Biosystems Engineering最新文献_第5页

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 : 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

Optimising refrigerated container cooling performance: A virtual modelling approach for temperature and quality management in fresh fruit cold chain 优化冷藏容器冷却性能：在新鲜水果冷链温度和质量管理的虚拟建模方法

IF 5.3 1区农林科学 Q1 AGRICULTURAL ENGINEERING

Biosystems Engineering

Pub Date : 2025-12-16 DOI: 10.1016/j.biosystemseng.2025.104357

Chijioke Leonard Nkwocha , Alemayehu Ambaw Tsige , Buhle Maphosa , Corné Coetzee , Umezuruike Linus Opara

Effective temperature management in refrigerated containers (reefers) is essential for preserving fruit quality during long-distance cold chain transport. This study employs a virtual modelling approach to assess and optimise reefer cooling performance, focusing on spatial and temporal temperature variations within a fully loaded container transporting fresh fruit. Time-series data was obtained from embedded sensors which monitored fruit core and ambient air temperatures at six locations: front left (FL), front right (FR), middle left (ML), middle right (MR), back left (BL), and back right (BR). Results revealed pronounced thermal asymmetry, with the left side, especially FL and ML, exhibiting the slowest cooling response, remaining above 1 °C for nearly 190 h, compared to under 130 h for MR and BL. This disparity, only partly attributable to minor differences in pallet airflow resistance, may be influenced by external heat gain, potentially due to solar exposure depending on container orientation during maritime transport. Crucially, the study recommends using fruit-core temperature from a pallet near the container door as a key indicator of residual cooling needs and pre-optimality prior to shipment. It also highlights the importance of accounting for apparent ambient temperature and time–temperature exposure before vessel departure in determining proper loading conditions. Further experimental work is encouraged to investigate the influence of container placement on the ship deck and its interaction with external environmental conditions.

在长途冷链运输中，有效的冷藏容器（冷藏箱）温度管理对于保持水果品质至关重要。本研究采用虚拟建模方法来评估和优化冷藏箱冷却性能，重点关注运输新鲜水果的满载集装箱内的空间和时间温度变化。时间序列数据来自嵌入式传感器，这些传感器监测果核和六个位置的环境空气温度：前左（FL）、前右（FR）、中左（ML）、中右（MR）、后左（BL）和后右（BR）。结果显示了明显的热不对称，左侧，特别是FL和ML，表现出最慢的冷却响应，保持在1°C以上近190小时，而MR和BL则低于130小时。这种差异仅部分归因于托盘气流阻力的微小差异，可能受到外部热增益的影响，可能是由于海上运输过程中集装箱朝向的太阳照射。至关重要的是，该研究建议使用集装箱门附近托盘的果核温度作为运输前剩余冷却需求和预优化的关键指标。它还强调了在船舶出发前考虑表观环境温度和时间温度暴露的重要性，以确定适当的装载条件。鼓励进一步的实验工作来研究集装箱放置对船舶甲板的影响及其与外部环境条件的相互作用。

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引用次数: 0

Enhancing trajectory overlap of follower robots via a novel circular constraint following paradigm in human-robot collaborative fruit harvesting 在人-机器人协同水果收获中，利用一种新的圆形约束跟随范式增强跟随机器人的轨迹重叠

IF 5.3 1区农林科学 Q1 AGRICULTURAL ENGINEERING

Biosystems Engineering

Pub Date : 2025-12-15 DOI: 10.1016/j.biosystemseng.2025.104359

Hengda Li, Liang Sun, Zhenghao Li, Ying Chen, Haichao Li, Pingyi Liu

To enhance the efficiency and automation of collaborative fruit harvesting, this study investigates follower robots operating in greenhouse environments. It identifies the trajectory offset problem inherent in the traditional directional constraint following paradigm (DCFP), which limits its applicability in confined spaces. Through theoretical analysis, simulation, and experimental validation, the study reveals the underlying mechanisms of trajectory offset, showing that the deviation increases with both the predefined following distance and the curvature of the human path. To address this issue, a novel following paradigm, the circular constraint following paradigm (CCFP), is proposed along with a detailed implementation method. Field experimental results in a greenhouse peach orchard demonstrate that CCFP achieves lower trajectory offset than DCFP, as quantified by a 32.9 % reduction in average matching cost (AMC) across four typical paths. This indicates that CCFP offers a higher trajectory overlap, enabling collision-free navigation with only the prior path provided by the picker, thus avoiding the need for complex obstacle avoidance systems. Additionally, CCFP is compatible with existing positioning and control models, offering a modular and scalable framework for the future development of agricultural following robots in constrained environments.

为了提高协同水果收获的效率和自动化程度，本研究研究了在温室环境下操作的跟随机器人。它识别了传统定向约束跟随范式（DCFP）固有的轨迹偏移问题，这限制了其在受限空间中的适用性。通过理论分析、仿真和实验验证，揭示了轨迹偏移的潜在机制，表明轨迹偏移随预定跟随距离和人类路径曲率的增加而增加。为了解决这一问题，本文提出了一种新的跟随范式，即循环约束跟随范式（CCFP），并给出了详细的实现方法。温室桃园的田间试验结果表明，CCFP比DCFP实现了更低的轨迹偏移，在四个典型路径上的平均匹配成本（AMC）降低了32.9%。这表明CCFP提供了更高的轨迹重叠，仅使用拾取器提供的先验路径就可以实现无碰撞导航，从而避免了对复杂避障系统的需求。此外，CCFP与现有的定位和控制模型兼容，为限制环境下农业跟随机器人的未来发展提供了模块化和可扩展的框架。

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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 : 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

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 : 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

Aggression recognition for individual pigs based on YOLOv8 and DCTM-UniformerV2 基于YOLOv8和DCTM-UniformerV2的猪个体攻击识别

IF 5.3 1区农林科学 Q1 AGRICULTURAL ENGINEERING

Biosystems Engineering

Pub Date : 2025-11-29 DOI: 10.1016/j.biosystemseng.2025.104345

Chen Chen , Rui Liu , Weixing Zhu , Tomas Norton

The aim of this study is to develop a deep learning algorithm based on YOLOv8 and DCTM-UniformerV2 to recognise aggressive behaviour in pigs. Compared with previous studies, this study further investigates aggression recognition from individual perspectives. In the experiment, 8 nursery pigs were mixed for 3 days and then 8 h of video was recorded each day. 410 aggressive 2 s episodes were labelled from the first day of video. By segmenting each pig in these episodes, sub-episodes of 820 aggressive and 2460 non-aggressive pigs were generated. These 820 aggressive sub-episodes were then augmented into 2460 sub-episodes by using horizontal and vertical mirroring. All the 4920 sub-episodes were divided into the training and validation sets at a ratio of 7:3. Moreover, 190 aggressive 2 s episodes were labelled from the other 2 days of videos, and then 380 aggressive and 1140 non-aggressive sub-episodes were generated as the test set. Firstly, identification module of YOLOv8 was used to locate each pig and determine their identities. Secondly, temporal shift module and depth channel attention mechanism were fused into the aggression recognition module of UniformerV2 to improve the discrimination of spatial-temporal features. Finally, the improved DCTM-UniformerV2 network was used to recognise aggressive behaviour of pigs. In the test set, the aggression could be recognised by using the proposed algorithm with an accuracy of 97.2 %. The result indicates that the proposed method can be used to recognise aggressive behaviour of individual pigs.

本研究的目的是开发一种基于YOLOv8和DCTM-UniformerV2的深度学习算法来识别猪的攻击行为。与以往的研究相比，本研究进一步从个体角度研究攻击识别。试验中，8头苗猪混合饲养3 d，每天录像8 h。从视频的第一天起，就有410个攻击性的2集被标记。通过对这些片段中的每头猪进行分割，生成了820头攻击性猪和2460头非攻击性猪的子片段。然后通过水平和垂直镜像将820个侵袭性亚集扩增为2460个亚集。将4920个子集按7:3的比例划分为训练集和验证集。另外，从另外2天的视频中标记190个攻击性2秒片段，然后生成380个攻击性子片段和1140个非攻击性子片段作为测试集。首先，利用YOLOv8的识别模块对每头猪进行定位，确定其身份。其次，将时间偏移模块和深度通道注意机制融合到UniformerV2的攻击识别模块中，提高对时空特征的识别能力；最后，使用改进的DCTM-UniformerV2网络来识别猪的攻击行为。在测试集中，该算法对攻击行为的识别准确率达到97.2%。结果表明，所提出的方法可以用于识别个体猪的攻击行为。

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引用次数: 0

A review of strategic enhancement of pollination with smart agriculture to counteract the decline of natural pollinators 以智慧农业策略加强传粉以对抗自然传粉媒介的减少

IF 5.3 1区农林科学 Q1 AGRICULTURAL ENGINEERING

Biosystems Engineering

Pub Date : 2025-11-28 DOI: 10.1016/j.biosystemseng.2025.104344

Wantong Zhang, Fa Song, Jiyu Sun

The population of animal pollinators has been steadily declining due to human activities, disrupting the balance of agricultural ecosystems. In response, UAV pollination technology has emerged as a promising option to mitigate the shortage of natural pollination services. This paper reviews the background of the development of UAV pollination technology, summarises and evaluates current UAV pollination technology and the intelligent technologies involved therein, highlighting the current state and development limitations. Finally, based on this analysis and the prevailing conditions of crop pollination and agricultural environments, the integration of intelligent technologies is proposed, particularly UAV-based systems, to improve the efficiency of pollination.

由于人类活动，动物传粉者的数量一直在稳步下降，破坏了农业生态系统的平衡。因此，无人机授粉技术已成为缓解自然授粉服务短缺的一种有希望的选择。本文回顾了无人机传粉技术的发展背景，对当前无人机传粉技术及其涉及的智能技术进行了总结和评价，突出了无人机传粉技术的现状和发展局限性。最后，在此分析的基础上，结合作物传粉的现状和农业环境，提出了智能技术的集成，特别是基于无人机的系统，以提高传粉效率。

引用次数: 0

Modelling and analysis of airflow-induced rotational behaviour of sunflower seeds for directional sowing 向日葵种子定向播种气流诱导旋转特性的建模与分析

IF 5.3 1区农林科学 Q1 AGRICULTURAL ENGINEERING

Biosystems Engineering

Pub Date : 2025-11-26 DOI: 10.1016/j.biosystemseng.2025.104342

Xuan Zhao, Anbin Zhang, Fei Liu, Hongbin Bai, Yuxing Ren, Wenxue Dong, Shuhan Yang

Directional mechanised seeding of sunflower seeds plays a critical role in enhancing crop yield and minimizing seed loss. However, limited understanding of the rotational behaviour of irregularly shaped seeds, such as sunflower seeds, under airflow conditions has constrained the precision of current seeding equipment. In this study, a mathematical model describing seed rotation under airflow was developed using parametric equations that represent the geometry of sunflower seeds. A custom-built experimental setup, together with computational fluid dynamics (CFD) simulations in ANSYS Fluent, was employed to analyse the rotational dynamics in detail. Single-factor experiments revealed that suction hole diameter, negative pressure intensity, seed centre offset, equivalent diameter, and initial posture angle all significantly affected the torque and rotational direction of the seed. An empirical model was further established through orthogonal experiments to quantitatively describe the seed's rotational response under airflow. Validation experiments with sunflower seeds demonstrated the model's high predictive accuracy within the following parameter ranges: suction hole diameter (13–33 mm), negative pressure (1–5 kPa), seed centre offset (5–13 mm), equivalent diameter (6.19–9.28 mm), and initial posture angle (0–288°). These findings provide a theoretical basis for the design of pneumatic directional seeding systems and a technical reference for the targeted seeding of other irregularly shaped seeds, contributing to the advancement of precision agriculture.

葵花籽定向机械化播种对提高作物产量和减少种子损失具有重要作用。然而，对不规则形状种子（如葵花籽）在气流条件下的旋转行为的有限理解限制了当前播种设备的精度。在这项研究中，建立了一个描述种子在气流下旋转的数学模型，使用参数方程来表示葵花籽的几何形状。采用定制的实验装置，结合ANSYS Fluent计算流体动力学（CFD）仿真，对旋转动力学进行了详细分析。单因素实验结果表明，吸力孔直径、负压强度、种子中心偏移量、等效直径和初始姿态角对种子的转矩和旋转方向有显著影响。通过正交试验，进一步建立了定量描述气流作用下种子旋转响应的经验模型。葵花籽的验证实验表明，该模型在以下参数范围内具有较高的预测精度：吸气孔直径（13-33 mm）、负压（1-5 kPa）、种子中心偏移（5-13 mm）、等效直径（6.19-9.28 mm）和初始姿态角（0-288°）。这些研究结果为气动定向播种系统的设计提供了理论依据，也为其他不规则形状种子的定向播种提供了技术参考，有助于推进精准农业。

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引用次数: 0

Airflow variations and particle conveying characteristics in pneumatic straw suction device based on CFD-DEM 基于CFD-DEM的气力吸管吸入装置气流变化及颗粒输送特性

IF 5.3 1区农林科学 Q1 AGRICULTURAL ENGINEERING

Biosystems Engineering

Pub Date : 2025-11-21 DOI: 10.1016/j.biosystemseng.2025.104341

Rongrong Li , Hongwen Li , Jin He , Yingbo Wang , Caiyun Lu , Zhengyang Wu , Shan Jiang , Zongfu Yang

Based on the principle of dilute-phase pneumatic conveying, this study proposes an innovative design for a pneumatic straw suction device for conservation tillage to reduce pressure drop and improve conveying efficiency. A coupled simulation method combining the Discrete Element Method (DEM) with Computational Fluid Dynamics (CFD) was employed, and its accuracy was validated through bench tests. The distribution and variation of airflow, straw particle motion, and the interaction of these parameters were investigated to achieve low pressure drop and high efficiency. By analysing the sources of pressure drop, it was found that the primary factors affecting airflow and particle conveying were the inner diameter of the air duct or the upper surface of the suction chamber, the bending diameter ratio of the elbow, and the fan rotational speed. The response surface optimisation revealed that the best low-loss, high-efficiency performance was achieved when the diameter of the air duct, the bending diameter ratio, and the fan rotational speed were set to 200 mm, 1.54, and 2900 rpm, respectively. Under these conditions, the pressure drop down, pressure drop up, and the percentage of straw mass were 12.58 Pa, 17.12 Pa, and 3.15 %, respectively. This study provides new insights into the interaction between straw particles and airflow in pneumatic conveying systems.

本研究基于稀相气力输送原理，提出了一种用于保护性耕作的气力吸草装置的创新设计，以减小压降，提高输送效率。采用离散元法（DEM）和计算流体力学（CFD）相结合的耦合仿真方法，通过台架试验验证了其准确性。为实现低压降和高效率，研究了气流的分布和变化、秸秆颗粒的运动以及这些参数的相互作用。通过分析压降的来源，发现影响气流和颗粒输送的主要因素是风管或吸入室上表面的内径、弯头弯曲直径比和风机转速。响应面优化结果表明，当风道直径为200 mm、弯曲直径比为1.54、风机转速为2900 rpm时，风机具有最佳的低损耗、高效率性能。在此条件下，压降为12.58 Pa，压降为17.12 Pa，秸秆质量率为3.15%。该研究为气力输送系统中秸秆颗粒与气流的相互作用提供了新的见解。

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引用次数: 0

Tillage-induced soil feature extraction and multi-sensors fusion for tillage system classification 耕作诱导土壤特征提取及多传感器融合耕作系统分类

IF 5.3 1区农林科学 Q1 AGRICULTURAL ENGINEERING

Biosystems Engineering

Pub Date : 2025-11-21 DOI: 10.1016/j.biosystemseng.2025.104329

Jia-Hao He , S.K. Mickelson , J.L. Hatfield , Mehari Z. Tekeste

Digitised soil tilth is a numerical index that quantifies soil's physical state after tillage operations using digital imaging or sensor technology. Limited research exists on extracting features from tilled soil to achieve soil tilth digitisation for Artificial Intelligence (AI)-driven smart tillage decision support for soil management and crop productivity. Visual soil images, infrared soil images, and soil temperature readings were collected on tilled soil field behind two tillage systems, mouldboard plough (MP) (sample size 432), and a disk ripper (DP) (sample size 432). Three feature extraction methods were employed to derive soil attributes. These extracted features were then applied to six machine learning models to assess their effectiveness in AI applications, aiming for AI-driven smart tillage decision system. The study also proposed implementing feature fusion, which combines features into 20-dimensional vectors from visual and infrared images, and soil temperature readings. This fusion approach creates a distinct separation between the two tillage systems in the feature space, enhancing AI applications by improving classification accuracy from 79 % to 99 %, which translates to a reliable decision-making system with 95 % reduction in misclassification errors compared to using random forest model with individual sensor features. The results indicate the initial success of the proposed fusion approach and extraction methods in AI applications, showing promise for further use in AI-driven smart tillage decision system. Besides the model-based classification method development, computation capability and selection of sensor availability were also assessed for accelerated implementation of the methodology to field digital tillage applications.

数字化土壤耕度是利用数字成像或传感器技术量化耕作操作后土壤物理状态的数值指标。人工智能（AI）驱动的智能耕作决策支持土壤管理和作物生产力，从耕作土壤中提取特征以实现土壤覆盖度数字化的研究有限。在两种耕作方式，模板犁（MP）（样本量为432）和圆盘撕裂器（DP）（样本量为432）后的耕地上采集了土壤的视觉图像、红外土壤图像和土壤温度读数。采用三种特征提取方法提取土壤属性。然后将这些提取的特征应用于六个机器学习模型，以评估其在人工智能应用中的有效性，旨在构建人工智能驱动的智能耕作决策系统。该研究还提出实施特征融合，将视觉和红外图像的特征与土壤温度读数结合成20维向量。这种融合方法在特征空间中创建了两种耕作系统之间的明显分离，通过将分类精度从79%提高到99%来增强人工智能应用，与使用具有单个传感器特征的随机森林模型相比，这转化为可靠的决策系统，其误分类错误率降低了95%。结果表明，所提出的融合方法和提取方法在人工智能应用中取得了初步成功，显示出在人工智能驱动的智能耕作决策系统中进一步应用的前景。除了基于模型的分类方法开发，还评估了计算能力和传感器可用性的选择，以加速该方法在田间数字耕作中的应用。

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