Ansgar Dreier, Gina Lopez, Rajina Bajracharya, Heiner Kuhlmann, Lasse Klingbeil
{"title":"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","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":"{\"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}","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}
Structural wheat trait estimation using UAV-based laser scanning data: Analysis of critical aspects and recommendations based on a case study
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.
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.
求助内容：
应助结果提醒方式：
京公网安备 11010802042870号
