低成本自动生成草地褐叶黄螨防治应用图

IF 5.4 2区 农林科学 Q1 AGRICULTURE, MULTIDISCIPLINARY
Frederick Charles Eichhorn, Sebastian Kneer, Daniel Görges
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引用次数: 0

摘要

大多数新开发的喷雾器现在都具有先进的功能,允许以厘米级的精度施用除草剂,可能减少除草剂的使用高达90%。然而,准确地确定喷雾的精确位置,即应用地图,仍然是一个重大的研究挑战。最近,商业供应商和研究机构都提出了各种基于无人机的方法来生成应用程序地图。尽管取得了这些进步,但由于监管限制和与技术相关的高成本,实际应用受到限制。增加这些技术的采用的一个有希望的方法是利用更具成本效益的硬件解决方案。在本文中,我们介绍并评估了一种新的检测方法,该方法专门用于识别臭鼻蝽(sorrel),并自动生成与大多数启用gnss的喷雾器兼容的应用程序地图。为此,我们提出了一个新的治疗成功指标,称为治疗f1得分,并使用我们提出的系统对DJI Mini 2和DJI matrix 350 RTK的性能进行了比较分析,分别获得了0.61%和0.65%的治疗f1得分。与同类应用程序中通常使用的硬件相比,该系统能够使用更便宜的硬件提供良好的性能,这表明该系统具有更广泛采用的潜力,特别是考虑到在治疗f1评分中只有4个百分点的意外适度的性能差距。在受控的实验条件下,我们观察到除草剂的使用减少了97%,没有遗漏任何目标。在现实世界草甸的实际应用中,除草剂用量减少了40%,处理精度达到85%。这些发现强调了未来技术进步的巨大潜力。独立目标检测器的平均平均精度(mAP)为67.4%,f1得分为62%,即使在其他研究人员收集的非分布无人机数据上也表现出稳健的性能。然而,目标检测算法的性能被认为是系统的一个关键瓶颈。为了促进这一领域的进一步研究和发展,我们已经提供了我们的训练数据集供下载。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Low-cost automated generation of application maps for control of Rumex Obtusifolius in grasslands

The majority of newly developed sprayers now feature advanced capabilities, allowing herbicide application with centimeter-level precision, potentially reducing herbicide use by up to 90%. However, accurately identifying the precise locations to spray, known as the application map, remains a significant research challenge. Recently, both commercial providers and research institutions have proposed various drone-based methods for generating application maps. Despite these advancements, practical adoption is limited, primarily due to regulatory constraints and the high costs associated with the technology. A promising approach to increasing the adoption of these technologies lies in the utilization of more cost-effective hardware solutions. In this paper, we introduce and evaluate a novel detection method specifically designed for identifying Rumex obtusifolius (sorrel) and for automatically generating application maps that are compatible with most GNSS-enabled sprayers. To this end, we present a new metric for treatment success, termed the treatment F1-score, and conduct a comparative analysis of the performance of the DJI Mini 2 and the DJI Matrice 350 RTK using our proposed system, achieving treatment F1-scores of 0.61% and 0.65% , respectively. The ability of this system to deliver good performance utilizing significantly less expensive hardware than typically employed in similar applications suggests a potential for broader adoption, particularly given the unexpectedly modest performance gap of only 4 percentage points in the treatment F1-score. Under controlled experimental conditions, we observed reductions in herbicide use of up to 97% without missing any targets. In practical applications within real-world meadows, a 40% reduction in herbicide consumption was achieved with a treatment accuracy of 85% . These findings underscore the substantial potential for future technological advancements. The standalone object detector achieves a mean Average Precision (mAP) of 67.4% and an F1-score of 62%, demonstrating robust performance even on out-of-distribution drone data collected by other researchers. Still, the performance of the object detection algorithm is identified as a critical bottleneck in the system. To facilitate further research and development in this domain, we have made our training dataset available for download.

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来源期刊
Precision Agriculture
Precision Agriculture 农林科学-农业综合
CiteScore
12.30
自引率
8.10%
发文量
103
审稿时长
>24 weeks
期刊介绍: Precision Agriculture promotes the most innovative results coming from the research in the field of precision agriculture. It provides an effective forum for disseminating original and fundamental research and experience in the rapidly advancing area of precision farming. There are many topics in the field of precision agriculture; therefore, the topics that are addressed include, but are not limited to: Natural Resources Variability: Soil and landscape variability, digital elevation models, soil mapping, geostatistics, geographic information systems, microclimate, weather forecasting, remote sensing, management units, scale, etc. Managing Variability: Sampling techniques, site-specific nutrient and crop protection chemical recommendation, crop quality, tillage, seed density, seed variety, yield mapping, remote sensing, record keeping systems, data interpretation and use, crops (corn, wheat, sugar beets, potatoes, peanut, cotton, vegetables, etc.), management scale, etc. Engineering Technology: Computers, positioning systems, DGPS, machinery, tillage, planting, nutrient and crop protection implements, manure, irrigation, fertigation, yield monitor and mapping, soil physical and chemical characteristic sensors, weed/pest mapping, etc. Profitability: MEY, net returns, BMPs, optimum recommendations, crop quality, technology cost, sustainability, social impacts, marketing, cooperatives, farm scale, crop type, etc. Environment: Nutrient, crop protection chemicals, sediments, leaching, runoff, practices, field, watershed, on/off farm, artificial drainage, ground water, surface water, etc. Technology Transfer: Skill needs, education, training, outreach, methods, surveys, agri-business, producers, distance education, Internet, simulations models, decision support systems, expert systems, on-farm experimentation, partnerships, quality of rural life, etc.
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