Hyperspectral Inversion Model of Chlorophyll Content in Peanut Leaves
The purpose of this study is to determine a method for quickly and accurately estimating the chlorophyll content of peanut plants at different plant densities. This was explored using leaf spectral reflectance to monitor peanut chlorophyll content to detect sensitive spectral bands and the optimum s...
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MDPI AG
2020-03-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/10/7/2259 |
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| author | Haixia Qi Bingyu Zhu Lingxi Kong Weiguang Yang Jun Zou Yubin Lan Lei Zhang |
| author_facet | Haixia Qi Bingyu Zhu Lingxi Kong Weiguang Yang Jun Zou Yubin Lan Lei Zhang |
| author_sort | Haixia Qi |
| collection | DOAJ |
| description | The purpose of this study is to determine a method for quickly and accurately estimating the chlorophyll content of peanut plants at different plant densities. This was explored using leaf spectral reflectance to monitor peanut chlorophyll content to detect sensitive spectral bands and the optimum spectral indicators to establish a quantitative model. Peanut plants under different plant density conditions were monitored during three consecutive growth periods; single-photon avalanche diode (SPAD) and hyperspectral data derived from the leaves under the different plant density conditions were recorded. By combining arbitrary bands, indices were constructed across the full spectral range (350−2500 nm) based on blade spectra: the normalized difference spectral index (NDSI), ratio spectral index (RSI), difference spectral index (DSI) and soil-adjusted spectral index (SASI). This enabled the best vegetation index reflecting peanut-leaf SPAD values to be screened out by quantifying correlations with chlorophyll content, and the peanut leaf SPAD estimation models established by regression analysis to be compared and analyzed. The results showed that the chlorophyll content of peanut leaves decreased when plant density was either too high or too low, and that it reached its maximum at the appropriate plant density. In addition, differences in the spectral reflectance of peanut leaves under different chlorophyll content levels were highly obvious. Without considering the influence of cell structure as chlorophyll content increased, leaf spectral reflectance in the visible (350−700 nm): near-infrared (700−1300 nm) ranges also increased. The spectral bands sensitive to chlorophyll content were mainly observed in the visible and near-infrared ranges. The study results showed that the best spectral indicators for determining peanut chlorophyll content were NDSI (R<sub>520</sub>, R<sub>528</sub>), RSI (R<sub>748</sub>, R<sub>561</sub>), DSI (R<sub>758</sub>, R<sub>602</sub>) and SASI (R<sub>753</sub>, R<sub>624</sub>). Testing of these regression models showed that coefficient of determination values based on the NDSI, RSI, DSI and SASI estimation models were all greater than 0.65, while root mean square error values were all lower than 2.04. Therefore, the regression model established according to the above spectral indicators was a valid predictor of the chlorophyll content of peanut leaves. |
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| id | doaj.art-d247c8c9a6c04f53ada15e396fe02f48 |
| institution | Directory Open Access Journal |
| issn | 2076-3417 |
| language | English |
| last_indexed | 2024-12-12T15:23:40Z |
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| spelling | doaj.art-d247c8c9a6c04f53ada15e396fe02f482022-12-22T00:20:19ZengMDPI AGApplied Sciences2076-34172020-03-01107225910.3390/app10072259app10072259Hyperspectral Inversion Model of Chlorophyll Content in Peanut LeavesHaixia Qi0Bingyu Zhu1Lingxi Kong2Weiguang Yang3Jun Zou4Yubin Lan5Lei Zhang6National Center for International Collaboration Research on Precision Agricultural Aviation Pesticides, Spraying Technology (NPAAC), South China Agricultural University, Guangzhou 510642, ChinaNational Center for International Collaboration Research on Precision Agricultural Aviation Pesticides, Spraying Technology (NPAAC), South China Agricultural University, Guangzhou 510642, ChinaCollege of Engineering, South China Agricultural University, Guangzhou 510642, ChinaNational Center for International Collaboration Research on Precision Agricultural Aviation Pesticides, Spraying Technology (NPAAC), South China Agricultural University, Guangzhou 510642, ChinaNational Center for International Collaboration Research on Precision Agricultural Aviation Pesticides, Spraying Technology (NPAAC), South China Agricultural University, Guangzhou 510642, ChinaNational Center for International Collaboration Research on Precision Agricultural Aviation Pesticides, Spraying Technology (NPAAC), South China Agricultural University, Guangzhou 510642, ChinaNational Center for International Collaboration Research on Precision Agricultural Aviation Pesticides, Spraying Technology (NPAAC), South China Agricultural University, Guangzhou 510642, ChinaThe purpose of this study is to determine a method for quickly and accurately estimating the chlorophyll content of peanut plants at different plant densities. This was explored using leaf spectral reflectance to monitor peanut chlorophyll content to detect sensitive spectral bands and the optimum spectral indicators to establish a quantitative model. Peanut plants under different plant density conditions were monitored during three consecutive growth periods; single-photon avalanche diode (SPAD) and hyperspectral data derived from the leaves under the different plant density conditions were recorded. By combining arbitrary bands, indices were constructed across the full spectral range (350−2500 nm) based on blade spectra: the normalized difference spectral index (NDSI), ratio spectral index (RSI), difference spectral index (DSI) and soil-adjusted spectral index (SASI). This enabled the best vegetation index reflecting peanut-leaf SPAD values to be screened out by quantifying correlations with chlorophyll content, and the peanut leaf SPAD estimation models established by regression analysis to be compared and analyzed. The results showed that the chlorophyll content of peanut leaves decreased when plant density was either too high or too low, and that it reached its maximum at the appropriate plant density. In addition, differences in the spectral reflectance of peanut leaves under different chlorophyll content levels were highly obvious. Without considering the influence of cell structure as chlorophyll content increased, leaf spectral reflectance in the visible (350−700 nm): near-infrared (700−1300 nm) ranges also increased. The spectral bands sensitive to chlorophyll content were mainly observed in the visible and near-infrared ranges. The study results showed that the best spectral indicators for determining peanut chlorophyll content were NDSI (R<sub>520</sub>, R<sub>528</sub>), RSI (R<sub>748</sub>, R<sub>561</sub>), DSI (R<sub>758</sub>, R<sub>602</sub>) and SASI (R<sub>753</sub>, R<sub>624</sub>). Testing of these regression models showed that coefficient of determination values based on the NDSI, RSI, DSI and SASI estimation models were all greater than 0.65, while root mean square error values were all lower than 2.04. Therefore, the regression model established according to the above spectral indicators was a valid predictor of the chlorophyll content of peanut leaves.https://www.mdpi.com/2076-3417/10/7/2259chlorophyllremote sensinghyperspectralvegetation index |
| spellingShingle | Haixia Qi Bingyu Zhu Lingxi Kong Weiguang Yang Jun Zou Yubin Lan Lei Zhang Hyperspectral Inversion Model of Chlorophyll Content in Peanut Leaves Applied Sciences chlorophyll remote sensing hyperspectral vegetation index |
| title | Hyperspectral Inversion Model of Chlorophyll Content in Peanut Leaves |
| title_full | Hyperspectral Inversion Model of Chlorophyll Content in Peanut Leaves |
| title_fullStr | Hyperspectral Inversion Model of Chlorophyll Content in Peanut Leaves |
| title_full_unstemmed | Hyperspectral Inversion Model of Chlorophyll Content in Peanut Leaves |
| title_short | Hyperspectral Inversion Model of Chlorophyll Content in Peanut Leaves |
| title_sort | hyperspectral inversion model of chlorophyll content in peanut leaves |
| topic | chlorophyll remote sensing hyperspectral vegetation index |
| url | https://www.mdpi.com/2076-3417/10/7/2259 |
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