Estimating Leaf Nitrogen Content in Corn Based on Information Fusion of Multiple-Sensor Imagery from UAV
With the rapid development of unmanned aerial vehicle (UAV) and sensor technology, UAVs that can simultaneously carry different sensors have been increasingly used to monitor nitrogen status in crops due to their flexibility and adaptability. This study aimed to explore how to use the image informat...
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MDPI AG
2021-01-01
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| Series: | Remote Sensing |
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| Online Access: | https://www.mdpi.com/2072-4292/13/3/340 |
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| author | Xingang Xu Lingling Fan Zhenhai Li Yang Meng Haikuan Feng Hao Yang Bo Xu |
| author_facet | Xingang Xu Lingling Fan Zhenhai Li Yang Meng Haikuan Feng Hao Yang Bo Xu |
| author_sort | Xingang Xu |
| collection | DOAJ |
| description | With the rapid development of unmanned aerial vehicle (UAV) and sensor technology, UAVs that can simultaneously carry different sensors have been increasingly used to monitor nitrogen status in crops due to their flexibility and adaptability. This study aimed to explore how to use the image information combined from two different sensors mounted on an UAV to evaluate leaf nitrogen content (LNC) in corn. Field experiments with corn were conducted using different nitrogen rates and cultivars at the National Precision Agriculture Research and Demonstration Base in China in 2017. Digital RGB and multispectral images were obtained synchronously by UAV in the V12, R1, and R3 growth stages of corn, respectively. A novel family of modified vegetation indices, named coverage adjusted spectral indices (CASIs (CASI <inline-formula><math display="inline"><semantics><mrow><mo>=</mo><mi>VI</mi><mo>/</mo><mfenced><mrow><mn>1</mn><mo>+</mo><msub><mrow><mi>FV</mi></mrow><mrow><mi>cover</mi></mrow></msub></mrow></mfenced></mrow></semantics></math></inline-formula>, where VI denotes the reference vegetation index and <inline-formula><math display="inline"><semantics><mrow><msub><mrow><mi>FV</mi></mrow><mrow><mi>cover</mi></mrow></msub></mrow></semantics></math></inline-formula> refers to the fraction of vegetation coverage), has been introduced to estimate LNC in corn. Thereby, typical VIs were extracted from multispectral images, which have the advantage of relatively higher spectral resolution, and <inline-formula><math display="inline"><semantics><mrow><msub><mrow><mi>FV</mi></mrow><mrow><mi>cover</mi></mrow></msub></mrow></semantics></math></inline-formula> was calculated by RGB images that feature higher spatial resolution. Then, the PLS (partial least squares) method was employed to investigate the relationships between LNC and the optimal set of CASIs or VIs selected by the RFA (random frog algorithm) in different corn growth stages. The analysis results indicated that whether removing soil noise or not, CASIs guaranteed a better estimation of LNC than VIs for all of the three growth stages of corn, and the usage of CASIs in the R1 stage yielded the best <i>R</i><sup>2</sup> value of 0.59, with a RMSE (root mean square error) of 22.02% and NRMSE (normalized root mean square error) of 8.37%. It was concluded that CASIs, based on the fusion of information acquired synchronously from both lower resolution multispectral and higher resolution RGB images, have a good potential for crop nitrogen monitoring by UAV. Furthermore, they could also serve as a useful way for assessing other physical and chemical parameters in further applications for crops. |
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| language | English |
| last_indexed | 2024-03-09T04:12:50Z |
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| spelling | doaj.art-6adf5a87daa04386b87d23b1c9b0341d2023-12-03T13:58:12ZengMDPI AGRemote Sensing2072-42922021-01-0113334010.3390/rs13030340Estimating Leaf Nitrogen Content in Corn Based on Information Fusion of Multiple-Sensor Imagery from UAVXingang Xu0Lingling Fan1Zhenhai Li2Yang Meng3Haikuan Feng4Hao Yang5Bo Xu6Key Laboratory of Quantitative Remote Sensing in Agriculture of Ministry of Agriculture, Beijing Research Center for Information Technology in Agriculture, Beijing Academy of Agriculture and Forestry Sciences, Beijing 100097, ChinaKey Laboratory of Quantitative Remote Sensing in Agriculture of Ministry of Agriculture, Beijing Research Center for Information Technology in Agriculture, Beijing Academy of Agriculture and Forestry Sciences, Beijing 100097, ChinaKey Laboratory of Quantitative Remote Sensing in Agriculture of Ministry of Agriculture, Beijing Research Center for Information Technology in Agriculture, Beijing Academy of Agriculture and Forestry Sciences, Beijing 100097, ChinaKey Laboratory of Quantitative Remote Sensing in Agriculture of Ministry of Agriculture, Beijing Research Center for Information Technology in Agriculture, Beijing Academy of Agriculture and Forestry Sciences, Beijing 100097, ChinaKey Laboratory of Quantitative Remote Sensing in Agriculture of Ministry of Agriculture, Beijing Research Center for Information Technology in Agriculture, Beijing Academy of Agriculture and Forestry Sciences, Beijing 100097, ChinaKey Laboratory of Quantitative Remote Sensing in Agriculture of Ministry of Agriculture, Beijing Research Center for Information Technology in Agriculture, Beijing Academy of Agriculture and Forestry Sciences, Beijing 100097, ChinaKey Laboratory of Quantitative Remote Sensing in Agriculture of Ministry of Agriculture, Beijing Research Center for Information Technology in Agriculture, Beijing Academy of Agriculture and Forestry Sciences, Beijing 100097, ChinaWith the rapid development of unmanned aerial vehicle (UAV) and sensor technology, UAVs that can simultaneously carry different sensors have been increasingly used to monitor nitrogen status in crops due to their flexibility and adaptability. This study aimed to explore how to use the image information combined from two different sensors mounted on an UAV to evaluate leaf nitrogen content (LNC) in corn. Field experiments with corn were conducted using different nitrogen rates and cultivars at the National Precision Agriculture Research and Demonstration Base in China in 2017. Digital RGB and multispectral images were obtained synchronously by UAV in the V12, R1, and R3 growth stages of corn, respectively. A novel family of modified vegetation indices, named coverage adjusted spectral indices (CASIs (CASI <inline-formula><math display="inline"><semantics><mrow><mo>=</mo><mi>VI</mi><mo>/</mo><mfenced><mrow><mn>1</mn><mo>+</mo><msub><mrow><mi>FV</mi></mrow><mrow><mi>cover</mi></mrow></msub></mrow></mfenced></mrow></semantics></math></inline-formula>, where VI denotes the reference vegetation index and <inline-formula><math display="inline"><semantics><mrow><msub><mrow><mi>FV</mi></mrow><mrow><mi>cover</mi></mrow></msub></mrow></semantics></math></inline-formula> refers to the fraction of vegetation coverage), has been introduced to estimate LNC in corn. Thereby, typical VIs were extracted from multispectral images, which have the advantage of relatively higher spectral resolution, and <inline-formula><math display="inline"><semantics><mrow><msub><mrow><mi>FV</mi></mrow><mrow><mi>cover</mi></mrow></msub></mrow></semantics></math></inline-formula> was calculated by RGB images that feature higher spatial resolution. Then, the PLS (partial least squares) method was employed to investigate the relationships between LNC and the optimal set of CASIs or VIs selected by the RFA (random frog algorithm) in different corn growth stages. The analysis results indicated that whether removing soil noise or not, CASIs guaranteed a better estimation of LNC than VIs for all of the three growth stages of corn, and the usage of CASIs in the R1 stage yielded the best <i>R</i><sup>2</sup> value of 0.59, with a RMSE (root mean square error) of 22.02% and NRMSE (normalized root mean square error) of 8.37%. It was concluded that CASIs, based on the fusion of information acquired synchronously from both lower resolution multispectral and higher resolution RGB images, have a good potential for crop nitrogen monitoring by UAV. Furthermore, they could also serve as a useful way for assessing other physical and chemical parameters in further applications for crops.https://www.mdpi.com/2072-4292/13/3/340RGBmultispectralcoverage adjusted spectral indexvegetation indexvegetation coveragerandom frog algorithm |
| spellingShingle | Xingang Xu Lingling Fan Zhenhai Li Yang Meng Haikuan Feng Hao Yang Bo Xu Estimating Leaf Nitrogen Content in Corn Based on Information Fusion of Multiple-Sensor Imagery from UAV Remote Sensing RGB multispectral coverage adjusted spectral index vegetation index vegetation coverage random frog algorithm |
| title | Estimating Leaf Nitrogen Content in Corn Based on Information Fusion of Multiple-Sensor Imagery from UAV |
| title_full | Estimating Leaf Nitrogen Content in Corn Based on Information Fusion of Multiple-Sensor Imagery from UAV |
| title_fullStr | Estimating Leaf Nitrogen Content in Corn Based on Information Fusion of Multiple-Sensor Imagery from UAV |
| title_full_unstemmed | Estimating Leaf Nitrogen Content in Corn Based on Information Fusion of Multiple-Sensor Imagery from UAV |
| title_short | Estimating Leaf Nitrogen Content in Corn Based on Information Fusion of Multiple-Sensor Imagery from UAV |
| title_sort | estimating leaf nitrogen content in corn based on information fusion of multiple sensor imagery from uav |
| topic | RGB multispectral coverage adjusted spectral index vegetation index vegetation coverage random frog algorithm |
| url | https://www.mdpi.com/2072-4292/13/3/340 |
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