{"title":"UAV-based canopy monitoring: calibration of a multispectral sensor for green area index and nitrogen uptake across several crops","authors":"","doi":"10.1007/s11119-024-10123-2","DOIUrl":null,"url":null,"abstract":"<h3>Abstract</h3> <p>The fast and accurate provision of within-season data of green area index (<em>GAI</em>) and total N uptake (<em>total N</em>) is the basis for crop modeling and precision agriculture. However, due to rapid advancements in multispectral sensors and the high sampling effort, there is currently no existing reference work for the calibration of one UAV (unmanned aerial vehicle)-based multispectral sensor to <em>GAI</em> and <em>total N</em> for silage maize, winter barley, winter oilseed rape, and winter wheat.</p> <p>In this paper, a practicable calibration framework is presented. On the basis of a multi-year dataset, crop-specific models are calibrated for the UAV-based estimation of <em>GAI</em> throughout the entire growing season and of <em>total N</em> until flowering. These models demonstrate high accuracies in an independent evaluation over multiple growing seasons and trial sites (mean absolute error of 0.19–0.48 m<sup>2</sup> m<sup>−2</sup> for <em>GAI</em> and of 0.80–1.21 g m<sup>−2</sup> for <em>total N</em>). The calibration of a uniform <em>GAI</em> model does not provide convincing results. Near infrared-based ratios are identified as the most important component for all calibrations. To account for the significant changes in the <em>GAI</em>/ <em>total N</em> ratio during the vegetative phase of winter barley and winter oilseed rape, their calibrations for <em>total N</em> must include a corresponding factor. The effectiveness of the calibrations is demonstrated using three years of data from an extensive field trial. High correlation of the derived <em>total N</em> uptake until flowering and the whole-season radiation uptake with yield data underline the applicability of UAV-based crop monitoring for agricultural applications.</p>","PeriodicalId":20423,"journal":{"name":"Precision Agriculture","volume":"40 1","pages":""},"PeriodicalIF":5.4000,"publicationDate":"2024-02-27","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/s11119-024-10123-2","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
The fast and accurate provision of within-season data of green area index (GAI) and total N uptake (total N) is the basis for crop modeling and precision agriculture. However, due to rapid advancements in multispectral sensors and the high sampling effort, there is currently no existing reference work for the calibration of one UAV (unmanned aerial vehicle)-based multispectral sensor to GAI and total N for silage maize, winter barley, winter oilseed rape, and winter wheat.
In this paper, a practicable calibration framework is presented. On the basis of a multi-year dataset, crop-specific models are calibrated for the UAV-based estimation of GAI throughout the entire growing season and of total N until flowering. These models demonstrate high accuracies in an independent evaluation over multiple growing seasons and trial sites (mean absolute error of 0.19–0.48 m2 m−2 for GAI and of 0.80–1.21 g m−2 for total N). The calibration of a uniform GAI model does not provide convincing results. Near infrared-based ratios are identified as the most important component for all calibrations. To account for the significant changes in the GAI/ total N ratio during the vegetative phase of winter barley and winter oilseed rape, their calibrations for total N must include a corresponding factor. The effectiveness of the calibrations is demonstrated using three years of data from an extensive field trial. High correlation of the derived total N uptake until flowering and the whole-season radiation uptake with yield data underline the applicability of UAV-based crop monitoring for agricultural applications.
摘要 快速准确地提供季内绿地指数（GAI）和总氮吸收量（总氮）数据是作物建模和精准农业的基础。然而，由于多光谱传感器的快速发展和采样工作量大，目前还没有基于无人机（UAV）的多光谱传感器对青贮玉米、冬大麦、冬油菜和冬小麦的 GAI 和总氮进行校准的参考文献。本文提出了一个切实可行的校准框架。在多年数据集的基础上，对作物特定模型进行了校准，以用于基于无人机估算整个生长季节的 GAI 和开花前的总氮。这些模型在多个生长季节和试验地点的独立评估中表现出很高的精确度（GAI 的平均绝对误差为 0.19-0.48 m2 m-2，总氮的平均绝对误差为 0.80-1.21 g m-2）。统一 GAI 模型的校准结果并不令人信服。基于近红外的比率被认为是所有校准中最重要的组成部分。为了解释冬大麦和冬油菜无性期 GAI/总氮比率的显著变化，它们的总氮校准必须包括一个相应的因子。我们利用大面积田间试验的三年数据证明了校准的有效性。得出的开花前总氮吸收量和全季辐射吸收量与产量数据高度相关，这突出表明了基于无人机的作物监测在农业应用中的适用性。
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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