Structural wheat trait estimation using UAV-based laser scanning data: Analysis of critical aspects and recommendations based on a case study

IF 5.4 2区农林科学 Q1 AGRICULTURE, MULTIDISCIPLINARY Precision Agriculture Pub Date : 2024-12-27 DOI:10.1007/s11119-024-10202-4

Ansgar Dreier, Gina Lopez, Rajina Bajracharya, Heiner Kuhlmann, Lasse Klingbeil

{"title":"Structural wheat trait estimation using UAV-based laser scanning data: Analysis of critical aspects and recommendations based on a case study","authors":"Ansgar Dreier, Gina Lopez, Rajina Bajracharya, Heiner Kuhlmann, Lasse Klingbeil","doi":"10.1007/s11119-024-10202-4","DOIUrl":null,"url":null,"abstract":"<h3 data-test=\"abstract-sub-heading\">Purpose</h3><p>The use of UAVs (Unmanned Aerial Vehicles) equipped with sensors such as laser scanners offers an alternative to conventional, labor-intensive manual measurements in agriculture, as they enable precise and non-destructive field surveys.</p><h3 data-test=\"abstract-sub-heading\">Methods</h3><p>This paper evaluates the use of UAV-based laser scanning (RIEGL miniVUX-SYS) for estimating the crop height and the plant area index (PAI) of winter wheat. (Methods) It further introduces a novel ground classification method, enhancing early growth stage classification through sensor attributes like intensity and pulse shape deviation.</p><h3 data-test=\"abstract-sub-heading\">Results</h3><p>The crop height estimation shows a high <span>\\(R^2\\)</span> score with <span>\\(99.69~\\%\\)</span> but a systematically lower estimate with a mean absolute error of 7.4 <i>cm</i>. The potential of PAI derivation is analyzed with three different estimation strategies and provides an overview and limitations of the approach. Additional weighting based on the scan angle and the adaptation of the extinction coefficient present results with <span>\\(R^2\\)</span> of <span>\\(97.66~\\%\\)</span> and a mean absolute error of 0.25.</p><h3 data-test=\"abstract-sub-heading\">Conclusion</h3><p>The investigation discusses further the impact of the calculated gap fraction, which describes the ratio of laser beams penetrating through the crop canopy in comparison to the total number of measurements.</p>","PeriodicalId":20423,"journal":{"name":"Precision Agriculture","volume":"27 1","pages":""},"PeriodicalIF":5.4000,"publicationDate":"2024-12-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Precision Agriculture","FirstCategoryId":"97","ListUrlMain":"https://doi.org/10.1007/s11119-024-10202-4","RegionNum":2,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"AGRICULTURE, MULTIDISCIPLINARY","Score":null,"Total":0}

引用次数: 0

Abstract

Purpose

The use of UAVs (Unmanned Aerial Vehicles) equipped with sensors such as laser scanners offers an alternative to conventional, labor-intensive manual measurements in agriculture, as they enable precise and non-destructive field surveys.

Methods

This paper evaluates the use of UAV-based laser scanning (RIEGL miniVUX-SYS) for estimating the crop height and the plant area index (PAI) of winter wheat. (Methods) It further introduces a novel ground classification method, enhancing early growth stage classification through sensor attributes like intensity and pulse shape deviation.

Results

The crop height estimation shows a high \(R^2\) score with \(99.69~\%\) but a systematically lower estimate with a mean absolute error of 7.4 cm. The potential of PAI derivation is analyzed with three different estimation strategies and provides an overview and limitations of the approach. Additional weighting based on the scan angle and the adaptation of the extinction coefficient present results with \(R^2\) of \(97.66~\%\) and a mean absolute error of 0.25.

Conclusion

The investigation discusses further the impact of the calculated gap fraction, which describes the ratio of laser beams penetrating through the crop canopy in comparison to the total number of measurements.

查看原文

微信好友朋友圈 QQ好友复制链接

本刊更多论文

基于无人机激光扫描数据的结构小麦性状估计：基于案例研究的关键方面分析和建议

无人机（Unmanned Aerial Vehicles）配备了传感器，如激光扫描仪，为传统的、劳动密集型的农业人工测量提供了一种替代方案，因为它们能够实现精确和非破坏性的实地调查。方法对基于无人机的激光扫描技术（RIEGL miniVUX-SYS）在冬小麦作物高度和作物面积指数（PAI）估算中的应用进行了评价。（方法）进一步引入一种新的地面分类方法，通过强度和脉冲形状偏差等传感器属性增强生长早期的分类能力。结果作物高度估计值较高 \(R^2\) 得分 \(99.69~\%\) 但一个系统较低的估计，平均绝对误差为7.4厘米。用三种不同的估计策略分析了PAI衍生的潜力，并提供了该方法的概述和局限性。基于扫描角的附加加权和消光系数的自适应给出了结果 \(R^2\) 的 \(97.66~\%\) 平均绝对误差为0.25。结论进一步讨论了计算间隙分数的影响，该分数描述了激光穿透作物冠层的比例与总测量次数的比较。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

求助全文

约1分钟内获得全文去求助

来源期刊

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.