Using Digital Cameras on an Unmanned Aerial Vehicle to Derive Optimum Color Vegetation Indices for Leaf Nitrogen Concentration Monitoring in Winter Wheat
Commercially available digital cameras can be mounted on an unmanned aerial vehicle (UAV) for crop growth monitoring in open-air fields as a low-cost, highly effective observation system. However, few studies have investigated their potential for nitrogen (N) status monitoring, and the performance o...
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description | Commercially available digital cameras can be mounted on an unmanned aerial vehicle (UAV) for crop growth monitoring in open-air fields as a low-cost, highly effective observation system. However, few studies have investigated their potential for nitrogen (N) status monitoring, and the performance of camera-derived vegetation indices (VIs) under different conditions remains poorly understood. In this study, five commonly used VIs derived from normal color (RGB) images and two typical VIs derived from color near-infrared (CIR) images were used to estimate leaf N concentration (LNC). To explore the potential of digital cameras for monitoring LNC at all crop growth stages, two new VIs were proposed, namely, the true color vegetation index (TCVI) from RGB images and the false color vegetation index (FCVI) from CIR images. The relationships between LNC and the different VIs varied at different stages. The commonly used VIs performed well at some stages, but the newly proposed TCVI and FCVI had the best performance at all stages. The performances of the VIs with red (or near-infrared) and green bands as the numerator were limited by saturation at intermediate to high LNCs (LNC > 3.0%), but the TCVI and FCVI had the ability to mitigate the saturation. The results of model validations further supported the superiority of the TCVI and FCVI for LNC estimation. Compared to the other VIs derived using RGB cameras, the relative root mean square errors (RRMSEs) of the TCVI were improved by 8.6% on average. For the CIR images, the best-performing VI for LNC was the FCVI (R2 = 0.756, RRMSE = 14.18%). The LNC–TCVI and LNC–FCVI were stable under different cultivars, N application rates, and planting densities. The results confirmed the applicability of UAV-based RGB and CIR cameras for crop N status monitoring under different conditions, which should assist the precision management of N fertilizers in agronomic practices. |
doi_str_mv | 10.3390/rs11222667 |
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However, few studies have investigated their potential for nitrogen (N) status monitoring, and the performance of camera-derived vegetation indices (VIs) under different conditions remains poorly understood. In this study, five commonly used VIs derived from normal color (RGB) images and two typical VIs derived from color near-infrared (CIR) images were used to estimate leaf N concentration (LNC). To explore the potential of digital cameras for monitoring LNC at all crop growth stages, two new VIs were proposed, namely, the true color vegetation index (TCVI) from RGB images and the false color vegetation index (FCVI) from CIR images. The relationships between LNC and the different VIs varied at different stages. The commonly used VIs performed well at some stages, but the newly proposed TCVI and FCVI had the best performance at all stages. The performances of the VIs with red (or near-infrared) and green bands as the numerator were limited by saturation at intermediate to high LNCs (LNC > 3.0%), but the TCVI and FCVI had the ability to mitigate the saturation. The results of model validations further supported the superiority of the TCVI and FCVI for LNC estimation. Compared to the other VIs derived using RGB cameras, the relative root mean square errors (RRMSEs) of the TCVI were improved by 8.6% on average. For the CIR images, the best-performing VI for LNC was the FCVI (R2 = 0.756, RRMSE = 14.18%). The LNC–TCVI and LNC–FCVI were stable under different cultivars, N application rates, and planting densities. The results confirmed the applicability of UAV-based RGB and CIR cameras for crop N status monitoring under different conditions, which should assist the precision management of N fertilizers in agronomic practices.</description><identifier>ISSN: 2072-4292</identifier><identifier>EISSN: 2072-4292</identifier><identifier>DOI: 10.3390/rs11222667</identifier><language>eng</language><publisher>Basel: MDPI AG</publisher><subject>Agricultural practices ; Air monitoring ; Biomass ; Cameras ; Color ; Color imagery ; Crop growth ; Crops ; Cultivars ; Digital cameras ; Fertilizers ; I.R. radiation ; Image processing ; Infrared imagery ; Leaves ; Nitrogen ; Remote sensing ; Saturation ; Unmanned aerial vehicles ; Vegetation ; Vegetation index ; Wheat ; Winter wheat</subject><ispartof>Remote sensing (Basel, Switzerland), 2019-11, Vol.11 (22), p.2667</ispartof><rights>2019 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c295t-9117b8a3198ff4492475a76ad7bb0d443e50d47b36b75ebbc51e1c04d54fdd9f3</citedby><cites>FETCH-LOGICAL-c295t-9117b8a3198ff4492475a76ad7bb0d443e50d47b36b75ebbc51e1c04d54fdd9f3</cites><orcidid>0000-0002-2214-7009 ; 0000-0002-4184-0730 ; 0000-0002-1884-2404</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><link.rule.ids>314,777,781,861,27905,27906</link.rule.ids></links><search><creatorcontrib>Jiang, Jiale</creatorcontrib><creatorcontrib>Cai, Weidi</creatorcontrib><creatorcontrib>Zheng, Hengbiao</creatorcontrib><creatorcontrib>Cheng, Tao</creatorcontrib><creatorcontrib>Tian, Yongchao</creatorcontrib><creatorcontrib>Zhu, Yan</creatorcontrib><creatorcontrib>Ehsani, Reza</creatorcontrib><creatorcontrib>Hu, Yongqiang</creatorcontrib><creatorcontrib>Niu, Qingsong</creatorcontrib><creatorcontrib>Gui, Lijuan</creatorcontrib><creatorcontrib>Yao, Xia</creatorcontrib><title>Using Digital Cameras on an Unmanned Aerial Vehicle to Derive Optimum Color Vegetation Indices for Leaf Nitrogen Concentration Monitoring in Winter Wheat</title><title>Remote sensing (Basel, Switzerland)</title><description>Commercially available digital cameras can be mounted on an unmanned aerial vehicle (UAV) for crop growth monitoring in open-air fields as a low-cost, highly effective observation system. However, few studies have investigated their potential for nitrogen (N) status monitoring, and the performance of camera-derived vegetation indices (VIs) under different conditions remains poorly understood. In this study, five commonly used VIs derived from normal color (RGB) images and two typical VIs derived from color near-infrared (CIR) images were used to estimate leaf N concentration (LNC). To explore the potential of digital cameras for monitoring LNC at all crop growth stages, two new VIs were proposed, namely, the true color vegetation index (TCVI) from RGB images and the false color vegetation index (FCVI) from CIR images. The relationships between LNC and the different VIs varied at different stages. The commonly used VIs performed well at some stages, but the newly proposed TCVI and FCVI had the best performance at all stages. The performances of the VIs with red (or near-infrared) and green bands as the numerator were limited by saturation at intermediate to high LNCs (LNC > 3.0%), but the TCVI and FCVI had the ability to mitigate the saturation. The results of model validations further supported the superiority of the TCVI and FCVI for LNC estimation. Compared to the other VIs derived using RGB cameras, the relative root mean square errors (RRMSEs) of the TCVI were improved by 8.6% on average. For the CIR images, the best-performing VI for LNC was the FCVI (R2 = 0.756, RRMSE = 14.18%). The LNC–TCVI and LNC–FCVI were stable under different cultivars, N application rates, and planting densities. The results confirmed the applicability of UAV-based RGB and CIR cameras for crop N status monitoring under different conditions, which should assist the precision management of N fertilizers in agronomic practices.</description><subject>Agricultural practices</subject><subject>Air monitoring</subject><subject>Biomass</subject><subject>Cameras</subject><subject>Color</subject><subject>Color imagery</subject><subject>Crop growth</subject><subject>Crops</subject><subject>Cultivars</subject><subject>Digital cameras</subject><subject>Fertilizers</subject><subject>I.R. radiation</subject><subject>Image processing</subject><subject>Infrared imagery</subject><subject>Leaves</subject><subject>Nitrogen</subject><subject>Remote sensing</subject><subject>Saturation</subject><subject>Unmanned aerial vehicles</subject><subject>Vegetation</subject><subject>Vegetation index</subject><subject>Wheat</subject><subject>Winter 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Tao</au><au>Tian, Yongchao</au><au>Zhu, Yan</au><au>Ehsani, Reza</au><au>Hu, Yongqiang</au><au>Niu, Qingsong</au><au>Gui, Lijuan</au><au>Yao, Xia</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Using Digital Cameras on an Unmanned Aerial Vehicle to Derive Optimum Color Vegetation Indices for Leaf Nitrogen Concentration Monitoring in Winter Wheat</atitle><jtitle>Remote sensing (Basel, Switzerland)</jtitle><date>2019-11-14</date><risdate>2019</risdate><volume>11</volume><issue>22</issue><spage>2667</spage><pages>2667-</pages><issn>2072-4292</issn><eissn>2072-4292</eissn><abstract>Commercially available digital cameras can be mounted on an unmanned aerial vehicle (UAV) for crop growth monitoring in open-air fields as a low-cost, highly effective observation system. However, few studies have investigated their potential for nitrogen (N) status monitoring, and the performance of camera-derived vegetation indices (VIs) under different conditions remains poorly understood. In this study, five commonly used VIs derived from normal color (RGB) images and two typical VIs derived from color near-infrared (CIR) images were used to estimate leaf N concentration (LNC). To explore the potential of digital cameras for monitoring LNC at all crop growth stages, two new VIs were proposed, namely, the true color vegetation index (TCVI) from RGB images and the false color vegetation index (FCVI) from CIR images. The relationships between LNC and the different VIs varied at different stages. The commonly used VIs performed well at some stages, but the newly proposed TCVI and FCVI had the best performance at all stages. The performances of the VIs with red (or near-infrared) and green bands as the numerator were limited by saturation at intermediate to high LNCs (LNC > 3.0%), but the TCVI and FCVI had the ability to mitigate the saturation. The results of model validations further supported the superiority of the TCVI and FCVI for LNC estimation. Compared to the other VIs derived using RGB cameras, the relative root mean square errors (RRMSEs) of the TCVI were improved by 8.6% on average. For the CIR images, the best-performing VI for LNC was the FCVI (R2 = 0.756, RRMSE = 14.18%). The LNC–TCVI and LNC–FCVI were stable under different cultivars, N application rates, and planting densities. 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subjects | Agricultural practices Air monitoring Biomass Cameras Color Color imagery Crop growth Crops Cultivars Digital cameras Fertilizers I.R. radiation Image processing Infrared imagery Leaves Nitrogen Remote sensing Saturation Unmanned aerial vehicles Vegetation Vegetation index Wheat Winter wheat |
title | Using Digital Cameras on an Unmanned Aerial Vehicle to Derive Optimum Color Vegetation Indices for Leaf Nitrogen Concentration Monitoring in Winter Wheat |
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