LAND.TECHNIK AgEng 2019最新文献

英文中文

Security Concept for ISOconnect – a Secure ISOBUS Telemetry Device 安全概念的ISOconnect -一个安全的ISOBUS遥测设备

LAND.TECHNIK AgEng 2019

Pub Date : 2019-11-07 DOI: 10.51202/9783181023617-561

M. Rothmund, R. Marx

引用次数: 0

Multi-domain simulation of agricultural system solutions 农业系统多域仿真解决方案

LAND.TECHNIK AgEng 2019

Pub Date : 2019-11-07 DOI: 10.51202/9783181023617-281

M. Piechnick

引用次数: 1

Modular Multi-Purpose Battery Storage System for complex compartments – New module design approach for maximzing benefits of work machines 用于复杂隔间的模块化多用途电池存储系统-新的模块设计方法，可最大限度地提高工作机器的效益

LAND.TECHNIK AgEng 2019

Pub Date : 1900-01-01 DOI: 10.51202/9783181023617-391

M. Hammer, M. Steffan

引用次数: 0

TIM empowers cross vendor couples to higher performance TIM使跨供应商耦合能够获得更高的性能

LAND.TECHNIK AgEng 2019

Pub Date : 1900-01-01 DOI: 10.51202/9783181023617-573

A. Volbracht, J.-H. Wolker, J. Jeninga

引用次数: 0

Swarm Unit – Development of a Fully Electric Agricultural Machine with External Power Supply 群单元——外接电源全电动农业机械的研制

LAND.TECHNIK AgEng 2019

Pub Date : 1900-01-01 DOI: 10.51202/9783181023617-401

S. Pfaffmann, F. de Moraes Boos, N. Tarasinski, V. Kegel

引用次数: 7

A robust plant localization and identification system for precision farming – The effects of uncontrolled illumination on an end-to-end deep-learning plant detection algorithm 一种用于精准农业的鲁棒植物定位和识别系统——不受控光照对端到端深度学习植物检测算法的影响

LAND.TECHNIK AgEng 2019

Pub Date : 1900-01-01 DOI: 10.51202/9783181023617-383

T. Ruigrok, L. Voorhoeve, G. Kootstra, E. J. van Henten

In this research, we investigated the robustness of an end-to-end deep-learning plant detection algorithm with respect to influences from uncontrolled illumination. For this research, we acquired two datasets, one containing images of plants taken under controlled illumination and one dataset with images acquired under uncontrolled illumination. We trained and evaluated the YOLOv3 object detector on both datasets. The object detector scored a mean Average Precision of 0.96 on controlled illumination conditions and 0.90 on the uncontrolled illumination conditions. This difference in performance is significant. Introduction The development of environmental awareness has led to an increased demand for efficient use of nutrients, agrochemicals, and other resources in agriculture. Nutrients and agrochemicals are most efficiently used when applied plant specifically. For plant-specific precision farming, a vision-based plant localization and identification system(hereafter plantdetection system) is a key enabling technology. In the last decade, extensive research on the development of plantdetection systems is performed. However, the developed plant-detection systems cannot deal with the variation preFig. 1: Prototype of a volunteer potato spraying robot. The camera position is indicated by the red circle. VDI-Berichte Nr. 2361, 2019 383 https://doi.org/10.51202/9783181023617-383 Generiert durch IP '54.70.40.11', am 22.05.2021, 18:38:26. Das Erstellen und Weitergeben von Kopien dieses PDFs ist nicht zulässig. sent in the agricultural environment. These systems lack robustness to uncontrolled natural illumination [1], different soil types [2][3], variation in growth stages [4] and occluding plant parts [5]. In this study, we used recently developed deep-learning computer-vision algorithms. These algorithms have demonstrated their robustness and generalization properties in a wide range of tasks (e.g. obstacle detection and scene understanding for autonomous vehicles). Despite the promising results achieved in other domains, no deep-learning algorithm is reported with a plant-detection rate above 95% [6][7][8][9]. Furthermore, most detection systems require a covered setup with artificial lighting, imposing constraints on the applicability and capacity. Within this paper, we investigated the difference in plant detection performance of the YOLOv3 object detector on images acquired in controlled illumination conditions versus images acquired under uncontrolled illuminated conditions. As use case, a small scale volunteer potato spraying robot, as shown in Fig. 1 was used. Materials and methods Dataset description On the 28th of May and the 1st of June in 2018, data was acquired at the Wageningen University farm in Valthermond in The Netherlands, shown in Fig. 2. During the data campaign, two datasets were acquired: The controlled light, and the uncontrolled light dataset. The controlled light dataset contains images of sugar beets and volunteer potatoes Fi

在这项研究中，我们研究了端到端深度学习植物检测算法在不受控光照影响下的鲁棒性。在本研究中，我们获得了两个数据集，一个包含受控照明下的植物图像，另一个包含非受控照明下的植物图像。我们在两个数据集上训练和评估了YOLOv3目标检测器。目标检测器在受控光照条件下的平均精度为0.96，在非受控光照条件下的平均精度为0.90。这种性能上的差异是显著的。环境意识的发展导致了对农业中有效利用营养物质、农用化学品和其他资源的需求增加。营养物和农用化学品在专门施用于植物时最有效。在植物精准农业中，基于视觉的植物定位识别系统(以下简称植物检测系统)是一项关键的使能技术。在过去的十年中，对植物检测系统的发展进行了广泛的研究。然而，现有的植物检测系统无法处理预图的变化。1:马铃薯志愿喷洒机器人的原型。摄像机位置用红色圆圈表示。VDI-Berichte编号2361,2019 383 https://doi.org/10.51202/9783181023617-383 Generiert durch IP '54.70.40.11'， am 22.05.2021, 18:38:26。Das Erstellen和Weitergeben von Kopien dieses pdf第一晚zulässig。在农业环境中发送。这些系统对不受控制的自然光照[1]、不同土壤类型[2][3]、生长阶段变化[4]和植物部位遮挡[5]缺乏鲁棒性。在这项研究中，我们使用了最近开发的深度学习计算机视觉算法。这些算法已经在广泛的任务中证明了它们的鲁棒性和泛化特性(例如，自动驾驶汽车的障碍物检测和场景理解)。尽管在其他领域取得了令人鼓舞的成果，但目前还没有深度学习算法的植物检测率超过95%[6][7][8][9]。此外，大多数检测系统需要有人工照明的遮盖装置，这对适用性和容量造成了限制。在本文中，我们研究了YOLOv3目标探测器在受控照明条件下获得的图像与在非受控照明条件下获得的图像的植物检测性能的差异。用例为小型志愿马铃薯喷洒机器人，如图1所示。2018年5月28日和6月1日，在荷兰Valthermond的Wageningen University农场采集数据，如图2所示。在数据运动期间，获取了两个数据集:受控光数据集和非受控光数据集。受控光数据集包含甜菜和志愿者马铃薯的图像。图2:用于数据集的田地位置。VDI-Berichte编号2361,2019 384 https://doi.org/10.51202/9783181023617-383 Generiert durch IP '54.70.40.11'， am 22.05.2021, 18:38:26。Das Erstellen和Weitergeben von Kopien dieses pdf第一晚zulässig。在受控照明下获得的。为了减轻环境阳光的影响，使用了一个拖拉机安装的盖子。在这个覆盖物下，均匀的照明由LED天花板保证，如图3所示。非受控光数据集包含使用图1所示的原型机器人在非受控自然光下获取的图像。数据采集时间为5月28日和6月1日10:00-11:00和15:00-16:00，阳光直射，无云。两个数据集都是用分辨率为2076 x 2076像素的IDS uEye CP相机采集的。相机在最大光圈配备了一个Kowa LM5JC10M镜头，用于最大光捕获。此外，相机的软件是conFig。D采用自动白平衡和自动增益。在这两个数据集中，对526幅图像进行了标注，用于甜菜和志愿者马铃薯植物的边界盒检测。两个数据集的图像样本如图4所示。图4:左图来自受控光数据集。右图来自图3:在左图中，显示了用于阻挡环境光的设置。在设置下，均匀的照明由LED天花板保证，如图所示。VDI-Berichte编号2361,2019 385 https://doi.org/10.51202/9783181023617-383 Generiert durch IP '54.70.40.11'， am 22.05.2021, 18:38:26。Das Erstellen和Weitergeben von Kopien dieses pdf第一晚zulässig。不受控光数据集。请注意，来自受控光数据集的图像是均匀照明的，没有阴影。相反，来自非受控光数据集的图像包含硬阴影和明亮的阳光。考虑到自动志愿者土豆清除机器人的使用案例，需要一种可以在移动pc上实时运行的轻量级算法。因此，使用由[11]开发并由[12]实现的YOLOv3目标检测器。该目标检测器是最快的目标检测器之一，几乎与最先进的目标检测器(如Faster RCNN和Retinanet)一样准确[11]。YOLOv3算法是在重新缩放到416x416像素分辨率的图像上进行训练的。使用[13]提供的预训练权值对模型进行初始化，并使用默认设置进行500次epoch的训练(见附录a)。使用中给出的参数对训练图像进行图像几何形状和图像颜色的数据增强

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引用次数: 0

Development and utilization of a new application system for precise fertilizer placement in corn 玉米精准施肥新系统的开发与利用

LAND.TECHNIK AgEng 2019

Pub Date : 1900-01-01 DOI: 10.51202/9783181023617-513

M. Bouten, T. Meinel, W. Kath-Petersen

引用次数: 0

Implementation of a Dynamic Product Development Method for Agricultural Engineering Based on Virtual Prototypes 基于虚拟样机的农业工程动态产品开发方法的实现

LAND.TECHNIK AgEng 2019

Pub Date : 1900-01-01 DOI: 10.51202/9783181023617-273

P. Sivasothy, G. Bulun, J. Seewig, J. Imwalle

引用次数: 0

Reducing draft force on mounted implements by an electrified traction roller – Traction assistance on a slurry-injecting grubber 通过电气化牵引辊减少装置上的牵引力。在喷浆器上的牵引辅助

LAND.TECHNIK AgEng 2019

Pub Date : 1900-01-01 DOI: 10.51202/9783181023617-495

J. Wiecha, T. Bögel, T. Herlitzius, H. Bernhardt

引用次数: 1

New Cleaning system design for high capacity combine harvesters 为大容量联合收割机设计的新型清洗系统

LAND.TECHNIK AgEng 2019

Pub Date : 1900-01-01 DOI: 10.51202/9783181023617-537

M. Bilde, J. Andersen, A. Morrison

引用次数: 0

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