 °C, p < 0.01) phases. TSI was significantly elevated under extreme cold (31.7 °C, p < 0.01), semi-hot (40.0 °C_1, p < 0.01), and extreme hot (42.8 °C, p < 0.01), demonstrating its ability to detect both extreme heat and cold stress more comprehensively than AI or CI alone. Despite the initial investment in cameras and computing, this non-invasive and camera-compatible method is cost-effective compared with continuous manual monitoring or invasive physiological assays, enabling timely detection of thermal stress in day-old chicks.

热应激（热应激和冷应激）对日龄肉鸡在运输前的临时孵化后储存过程中构成了重大挑战。本研究提出了一种基于计算机视觉的群体行为反应自动评估方法。研究人员开发了两种行为指数：一种是改进的聚类指数（CI），它包含凸壳面积和时间变异性，以量化冷应激下的聚集；另一种是温度应激指数（TSI），它将CI与高斯混合模型分析的活动指数（AI）结合起来，以捕捉活动对热应激的反应。采用热像仪和夜视仪对48只雏鸡进行了4个试验，分别在可控的冷到热（CH）和热到冷（HC）温度剖面下进行了试验。对于热图像分割，HRNet模型的最终平均IoU为93.37%。线性混合模型对3个指数均有显著的温度效应（p < 0.01）： CI在极冷（31.7°C, p < 0.01）和半冷（34.4°C, p < 0.01）阶段显著升高，AI在半热（40.0°C, p < 0.01）和极热（42.8°C, p < 0.01）阶段显著升高。在极冷（31.7°C, p < 0.01）、半热（40.0°C, p < 0.01）和极热（42.8°C, p < 0.01）条件下，TSI显著升高，表明其能够比单独AI或CI更全面地检测极热和极冷胁迫。尽管在摄像机和计算机上进行了初步投资，但与连续人工监测或侵入性生理检测相比，这种非侵入性且与摄像机兼容的方法具有成本效益，能够及时检测日龄雏鸡的热应激。

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引用次数: 0

Design and optimisation of a distribution header in an alfalfa air-blown seed metering device using CFD-DEM coupled simulation 利用CFD-DEM耦合模拟设计和优化苜蓿风吹式种子计量装置中的分配集箱

IF 5.3 1区农林科学 Q1 AGRICULTURAL ENGINEERING

Biosystems Engineering

Pub Date : 2025-10-10 DOI: 10.1016/j.biosystemseng.2025.104306

Changbin He , Dejin Qiao , Chaoyu Du , Hongwei Jing , Yanying Guo

Pneumatic seed metering devices have become increasingly important in modern agriculture and forage production. This paper presents a novel approach to enhancing the efficiency of alfalfa seed planting through the development of an optimised distribution header in an air-blown seed metering device. By employing a coupled Computational Fluid Dynamics (CFD) and Discrete Element Method (DEM) simulation, the study explores the fluid mechanics principles underlying pneumatic seed-metering technologies. Subsequently, by analysing the dynamic interactions between the air flow and seeds movement, and combining single-factor and orthogonal experiments, the research identified optimal design parameters that maximised the uniformity in the seed distribution. Finally, a test bench was constructed and related indoor experiments were conducted to validate the performance. The results indicated that the air velocity, the cone-apex angle, and the inlet tube diameter all affected the moving state of the air-seed flow in the distributor. The influence on the variation coefficient of seeds distribution (CV) was ranked as follows: top cover cone angle (θ₂) > outlet pipe inclination angle (θ₁) > inlet diameter (D). The optimal structure parameters were θ₂ = 120°, θ₁ = 60°, and D = 50 mm. With these values, the average CV was 7.69 %, which is appropriate for alfalfa seeding for pneumatic no-till seeders. The findings provide an efficient solution for seeding pasture seeds that are characterised by an irregular shape, small particles, and poor mobility.

气动排种装置在现代农业和饲料生产中发挥着越来越重要的作用。本文提出了一种新的方法来提高苜蓿种子种植效率，通过开发一个优化的分配头在一个风吹种子计量装置。通过耦合计算流体动力学（CFD）和离散元法（DEM）模拟，研究了气动种子计量技术的流体力学原理。随后，通过分析气流与种子运动之间的动态相互作用，结合单因素试验和正交试验，确定了最大限度地提高种子分布均匀性的最佳设计参数。最后搭建了试验台，并进行了相关的室内实验验证。结果表明，气流速度、锥尖角和进口管径均影响分布器内空气种子流的运动状态。对种子分布变异系数（CV）的影响排序为：顶盖锥角（θ2） >；出口管道倾角（θ1） >；进口直径(D)。最佳结构参数为θ2 = 120°，θ1 = 60°，D = 50 mm。在此条件下，平均CV值为7.69%，适用于苜蓿气动免耕播种机播种。这一发现为那些形状不规则、颗粒小、流动性差的牧草种子的播种提供了一种有效的解决方案。

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引用次数: 0

Development of a FDEM model for cotton stalks based on biomechanical and structural properties 基于生物力学和结构特性的棉花秸秆FDEM模型的建立

IF 5.3 1区农林科学 Q1 AGRICULTURAL ENGINEERING

Biosystems Engineering

Pub Date : 2025-10-08 DOI: 10.1016/j.biosystemseng.2025.104301

Xirui Yang , Peiyu Chen , Xingyu Wang , Haiyang Liu , Chen Ding , Haipeng Lan , Hong Zhang

In the mechanised recovery process of drip irrigation tapes, the crossing of side branches and lodged cotton stalks (CS) are significant factors contributing to the increased breakage rate. Effectively cutting the cotton stalks is key to solving this issue. Constructing a multi-scale numerical model of cotton stalks is a breakthrough method for studying their fracture process. This study analyses the mechanical properties and structural composition of different sections of cotton stalk. Based on this, adopts a finite-discrete element method (FDEM) combined with zero-thickness cohesive elements to construct a numerical model of the cotton stalk, and the model's accuracy was validated through random sampling. The results indicate that the proportion of lignified tissues in the CS gradually decreases from the base to the top, and the load-bearing capacity of the CS shifts from strong compressive and shear resistance to greater ductility and tensile strength, while the compressive force values exhibit a decreasing trend with the presence of jump points, the average values of which are located at 14.5 % (base), 56.2 % (middle), and 29.3 % (top) of the CS length. The constructed composite model of the cotton stalk accurately replicates its mechanical properties, force transfer, and damage evolution processes. The Spearman correlation coefficients for the random sampling simulation and experimental validation of CS at different sections reached 0.9998 (base) and 0.9207 (top), respectively. This study offers a groundbreaking method for investigating CS damage mechanisms and can also offer technical guidance for the recovery of drip irrigation tapes under this irrigation management model.

在滴灌带机械化回收过程中，侧枝交叉和棉秆滞留是导致滴灌带破损率增加的重要因素。有效地剪断棉秆是解决这一问题的关键。构建棉花秸秆多尺度数值模型是研究棉花秸秆断裂过程的突破性方法。本文分析了棉秆不同节段的力学性能和结构组成。在此基础上，采用有限离散元法（FDEM）结合零厚度内聚单元构建了棉秆的数值模型，并通过随机抽样验证了模型的准确性。结果表明：从底部到顶部，碳纤维中木质化组织的比例逐渐减小，碳纤维的承载能力由抗压、抗剪能力强向延性、抗拉能力强转变，而抗压力值则呈现下降趋势，其平均值分别位于碳纤维长度的14.5%（底部）、56.2%（中部）和29.3%（顶部）。所构建的复合模型准确地复制了棉秆的力学性能、力传递和损伤演化过程。CS在不同断面的随机抽样模拟和实验验证的Spearman相关系数分别达到0.9998（底部）和0.9207（顶部）。本研究为探究CS损伤机制提供了开创性的方法，也可为该灌溉管理模式下滴灌带的修复提供技术指导。

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引用次数: 0

Investigation on heat transfer characteristic of deformable high aspect-ratio biomass particle system in rotary drums with baffles 可变形高长径比生物质颗粒系统挡板转鼓传热特性研究

IF 5.3 1区农林科学 Q1 AGRICULTURAL ENGINEERING

Biosystems Engineering

Pub Date : 2025-10-01 DOI: 10.1016/j.biosystemseng.2025.104299

Conghui Gu , Yufan Zhang , Yuan Liu , Huiqian Yin , Xingyu Liang , Jingyu Zhu , Kaiyuan Deng , Zhulin Yuan

With the development of carbon reduction technologies, the energy conversion efficiency of the wet biomass particle system has attracted increasing attention. Heat transfer within these systems is a critical factor influencing combustion performance. In this study, experimental and simulation methods were used to investigate the behaviour of deformable high aspect-ratio biomass particles within different structural rotary drums. The particle heat transfer model of deformable high aspect-ratio biomass particles was established and validated. By comparing the simulation results with experimental data, good consistency was demonstrated. It systematically investigates the effects of drum rotation speed, the number of baffles, and drum temperature on the mixing and heat transfer processes of the biomass particle system. It was found that increasing the drum rotation speed facilitates heat transfer in the particles. The best heat transfer efficiency was observed at a speed of 10 r min⁻¹. As the mixing index improved, the heat exchange between particles became more efficient, and higher mixing index led to improved heat transfer efficiency. The highest mixing index reached 0.55, resulting in a notable increase in temperature. Moreover, increasing the number of baffles improved the uniformity of the particle temperature distribution, and optimising the drum temperature further enhanced heat transfer. The optimal results were achieved with 6 baffles and the drum wall temperature of 403.15 K. These findings provide valuable insights for improving energy efficiency in biomass systems.

随着碳减排技术的发展，湿生物质颗粒系统的能量转换效率越来越受到人们的关注。这些系统内的传热是影响燃烧性能的关键因素。在这项研究中，采用实验和模拟的方法来研究可变形的高纵横比生物质颗粒在不同结构的旋转鼓中的行为。建立并验证了可变形高展弦比生物质颗粒的传热模型。通过仿真结果与实验数据的比较，证明了较好的一致性。系统地研究了转鼓转速、挡板数和转鼓温度对生物质颗粒系统混合和传热过程的影响。结果表明，提高转鼓转速有利于颗粒内的传热。在速度为10 r min−1时，传热效率最高。随着混合指数的提高，颗粒间的换热效率提高，混合指数越高，换热效率越高。混合指数最高达到0.55，导致温度显著升高。此外，增加挡板的数量改善了颗粒温度分布的均匀性，优化转鼓温度进一步增强了传热。当挡板6个，鼓壁温度为403.15 K时，效果最佳。这些发现为提高生物质系统的能源效率提供了有价值的见解。

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引用次数: 0

Variations of multiple environmental factors and multi-objective optimisation control in a pig house 猪舍多环境因素变化与多目标优化控制

IF 5.3 1区农林科学 Q1 AGRICULTURAL ENGINEERING

Biosystems Engineering

Pub Date : 2025-10-01 DOI: 10.1016/j.biosystemseng.2025.104300

Xiaofei Sun , Shengchao Wang , Qiuju Xie , Congcong Sun , Haiming Yu , Wenfeng Wang

With the increasing scale and intensification of pig farming, the environmental quality of pig houses has become crucial for pigs' health and reproductive ability. Numerous environmental factors in the pig house have formed a complex nonlinear microclimate environment that is dynamic, time-varying, and coupled, making it difficult to achieve precise control of the environment. In this study, sixty days of indoor environmental data were collected by an Internet of Things platform to analyse the diurnal changes in indoor temperature, humidity, and concentrations of CO₂ and NH₃, as well as the seasonal changes in winter and summer. A multi-objective control strategy for optimising the pig house environment using Double Deep Q-Network (DDQN) was established. The results showed that 1) there were significantly higher correlations between multiple environmental factors in summer than in winter; 2) the determination coefficients R² of the multiple linear regression models constructed with indoor temperature and CO₂ concentration as dependent variables reached 0.915 and 0.778, respectively; 3) the DDQN control strategy kept the indoor temperature variation within ±1.7 °C, compared to ±2.1 °C with the traditional temperature threshold control strategy (TTCS); 4) the total running time of the three fans in a day under the DDQN control strategy was 28.01 h, with the total power consumption of 11.4 kWh, and an energy-saving rate of 7.39 % and the indoor various environmental factors are closer to the setting values. This research offers feasible reference for the intelligent and energy-saving environmental regulation in large-scale pig production for precise environmental control.

随着生猪养殖规模和集约化程度的不断提高，猪舍环境质量对猪的健康和繁殖能力至关重要。猪舍内众多的环境因素形成了一个复杂的非线性、动态、时变、耦合的小气候环境，难以实现对环境的精确控制。本研究通过物联网平台采集60 d室内环境数据，分析室内温度、湿度、CO2和NH3浓度的日变化以及冬、夏两季的季节变化。建立了基于双深度q网络（DDQN）的猪舍环境多目标优化控制策略。结果表明：1)夏季多环境因子间的相关性显著高于冬季；2)以室内温度和CO2浓度为因变量构建的多元线性回归模型的决定系数R2分别达到0.915和0.778；3) DDQN控制策略将室内温度变化控制在±1.7℃以内，而传统温度阈值控制策略（TTCS）将室内温度变化控制在±2.1℃以内；4) DDQN控制策略下三台风机一天总运行时间为28.01 h，总功耗为11.4 kWh，节能率为7.39%，室内各环境因子更接近设定值。本研究为规模化养猪生产中智能化、节能化的环境调控提供了可行的参考。

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引用次数: 0

CKSP: Cross-species knowledge sharing and preserving for universal animal activity recognition CKSP：跨物种知识共享和保护，用于普遍的动物活动识别

IF 5.3 1区农林科学 Q1 AGRICULTURAL ENGINEERING

Biosystems Engineering

Pub Date : 2025-10-01 DOI: 10.1016/j.biosystemseng.2025.104303

Axiu Mao , Meilu Zhu , Zhaojin Guo , Zheng He , Tomas Norton , Kai Liu

Deep learning techniques are dominating automated animal activity recognition (AAR) tasks with wearable sensors due to their high performance on large-scale labelled data. However, current deep learning-based AAR models are trained solely on datasets of individual animal species, constraining their applicability in practice and performing poorly when training data are limited. In this study, a one-for-many framework is proposed, dubbed Cross-species Knowledge Sharing and Preserving (CKSP), based on sensor data from diverse animal species. Given the coexistence of generic and species-specific behavioural patterns among different species, a Shared-Preserved Convolution (SPConv) module is designed. This module assigns an individual low-rank convolutional layer to each species for extracting species-specific features and employs a shared full-rank convolutional layer to learn generic features. This enables the CKSP framework to learn inter-species complementarity and alleviates data limitations via increasing data diversity. Considering the training conflict arising from discrepancies in data distributions among species, a Species-specific Batch Normalisation (SBN) module is devised that involves multiple BN layers to separately fit the distributions of different species. To validate CKSP's effectiveness, experiments are performed on three public datasets from horses, sheep, and cattle, respectively. The results show that this approach remarkably boosts the classification performance compared to the baseline method (one-for-one framework) solely trained on individual-species data, with increments of 6.04 %, 2.06 %, and 3.66 % in accuracy, and 10.33 %, 3.67 %, and 7.90 % in F1-score for the horse, sheep, and cattle datasets, respectively. This proves the promising capabilities of the method in leveraging multi-species data to augment classification performance.

深度学习技术由于其在大规模标记数据上的高性能，在可穿戴传感器的自动动物活动识别（AAR）任务中占据主导地位。然而，目前基于深度学习的AAR模型仅在单个动物物种的数据集上进行训练，这限制了其在实践中的适用性，并且在训练数据有限的情况下表现不佳。在本研究中，基于不同动物物种的传感器数据，提出了一个“一对多”的框架，称为跨物种知识共享和保护（CKSP）。考虑到不同物种间的共性行为模式和物种特异性行为模式并存，设计了共享保存卷积（SPConv）模型。该模块为每个物种分配一个单独的低阶卷积层来提取物种特有的特征，并使用一个共享的全阶卷积层来学习通用特征。这使得CKSP框架能够学习物种间的互补性，并通过增加数据多样性来缓解数据限制。考虑到物种间数据分布差异引起的训练冲突，设计了一个物种特异性批归一化（SBN）模块，该模块涉及多个批归一化层，分别拟合不同物种的分布。为了验证CKSP的有效性，分别在马、羊和牛的三个公共数据集上进行了实验。结果表明，与单物种数据训练的基准方法（一对一框架）相比，该方法的分类准确率分别提高了6.04%、2.06%和3.66%，马、羊和牛的f1得分分别提高了10.33%、3.67%和7.90%。这证明了该方法在利用多物种数据来增强分类性能方面的潜力。

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引用次数: 0

FEM-SPG coupling modelling and reinforced soil effect of maize root-soil composite 玉米根-土复合材料FEM-SPG耦合模型及加筋土效应

IF 5.3 1区农林科学 Q1 AGRICULTURAL ENGINEERING

Biosystems Engineering

Pub Date : 2025-10-01 DOI: 10.1016/j.biosystemseng.2025.104293

Yiwen Yuan , Mingxuan Du , Yanlin Zhou , Yueqian Yang , Xin Zhang , Shuhong Zhao

Root-soil composites, formed by root stubble remaining in the field and the surrounding soil, are major factors affecting the quality of maize seeding operations under conservation tillage system. Addressing this problem requires the development of efficient stubble management equipment. A dynamic simulation model of the cutting effect of a blade is established by coupling finite element method (FEM) and smoothed particle Galerkin (SPG) models. After drawing 3D solid models, soil was discretised into SPG particles, and stubble was divided into finite meshes and given different material properties respectively based on their measured data. Based on the mechanical properties of fibre-reinforced composite material, soil and stubble model were bonded as a composite by contact setting. The coupling between FEM and SPG and the non-uniqueness problem of this contact model were dealt with by the co-nodes. Direct shear simulations at two positions of the maize root-soil composite were carried out using SPG-FEM model and verified experimentally by in-situ tests. Test results showed that shear stress and force predicted by SPG-FEM model were in good agreement with the measured results. The results showed that the reinforcement ability of roots to soil was related to their contact area with soil and distribution uniformity. The effective circumference was shown to be able to predict the depth of maximum shear strength of root-soil composite. These current research results are useful for improving the reliability of numerical simulations of crop root-soil composite and optimising agricultural operation components.

保留在田间的根茬与周围土壤形成的根土复合物是影响保护性耕作制度下玉米播种作业质量的主要因素。解决这一问题需要开发高效的残茬管理设备。采用有限元法（FEM）和光滑粒子伽辽金（SPG）模型相结合的方法，建立了叶片切削效果的动态仿真模型。在绘制三维实体模型后，将土壤离散为SPG颗粒，并根据其实测数据将残茬划分为有限网格，分别赋予其不同的材料属性。基于纤维增强复合材料的力学特性，采用接触设置的方法将土体与残茬模型粘结为复合材料。利用协节点处理了有限元法与SPG法的耦合问题以及该接触模型的非唯一性问题。采用SPG-FEM模型对玉米根土复合材料的两个位置进行了直接剪切模拟，并通过现场试验进行了验证。试验结果表明，SPG-FEM模型预测的剪切应力和剪切力与实测结果吻合较好。结果表明，根系对土壤的加固能力与其与土壤的接触面积和分布均匀性有关。有效周长可以预测根土复合材料的最大抗剪强度深度。这些研究成果对于提高作物根系-土壤复合数值模拟的可靠性和优化农业经营要素具有重要意义。

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