Computers and Electronics in Agriculture最新文献_第8页

Spatial interpolators for Delineating management zones to mitigate Mucuna pruriens in sugarcane plantations in the Eastern Amazon 用于划定管理区的空间插值器，以减少亚马逊河东部甘蔗种植园中的金丝桃危害

IF 7.7 1区农林科学 Q1 AGRICULTURE, MULTIDISCIPLINARY

Computers and Electronics in Agriculture

Pub Date : 2024-11-10 DOI: 10.1016/j.compag.2024.109615

Luiz Antonio Soares Cardoso , Paulo Roberto Silva Farias , João Almiro Corrêa Soares , Carlos Rodrigo Tanajura Caldeira , Fábio Júnior de Oliveira

Unmanned Aerial Vehicles (UAVs) have emerged as essential tools in precision agriculture, employing aerial photogrammetry concepts to aid producers in various decision-making processes. This study evauates different spatial interpolators to define management zones in sugarcane fields, aiming to control potential infestations by Mucuna pruriens. We collected images using the EbeeSQ UAV, equipped with a multispectral sensor, and calculated vegetation indices, including NDVI, SAVI, NDRE, and GNDVI. Analysis revealed that GNDVI yielded the most favorable results, with a mean value of 0.304 and a coefficient of variation of 11.747 %. Using regular and random sampling grids, we applied Ordinary Kriging (OK) and Support Vector Machine (SVM) interpolators to assess spatial variability across 13 survey zones. The results indicated a Degree of Spatial Dependence averaging 57.197 % and a Moran Index of 0.609, confirming moderate spatial dependence. Cross-validation showed that OK with random sampling outperformed other methods, achieving a Root Mean Square Error (RMSE) of 0.064 and a coefficient of determination (r²) averaging 0.347. Furthermore, the relationship between the Fuzzy Performance Index (averaging 0.069) and Normalized Classification Entropy (averaging 0.077) enabled the creation of management zone maps. These maps effectively identify distinct classes within the study areas, enhancing decision-making for producers in managing velvet bean weed during critical developmental phases.

无人驾驶飞行器（UAV）已成为精准农业的重要工具，它采用航空摄影测量概念来帮助生产者进行各种决策。本研究利用不同的空间插值法来确定甘蔗田的管理区域，目的是控制潜在的金丝楠木侵扰。我们使用配备多光谱传感器的 EbeeSQ 无人机采集图像，并计算植被指数，包括 NDVI、SAVI、NDRE 和 GNDVI。分析表明，GNDVI 的结果最理想，平均值为 0.304，变异系数为 11.747%。我们使用规则和随机抽样网格，应用普通克里金（OK）和支持向量机（SVM）插值器评估了 13 个调查区的空间变异性。结果表明，空间依赖度平均为 57.197%，莫兰指数为 0.609，证实了中等程度的空间依赖性。交叉验证结果表明，采用随机抽样的 OK 方法优于其他方法，其均方根误差（RMSE）为 0.064，判定系数（r2）平均为 0.347。此外，模糊性能指数（平均为 0.069）和归一化分类熵（平均为 0.077）之间的关系使得管理区域图得以绘制。这些地图有效地确定了研究区域内的不同等级，从而提高了生产者在关键生长阶段管理绒豆杂草的决策能力。

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引用次数: 0

StraTracker: A dynamic counting method for growing strawberries based on multi-target tracking StraTracker：基于多目标跟踪的草莓生长动态计数法

IF 7.7 1区农林科学 Q1 AGRICULTURE, MULTIDISCIPLINARY

Computers and Electronics in Agriculture

Pub Date : 2024-11-09 DOI: 10.1016/j.compag.2024.109564

Qilin An, Yongzhi Cui, Wenyu Tong, Yangchun Liu, Bo Zhao, Liguo Wei

Accurately counting fruit in orchards is a critical step for effective digital farming management. However, the variability in fruit size, overlapping shadows, and light interference present significant challenges to applying computer vision during the strawberry growth phase. To address these challenges, we propose StraTracker, a multi-object tracking (MOT) algorithm specifically designed to identify and count strawberries at various growth stages. StraTracker transforms the counting task into a frame-by-frame tracking problem, integrating both motion and appearance features. The algorithm is composed of three key components: a strawberry detector based on YOLOv8n, a feature association module, and a dual-area counting (DC) module. First, the strawberry detector accurately recognizes five growth stages, achieving an average accuracy of 91.93 % at 38.3 FPS. Next, the feature association module, incorporating the Feature Slicing Attention (FSA) and Adaptive Kalman Filtering (AKF) modules, mitigates issues such as light interference, impractical tracking frames, and ID switching (IDs). As a result, StraTracker achieves a Multi-Object Tracking Accuracy (MOTA) of 83.28 % and a Higher-Order Tracking Accuracy (HOTA) of 77.26 %, with only 259 IDs, outperforming existing baseline models. Finally, the DC module categorizes fruit counts based on the unique IDs assigned during tracking. The algorithm’s coefficient of determination (

R^{2}

= 0.91) and GEH of 2.33 indicate a strong correlation between predicted and actual counts. In conclusion, StraTracker offers a promising solution for farmers to optimize planting strategies and develop more precise harvesting plans.

对果园中的果实进行精确计数是实现有效数字农业管理的关键一步。然而，果实大小的可变性、阴影重叠和光线干扰给在草莓生长阶段应用计算机视觉带来了巨大挑战。为了应对这些挑战，我们提出了 StraTracker，这是一种多目标跟踪 (MOT) 算法，专门用于识别和计数处于不同生长阶段的草莓。StraTracker 将计数任务转化为逐帧跟踪问题，同时整合了运动和外观特征。该算法由三个关键部分组成：基于 YOLOv8n 的草莓检测器、特征关联模块和双区域计数（DC）模块。首先，草莓检测器能准确识别五个生长阶段，在 38.3 FPS 下达到 91.93% 的平均准确率。接着，特征关联模块结合了特征切分注意（FSA）和自适应卡尔曼滤波（AKF）模块，缓解了光线干扰、不切实际的跟踪帧和 ID 切换（ID）等问题。因此，StraTracker 的多目标跟踪准确率 (MOTA) 达到 83.28%，高阶跟踪准确率 (HOTA) 达到 77.26%，ID 数量仅为 259 个，优于现有的基线模型。最后，DC 模块根据跟踪过程中分配的唯一 ID 对水果数量进行分类。该算法的判定系数（R2 = 0.91）和 2.33 的 GEH 表明，预测计数与实际计数之间具有很强的相关性。总之，StraTracker 为农民优化种植策略和制定更精确的收获计划提供了一个前景广阔的解决方案。

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引用次数: 0

Convolutional Neural Networks accurately predict soil matric potential from soil, weather, and satellite-derived data 卷积神经网络从土壤、天气和卫星数据中准确预测土壤母质电位

IF 7.7 1区农林科学 Q1 AGRICULTURE, MULTIDISCIPLINARY

Computers and Electronics in Agriculture

Pub Date : 2024-11-09 DOI: 10.1016/j.compag.2024.109597

Carlos Ballester, John Hornbuckle, Brenno Tondato, Rodrigo Filev-Maia

Being able to predict soil moisture dynamics offers water managers the possibility to better plan irrigation events and prevent soil moisture deficits from reaching levels that reduce crop production. Machine learning (ML) model predictions can potentially assist farmers in managing irrigation water more efficiently. In this study, we aimed to assess the accuracy of a set of ML models in predicting soil matric potential seven days ahead in gravity-surface irrigated cotton paddocks and evaluate the models’ performance for longer term predictions (14 days). The ML models used past soil moisture, weather, and satellite-derived crop-related data as features for the input parameters. Input data were structured in tuples that were organised following a 20-day ‘window’ approach that ‘slid’ one position forward after each training round. A convolutional neural network (CNN) model outperformed a Long Short-Term Memory, Dense Multilayer Perceptron, and Linear Regression model, the latter of which produced the least accurate predictions. The accuracy of the soil matric potential predictions with the CNN model was stable over time (R² ≥ 0.92 and root mean square deviation ≤ 7.5 kPa). However, less accurate predictions were obtained for a short period after emergence and at crop senescence. This study demonstrates the feasibility of producing accurate predictions of soil matric potential in cotton fields at 0.20 m soil depth with a CNN model, which can be integrated into irrigation decision support systems.

能够预测土壤水分动态为水资源管理者更好地规划灌溉活动和防止土壤水分不足达到降低作物产量的水平提供了可能。机器学习（ML）模型预测有可能帮助农民更有效地管理灌溉用水。在这项研究中，我们旨在评估一组 ML 模型在重力地面灌溉棉田中提前 7 天预测土壤水分潜力的准确性，并评估模型在长期预测（14 天）中的性能。ML 模型使用过去的土壤水分、天气和卫星作物相关数据作为输入参数特征。输入数据以元组为结构，按照 20 天的 "窗口 "方法进行组织，每轮训练后向前 "滑动 "一个位置。卷积神经网络（CNN）模型优于长短期记忆、密集多层感知器和线性回归模型，后者的预测准确度最低。使用 CNN 模型预测土壤墒情的准确度在一段时间内保持稳定（R2 ≥ 0.92，均方根偏差小于 7.5 千帕）。不过，对出苗后短时间内和作物衰老期的预测精度较低。这项研究证明了利用 CNN 模型准确预测棉田 0.20 米土壤深度土壤母质势的可行性，该模型可集成到灌溉决策支持系统中。

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引用次数: 0

An automatic 3D tomato plant stemwork phenotyping pipeline at internode level based on tree quantitative structural modelling algorithm 基于树状定量结构建模算法的番茄植物茎节间三维自动表型流水线

IF 7.7 1区农林科学 Q1 AGRICULTURE, MULTIDISCIPLINARY

Computers and Electronics in Agriculture

Pub Date : 2024-11-09 DOI: 10.1016/j.compag.2024.109607

Bolai Xin , Katarína Smoleňová , Harm Bartholomeus , Gert Kootstra

Phenotypic traits of stemwork are important indicators of plant growing status, contributing to multiple research domains including yield estimation, breeding engineering, and disease control. Traditional plant phenotyping with human work faces serious bottlenecks on labour intensity and time consumption. In recent years, the application of Quantitative Structural Modeling (QSM) together with three-dimensional (3D) sensor-based data acquisition techniques provides a feasible solution towards the automatic stemwork phenotyping. Nevertheless, existing QSM-based pipelines are sensitive towards the point cloud quality, and mostly focus on the phenotyping at plant or organ level. Information at internode level which are closely related to photosynthesis and light absorption was generally overlooked. To this end, a 3D automatic stemwork phenotyping pipeline is developed for tomato plants at both plant and internode level. Coloured point clouds are taken as the sensor input of the pipeline. A semantic segmentation based on PointNet++ was used to detect and localise the stemwork points. To improve the quality of the segmented stemwork point clouds, a density-based refining pipeline is proposed containing three main processes: non-replacement resampling, interference branch removal, and noise removal. A Tree Quantitative Structural Modeling (TreeQSM) algorithm was then applied to the stemwork point cloud to construct a digital reconstruction. The target phenotypic traits were finally calculated from the digital model by employing an internode association process. The proposed phenotyping pipeline was evaluated with a test dataset containing three tomato plant cultivars: Merlice, Brioso, and Gardener Delight. The related rooted mean squared errors of calculated internode length, internode diameters, leaf branching angle, leaf phyllotactic angle, and stem length range from 4.8 to 64.4%. Considering the time consuming manual phenotyping process, the proposed work provides a feasible solution towards the high throughput plant phenotyping, from which facilitates the related research on plant breeding and crop management.

茎干的表型性状是植物生长状况的重要指标，有助于产量评估、育种工程和疾病控制等多个研究领域。传统的人工植物表型面临着劳动强度和时间消耗的严重瓶颈。近年来，定量结构建模（QSM）与基于三维（3D）传感器的数据采集技术的结合应用，为自动茎干表型提供了可行的解决方案。不过，现有的基于 QSM 的管道对点云质量很敏感，而且大多侧重于植物或器官层面的表型。与光合作用和光吸收密切相关的节间信息通常被忽视。为此，我们为番茄植株开发了植株和节间水平的三维自动茎干表型管道。彩色点云作为管道的传感器输入。使用基于 PointNet++ 的语义分割来检测和定位茎干点。为了提高分割后茎干点云的质量，提出了一个基于密度的精炼管道，其中包含三个主要过程：非置换重采样、干扰枝去除和噪声去除。然后将树状定量结构建模（TreeQSM）算法应用于茎干点云，构建数字重建。通过采用节间关联过程，最终根据数字模型计算出目标表型性状。利用包含三个番茄栽培品种的测试数据集对所提出的表型鉴定管道进行了评估：Merlice、Brioso 和 Gardener Delight。计算出的节间长度、节间直径、叶片分枝角、叶片植动角和茎长的相关根均方误差在 4.8% 到 64.4% 之间。考虑到人工表型过程耗时较长，所提出的工作为高通量植物表型提供了一个可行的解决方案，从而促进了植物育种和作物管理的相关研究。

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引用次数: 0

Hedge three-dimensional reconstruction and motion control technology for trimming robot 用于修剪机器人的对冲三维重建和运动控制技术

IF 7.7 1区农林科学 Q1 AGRICULTURE, MULTIDISCIPLINARY

Computers and Electronics in Agriculture

Pub Date : 2024-11-09 DOI: 10.1016/j.compag.2024.109632

Jin Gu , Bin Zhang , Yu Wang , Yawei Zhang

Landscaping is an important way to realize carbon neutralization. The prospect of automatic trimming technology in the horticulture industry has received much attention in recent years. Compared with manual trimming, robots still have a large gap in trimming efficiency and functional integrity. The purpose of this study is to accurately obtain the shape parameters of a hedge by reconstructing its three-dimensional model, enabling the robot to have the complete ability to automate trimming, and improving the efficiency of trimming robot. Firstly, a trimming robot prototype system was constructed by using three-dimensional vision detection technology and autonomous motion control technology. Then, we studied the adaptive template matching method which was used for hedge detection, and the three-dimensional reconstruction method based on curvature feature similarity was used to obtain the position and shape parameters of hedge. We propose an adaptive Ant Colony Optimization trajectory planning method combined with point cloud classification strategy that can improve the efficiency of trimming robot. The results of tests show that the mean absolute value of measurement error of the hand-eye system is 3.7 mm, the mean value of the positioning error of the visual recognition is 2.1 mm, and the mean value of the positioning error of the trimming robot system is 3.8 mm. The trimming robot realized the automatic trimming operation of spherical hedge model and actual hedge in laboratory. During the actual trimming test, it demonstrated an average error of 8.2 mm, and its efficiency and reliability in trimming surpassed manual trimming methods. The research suggests that with the continuous improvement of robot technology, the use of trimming robot system in the horticulture industry will gradually become a reality.

园林绿化是实现碳中和的重要途径。近年来，自动修剪技术在园艺行业的应用前景备受关注。与人工修剪相比，机器人在修剪效率和功能完整性方面仍有较大差距。本研究的目的是通过重建绿篱的三维模型，准确获取绿篱的形状参数，使机器人具备完整的自动修剪能力，提高修剪机器人的效率。首先，利用三维视觉检测技术和自主运动控制技术构建了修剪机器人原型系统。然后，研究了用于绿篱检测的自适应模板匹配方法，并利用基于曲率特征相似性的三维重建方法获取绿篱的位置和形状参数。我们提出了一种结合点云分类策略的自适应蚁群优化轨迹规划方法，可以提高修剪机器人的效率。测试结果表明，手眼系统测量误差绝对值均值为 3.7 mm，视觉识别定位误差均值为 2.1 mm，修剪机器人系统定位误差均值为 3.8 mm。修剪机器人在实验室实现了球形绿篱模型和实际绿篱的自动修剪操作。在实际修剪试验中，其平均误差为 8.2 毫米，其修剪效率和可靠性超过了人工修剪方法。研究表明，随着机器人技术的不断进步，修剪机器人系统在园艺行业的应用将逐步成为现实。

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引用次数: 0

Autonomous inspection robot for dead laying hens in caged layer house 用于检测笼养蛋鸡舍中死亡蛋鸡的自主检测机器人

IF 7.7 1区农林科学 Q1 AGRICULTURE, MULTIDISCIPLINARY

Computers and Electronics in Agriculture

Pub Date : 2024-11-09 DOI: 10.1016/j.compag.2024.109595

Weihong Ma , Xingmeng Wang , Simon X. Yang , Xianglong Xue , Mingyu Li , Rong Wang , Ligen Yu , Lepeng Song , Qifeng Li

Daily inspections of individual laying hens in large-scale egg farms are both labor-intensive and time-consuming, requiring farm staff to manually check each caged hen and promptly remove any deceased birds to prevent the spread of disease within the battery cages. To streamline this process, a specialized robot has been developed to enhance inspection efficiency, reduce manual labor, and enable rapid identification of dead hens. This inspection robot integrates cutting-edge technologies such as deep learning for real-time detection and identification, QR code-based positioning for precise localization, and autonomous navigation for seamless movement through the farm. It automates the otherwise tedious inspection process by visualizing and pinpointing the location of dead hens within the cages. In experimental tests, the robot achieved a detection accuracy of 90.61 % by incorporating a supplementary lighting system, setting an inspection speed of 9 m per minute, and fine-tuning the inspection algorithm with a probability value parameter of 0.48 and an area ratio parameter of 0.05. Additionally, the robot demonstrated a low false detection rate of 0.14 % and a minimal obvious false detection rate of 0.06 %. Compared to traditional manual inspection methods, this robotic system not only automates the task but also significantly reduces labor requirements and improves the overall management efficiency of large-scale egg farms. With its high accuracy and speed, the robot presents a viable solution for modern poultry operations, ensuring timely removal of dead hens and contributing to better farm hygiene and animal welfare.

在大规模蛋鸡养殖场中，对每只蛋鸡的日常检查既耗费人力又耗费时间，需要养殖场工作人员手动检查每只笼养母鸡，并及时清除任何死亡鸡只，以防止疾病在电池笼内传播。为了简化这一流程，我们开发了一种专用机器人，以提高检查效率，减少人工劳动，并能快速识别死亡母鸡。这种检查机器人集成了多项尖端技术，如用于实时检测和识别的深度学习技术、用于精确定位的基于二维码的定位技术，以及用于在农场内无缝移动的自主导航技术。它通过可视化和精确定位笼内死亡母鸡的位置，将原本繁琐的检查过程自动化。在实验测试中，该机器人通过安装辅助照明系统、设定每分钟 9 米的检测速度以及微调检测算法（概率值参数为 0.48，面积比参数为 0.05），实现了 90.61 % 的检测准确率。此外，机器人的误检率较低，仅为 0.14%，明显误检率最低，仅为 0.06%。与传统的人工检测方法相比，该机器人系统不仅实现了任务自动化，还大大减少了劳动力需求，提高了大规模蛋鸡养殖场的整体管理效率。凭借其高精度和高速度，该机器人为现代家禽饲养提供了一个可行的解决方案，确保及时清除死鸡，为改善农场卫生和动物福利做出贡献。

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引用次数: 0

A survey on evaluation of blockchain-based agricultural traceability 基于区块链的农产品溯源评估调查

IF 7.7 1区农林科学 Q1 AGRICULTURE, MULTIDISCIPLINARY

Computers and Electronics in Agriculture

Pub Date : 2024-11-09 DOI: 10.1016/j.compag.2024.109548

Shaoning Pang , Shyh Wei Teng , Manzur Murshed , Cuong Van Bui , Priyabrata Karmakar , Yanyu Li , Hao Lin

The integration of blockchain technology in agricultural traceability has shown immense potential, yet its widespread adoption faces significant roadblocks. Using bulk product traceability as a foundational reference, this paper presents a comprehensive evaluation framework for Blockchain-based Agricultural Traceability. The framework accentuates product identification and data traceability across the supply chain, addressing traceability disconnections caused by bulk product blending. It dives into depth levels from adoption decision-making to system design, development, and deployment, emphasizing the critical aspects of traceability compliance and standardization. As a result, we identified the obstacles to adopting agricultural digital traceability and pave the pathway to traceability system deployment. We examined the barriers to implementing digital traceability of agricultural products, taking the Australian grain supply chain as an example. Our findings reveal that lack of standardization and participation barriers are the primary challenges in implementing digital traceability for agricultural products. Our paper offers insights and recommendations for researchers, industry practitioners, and business owners to overcome these challenges and enable digital traceability of agricultural products in global supply chains.

区块链技术与农业溯源的结合已显示出巨大的潜力，但其广泛应用还面临着巨大的障碍。本文以大宗产品可追溯性为基础参考，提出了基于区块链的农产品可追溯性综合评估框架。该框架强调整个供应链的产品识别和数据可追溯性，解决了散装产品混合造成的可追溯性脱节问题。它从采用决策到系统设计、开发和部署的各个层面深入探讨，强调了可追溯性合规性和标准化的关键环节。因此，我们找出了采用农业数字可追溯性的障碍，并为部署可追溯性系统铺平了道路。我们以澳大利亚谷物供应链为例，研究了实施农产品数字可追溯性的障碍。我们的研究结果表明，缺乏标准化和参与障碍是实施农产品数字溯源的主要挑战。我们的论文为研究人员、行业从业人员和企业主提供了见解和建议，以克服这些挑战，在全球供应链中实现农产品的数字化可追溯性。

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引用次数: 0

A high-precision automatic diagnosis method of maize developmental stage based on ensemble deep learning with IoT devices 基于物联网设备的集合深度学习的玉米生长发育阶段高精度自动诊断方法

IF 7.7 1区农林科学 Q1 AGRICULTURE, MULTIDISCIPLINARY

Computers and Electronics in Agriculture

Pub Date : 2024-11-09 DOI: 10.1016/j.compag.2024.109608

Linxiao Miao , Peng Wang , Haifeng Cao , Zhenqing Zhao , Zhenbang Hu , Qingshan Chen , Dawei Xin , Rongsheng Zhu

Accurately determining the stage of crop development holds significant importance for field crop management. With the advancement of smart agriculture, an increasing number of Internet of Things (IoT) devices are being integrated into agricultural production, enabling more efficient acquisition of high-precision crop images. Currently, research on detecting crop growth stages based on IoT device images remains relatively scarce. Most existing studies rely on a single network model for detection, often encountering issues such as low accuracy and overfitting. Therefore, in this study, we collected maize images using IoT devices and constructed an integrated deep learning model by utilizing four convolutional neural networks (CNNs) to detect the growth period of maize in real time. Additionally, we implemented several improvements on these four CNNs and subsequently tested the performance of the ensemble model on the maize dataset. Regarding the ensemble strategy for the ensemble model, we proposed a dynamic weighted voting method, building upon the original voting approach, which can mitigate model training fluctuations and expedite model convergence. Ultimately, we manually simulated various lighting conditions to assess their impact on the ensemble model. Experimental results demonstrate that the ensemble deep model proposed in this paper represents a robust method for detecting maize growth stages, achieving an accuracy rate of 0.976 on the maize dataset, effectively facilitating high-precision detection of maize growth stages in complex backgrounds.

准确确定作物的生长发育阶段对田间作物管理具有重要意义。随着智慧农业的发展，越来越多的物联网（IoT）设备被集成到农业生产中，从而能够更高效地获取高精度作物图像。目前，基于物联网设备图像检测作物生长阶段的研究仍然相对较少。现有研究大多依赖单一网络模型进行检测，往往会遇到准确率低和过拟合等问题。因此，在本研究中，我们利用物联网设备收集玉米图像，并利用四个卷积神经网络（CNN）构建了一个集成的深度学习模型，以实时检测玉米的生长期。此外，我们还对这四个卷积神经网络进行了若干改进，并随后在玉米数据集上测试了集合模型的性能。关于集合模型的集合策略，我们在原有投票方法的基础上提出了一种动态加权投票方法，这种方法可以缓解模型训练波动，加快模型收敛。最后，我们手动模拟了各种照明条件，以评估它们对集合模型的影响。实验结果表明，本文提出的集合深度模型是一种稳健的玉米生长阶段检测方法，在玉米数据集上的准确率达到了 0.976，有效促进了复杂背景下玉米生长阶段的高精度检测。

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引用次数: 0

Research progress of multiple agricultural machines for cooperative operations: A review 多农机协同作业的研究进展：综述

IF 7.7 1区农林科学 Q1 AGRICULTURE, MULTIDISCIPLINARY

Computers and Electronics in Agriculture

Pub Date : 2024-11-09 DOI: 10.1016/j.compag.2024.109628

Wenbo Wei , Maohua Xiao , Hui Wang , Yejun Zhu , Chenshuo Xie , Guosheng Geng

Multiple agricultural machines for cooperative operations are a technology that utilizes multiple agricultural machines to work together to complete specific agricultural tasks. This technology can improve productivity and reduce labor intensity, which can help alleviate the impact of population aging on agriculture. However, there remains a challenge in the practical application of this technology in terms of adaptability, operational accuracy, and operational efficiency. This review aims to systematically overview the status of multiple agricultural machines for cooperative operations through multiple machines planning technology, multiple machines communication technology, and multiple machines cooperative control. Taking the harvester-grain truck owner from the cooperative harvesting scene and the homogeneous multi-machine cooperation scene of the same kind of operation as an example, this work summarizes the typical operation scenarios and application areas of multi-machine cooperation in agriculture. Additionally, this review discusses the challenges and future directions of multiple agricultural machines for cooperative operations.

多农机合作作业是一种利用多台农机共同完成特定农业任务的技术。这项技术可以提高生产效率，降低劳动强度，有助于缓解人口老龄化对农业的影响。然而，在实际应用中，这项技术在适应性、操作准确性和操作效率方面仍然存在挑战。本综述旨在通过多机规划技术、多机通信技术和多机协同控制，系统概述多台农机协同作业的现状。以合作收割场景中的收割机-运粮车车主和同类作业中的同质多机合作场景为例，总结农业中多机合作的典型作业场景和应用领域。此外，本综述还讨论了多农业机械协同作业面临的挑战和未来发展方向。

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引用次数: 0

Path planning for mobile robots in greenhouse orchards based on improved A* and fuzzy DWA algorithms 基于改进的 A* 算法和模糊 DWA 算法的温室果园移动机器人路径规划

IF 7.7 1区农林科学 Q1 AGRICULTURE, MULTIDISCIPLINARY

Computers and Electronics in Agriculture

Pub Date : 2024-11-09 DOI: 10.1016/j.compag.2024.109598

Yuchao Wang , Chunhai Fu , Ruiyu Huang , Kelin Tong , Yong He , Lijia Xu

In complex greenhouse orchard environments, reasonable path planning algorithms are crucial for ensuring efficient and high-quality operation of mobile robots. The unstructured layouts of greenhouse orchard environments, which feature many irregular obstacles, pose high demands on navigation accuracy. Ideal path planning algorithms need to plan a safe and efficient navigation path in complex environments. In this paper, we propose a path planning fusion algorithm which integrates improved A* algorithm and Fuzzy Dynamic Window Approach (FDWA) algorithm. Firstly, the A* algorithm that introduces the rate of environmental obstacles is designed for generating global paths in greenhouses. The search strategy can be changed according to the number of environmental obstacles. Then, a rule to optimize the search neighborhood is proposed to adjust the search neighborhood to five-neighborhood, which improves the node search efficiency. Further, a local path planning strategy incorporating fuzzy control is proposed to enable the robot to maintain a safe distance from obstacles and improve the stability of obstacle avoidance. Finally, the effectiveness of proposed algorithm is verified via the simulated environment and actual greenhouse, respectively. The simulation results show that, the improved A* algorithm reduces the critical turning points and total steering angle by a maximum of 40%. The actual greenhouse experimental results show that, in three different paths, the proposed fusion algorithm reduces the distance deviation by a maximum of 31.8% and the heading angle deviation by a maximum of 28.6%, while increasing the safety distance by up to 30%.

在复杂的温室果园环境中，合理的路径规划算法是确保移动机器人高效、高质量运行的关键。温室果园环境布局不规则，存在许多不规则障碍物，这对导航精度提出了很高的要求。理想的路径规划算法需要在复杂环境中规划出安全高效的导航路径。本文提出了一种路径规划融合算法，将改进的 A* 算法和模糊动态窗口法（FDWA）算法融为一体。首先，设计了引入环境障碍率的 A* 算法，用于生成温室中的全局路径。搜索策略可根据环境障碍物的数量而改变。然后，提出了优化搜索邻域的规则，将搜索邻域调整为五邻域，提高了节点搜索效率。此外，还提出了一种结合模糊控制的局部路径规划策略，使机器人能与障碍物保持安全距离，提高避障的稳定性。最后，分别通过模拟环境和实际温室验证了所提算法的有效性。仿真结果表明，改进后的 A* 算法最大可减少 40% 的临界转弯点和总转向角。实际温室实验结果表明，在三条不同的路径上，所提出的融合算法最大减少了 31.8% 的距离偏差和 28.6% 的航向角偏差，同时增加了高达 30% 的安全距离。

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