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Application of UAS-Based Remote Sens...

Hassani, Kianoosh.

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Application of UAS-Based Remote Sensing in Estimating Crop Phenotypic Traits.

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Application of UAS-Based Remote Sensing in Estimating Crop Phenotypic Traits./
作者:	Hassani, Kianoosh.
出版者:	Ann Arbor : ProQuest Dissertations & Theses, : 2023,
面頁冊數:	102 p.
附註:	Source: Dissertations Abstracts International, Volume: 85-07, Section: B.
Contained By:	Dissertations Abstracts International85-07B.
標題:	Remote sensing. -
電子資源:	https://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=30568885
ISBN:	9798381302714

Application of UAS-Based Remote Sensing in Estimating Crop Phenotypic Traits.
Hassani, Kianoosh.

Application of UAS-Based Remote Sensing in Estimating Crop Phenotypic Traits. - Ann Arbor : ProQuest Dissertations & Theses, 2023 - 102 p.

Source: Dissertations Abstracts International, Volume: 85-07, Section: B.

Thesis (Ph.D.)--Oklahoma State University, 2023.

This item must not be sold to any third party vendors.

Traditional field-based methods for monitoring crop phenotypic traits-defined as structural and biochemical characteristics and their variability during plant life cycle-are costly and limited in their ability to cover large spatial extents. Therefore, I aimed to test the application of unoccupied aerial systems (UAS) in monitoring crop traits. I designed two experiments to achieve two objectives: (1) assess the ability of UAS-derived metrics in estimating nitrogen (N) content, yield, and several structural traits, including leaf area index (LAI), plant height, fresh and dry biomass, and yield over the growing season and (2) determine the impact of spatial and spectral resolution on retrieval accuracy of N content. While both experiments took place in Stillwater, Oklahoma, U.S., experiment 1 was conducted on winter wheat while experiment 2 was conducted on corn and sorghum. To achieve the objectives, I used an UAS system to collect multispectral imagery, a spectroradiometer to collect non-imaging hyperspectral data, and conducted field campaigns to harvest foliage samples for trait quantification. In terms of methodology, I used a suite of vegetation indices (VIs) and machine learning algorithms (MLAs) to estimate crop traits. My findings provided evidence that relatively simple VIs, as opposed to complex MLAs, can be reliably used for estimating crop traits and yield. This is an important finding in the context of crop monitoring as developing MLAs requires multiple extensive in-situ data collection campaigns. Further, results showed that UAS data with coarse spectral resolution had similar performance at estimating crop traits when compared to our spectroradiometer data with fine spectral resolution. In addition, UAS data with pixel sizes ranging from 1 cm to 1 m had similar performance in terms of correlation coefficient (r) and root mean square (RMSE) at estimating N content, suggesting that data with spatial resolution of approximately 1 m might be sufficient for monitoring crop traits. Overall, these findings can potentially aid breeders through providing rapid and non-destructive proxies of crop phenotypic traits. Moreover, my findings indicate the possibility of developing cheaper spectral monitoring instruments without sacrificing prediction accuracy of crop traits.

ISBN: 9798381302714Subjects--Topical Terms:

535394
Remote sensing.
Subjects--Index Terms:

Machine learning algorithm

Application of UAS-Based Remote Sensing in Estimating Crop Phenotypic Traits.
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520 $a Traditional field-based methods for monitoring crop phenotypic traits-defined as structural and biochemical characteristics and their variability during plant life cycle-are costly and limited in their ability to cover large spatial extents. Therefore, I aimed to test the application of unoccupied aerial systems (UAS) in monitoring crop traits. I designed two experiments to achieve two objectives: (1) assess the ability of UAS-derived metrics in estimating nitrogen (N) content, yield, and several structural traits, including leaf area index (LAI), plant height, fresh and dry biomass, and yield over the growing season and (2) determine the impact of spatial and spectral resolution on retrieval accuracy of N content. While both experiments took place in Stillwater, Oklahoma, U.S., experiment 1 was conducted on winter wheat while experiment 2 was conducted on corn and sorghum. To achieve the objectives, I used an UAS system to collect multispectral imagery, a spectroradiometer to collect non-imaging hyperspectral data, and conducted field campaigns to harvest foliage samples for trait quantification. In terms of methodology, I used a suite of vegetation indices (VIs) and machine learning algorithms (MLAs) to estimate crop traits. My findings provided evidence that relatively simple VIs, as opposed to complex MLAs, can be reliably used for estimating crop traits and yield. This is an important finding in the context of crop monitoring as developing MLAs requires multiple extensive in-situ data collection campaigns. Further, results showed that UAS data with coarse spectral resolution had similar performance at estimating crop traits when compared to our spectroradiometer data with fine spectral resolution. In addition, UAS data with pixel sizes ranging from 1 cm to 1 m had similar performance in terms of correlation coefficient (r) and root mean square (RMSE) at estimating N content, suggesting that data with spatial resolution of approximately 1 m might be sufficient for monitoring crop traits. Overall, these findings can potentially aid breeders through providing rapid and non-destructive proxies of crop phenotypic traits. Moreover, my findings indicate the possibility of developing cheaper spectral monitoring instruments without sacrificing prediction accuracy of crop traits.
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