LAND.TECHNIK AgEng 2019最新文献

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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
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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
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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
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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
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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
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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
{"title":"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","authors":"T. Ruigrok, L. Voorhoeve, G. Kootstra, E. J. van Henten","doi":"10.51202/9783181023617-383","DOIUrl":"https://doi.org/10.51202/9783181023617-383","url":null,"abstract":"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","PeriodicalId":106789,"journal":{"name":"LAND.TECHNIK AgEng 2019","volume":"50 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128247907","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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
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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
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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
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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
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引用次数: 0
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