Wheat Height Estimation Using LiDAR in Comparison to Ultrasonic Sensor and UAS

As one of the key crop traits, plant height is traditionally evaluated manually, which can be slow, laborious and prone to error. Rapid development of remote and proximal sensing technologies in recent years allows plant height to be estimated in more objective and efficient fashions, while research...
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Autor*in:	Wenan Yuan [verfasserIn] Jiating Li [verfasserIn] Madhav Bhatta [verfasserIn] Yeyin Shi [verfasserIn] P. Stephen Baenziger [verfasserIn] Yufeng Ge [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2018

Schlagwörter:	crop plant breeding phenotyping proximal sensing remote sensing

Übergeordnetes Werk:	In: Sensors - MDPI AG, 2003, 18(2018), 11, p 3731
Übergeordnetes Werk:	volume:18 ; year:2018 ; number:11, p 3731

Links:	Link aufrufen Link aufrufen Link aufrufen Journal toc

DOI / URN:	10.3390/s18113731

Katalog-ID:	DOAJ084601558

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allfieldsSound	10.3390/s18113731 doi (DE-627)DOAJ084601558 (DE-599)DOAJf7ad07902bfb43519eafbe75734f512d DE-627 ger DE-627 rakwb eng TP1-1185 Wenan Yuan verfasserin aut Wheat Height Estimation Using LiDAR in Comparison to Ultrasonic Sensor and UAS 2018 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier As one of the key crop traits, plant height is traditionally evaluated manually, which can be slow, laborious and prone to error. Rapid development of remote and proximal sensing technologies in recent years allows plant height to be estimated in more objective and efficient fashions, while research regarding direct comparisons between different height measurement methods seems to be lagging. In this study, a ground-based multi-sensor phenotyping system equipped with ultrasonic sensors and light detection and ranging (LiDAR) was developed. Canopy heights of 100 wheat plots were estimated five times during a season by the ground phenotyping system and an unmanned aircraft system (UAS), and the results were compared to manual measurements. Overall, LiDAR provided the best results, with a root-mean-square error (RMSE) of 0.05 m and an R<sup<2</sup< of 0.97. UAS obtained reasonable results with an RMSE of 0.09 m and an R<sup<2</sup< of 0.91. Ultrasonic sensors did not perform well due to our static measurement style. In conclusion, we suggest LiDAR and UAS are reliable alternative methods for wheat height evaluation. crop plant breeding phenotyping proximal sensing remote sensing Chemical technology Jiating Li verfasserin aut Madhav Bhatta verfasserin aut Yeyin Shi verfasserin aut P. Stephen Baenziger verfasserin aut Yufeng Ge verfasserin aut In Sensors MDPI AG, 2003 18(2018), 11, p 3731 (DE-627)331640910 (DE-600)2052857-7 14248220 nnns volume:18 year:2018 number:11, p 3731 https://doi.org/10.3390/s18113731 kostenfrei https://doaj.org/article/f7ad07902bfb43519eafbe75734f512d kostenfrei https://www.mdpi.com/1424-8220/18/11/3731 kostenfrei https://doaj.org/toc/1424-8220 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ SSG-OLC-PHA GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2111 GBV_ILN_2507 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 18 2018 11, p 3731
language	English
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authorswithroles_txt_mv	Wenan Yuan @@aut@@ Jiating Li @@aut@@ Madhav Bhatta @@aut@@ Yeyin Shi @@aut@@ P. Stephen Baenziger @@aut@@ Yufeng Ge @@aut@@
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callnumber-first	T - Technology
author	Wenan Yuan
spellingShingle	Wenan Yuan misc TP1-1185 misc crop misc plant breeding misc phenotyping misc proximal sensing misc remote sensing misc Chemical technology Wheat Height Estimation Using LiDAR in Comparison to Ultrasonic Sensor and UAS
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topic_title	TP1-1185 Wheat Height Estimation Using LiDAR in Comparison to Ultrasonic Sensor and UAS crop plant breeding phenotyping proximal sensing remote sensing
topic	misc TP1-1185 misc crop misc plant breeding misc phenotyping misc proximal sensing misc remote sensing misc Chemical technology
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title	Wheat Height Estimation Using LiDAR in Comparison to Ultrasonic Sensor and UAS
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title_full	Wheat Height Estimation Using LiDAR in Comparison to Ultrasonic Sensor and UAS
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title_sort	wheat height estimation using lidar in comparison to ultrasonic sensor and uas
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title_auth	Wheat Height Estimation Using LiDAR in Comparison to Ultrasonic Sensor and UAS
abstract	As one of the key crop traits, plant height is traditionally evaluated manually, which can be slow, laborious and prone to error. Rapid development of remote and proximal sensing technologies in recent years allows plant height to be estimated in more objective and efficient fashions, while research regarding direct comparisons between different height measurement methods seems to be lagging. In this study, a ground-based multi-sensor phenotyping system equipped with ultrasonic sensors and light detection and ranging (LiDAR) was developed. Canopy heights of 100 wheat plots were estimated five times during a season by the ground phenotyping system and an unmanned aircraft system (UAS), and the results were compared to manual measurements. Overall, LiDAR provided the best results, with a root-mean-square error (RMSE) of 0.05 m and an R<sup<2</sup< of 0.97. UAS obtained reasonable results with an RMSE of 0.09 m and an R<sup<2</sup< of 0.91. Ultrasonic sensors did not perform well due to our static measurement style. In conclusion, we suggest LiDAR and UAS are reliable alternative methods for wheat height evaluation.
abstractGer	As one of the key crop traits, plant height is traditionally evaluated manually, which can be slow, laborious and prone to error. Rapid development of remote and proximal sensing technologies in recent years allows plant height to be estimated in more objective and efficient fashions, while research regarding direct comparisons between different height measurement methods seems to be lagging. In this study, a ground-based multi-sensor phenotyping system equipped with ultrasonic sensors and light detection and ranging (LiDAR) was developed. Canopy heights of 100 wheat plots were estimated five times during a season by the ground phenotyping system and an unmanned aircraft system (UAS), and the results were compared to manual measurements. Overall, LiDAR provided the best results, with a root-mean-square error (RMSE) of 0.05 m and an R<sup<2</sup< of 0.97. UAS obtained reasonable results with an RMSE of 0.09 m and an R<sup<2</sup< of 0.91. Ultrasonic sensors did not perform well due to our static measurement style. In conclusion, we suggest LiDAR and UAS are reliable alternative methods for wheat height evaluation.
abstract_unstemmed	As one of the key crop traits, plant height is traditionally evaluated manually, which can be slow, laborious and prone to error. Rapid development of remote and proximal sensing technologies in recent years allows plant height to be estimated in more objective and efficient fashions, while research regarding direct comparisons between different height measurement methods seems to be lagging. In this study, a ground-based multi-sensor phenotyping system equipped with ultrasonic sensors and light detection and ranging (LiDAR) was developed. Canopy heights of 100 wheat plots were estimated five times during a season by the ground phenotyping system and an unmanned aircraft system (UAS), and the results were compared to manual measurements. Overall, LiDAR provided the best results, with a root-mean-square error (RMSE) of 0.05 m and an R<sup<2</sup< of 0.97. UAS obtained reasonable results with an RMSE of 0.09 m and an R<sup<2</sup< of 0.91. Ultrasonic sensors did not perform well due to our static measurement style. In conclusion, we suggest LiDAR and UAS are reliable alternative methods for wheat height evaluation.
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container_issue	11, p 3731
title_short	Wheat Height Estimation Using LiDAR in Comparison to Ultrasonic Sensor and UAS
url	https://doi.org/10.3390/s18113731 https://doaj.org/article/f7ad07902bfb43519eafbe75734f512d https://www.mdpi.com/1424-8220/18/11/3731 https://doaj.org/toc/1424-8220
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author2	Jiating Li Madhav Bhatta Yeyin Shi P. Stephen Baenziger Yufeng Ge
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up_date	2024-07-03T23:46:25.580Z
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Wheat Height Estimation Using LiDAR in Comparison to Ultrasonic Sensor and UAS

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