Estimating Chlorophyll Content of Leafy Green Vegetables from Adaxial and Abaxial Reflectance
As a primary pigment of leafy green vegetables, chlorophyll plays a major role in indicating vegetable growth status. The application of hyperspectral remote sensing reflectance offers a quick and nondestructive method to estimate the chlorophyll content of vegetables. Reflectance of adaxial and aba...
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MDPI AG
2019-09-01
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| Online Access: | https://www.mdpi.com/1424-8220/19/19/4059 |
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| author | Fan Lu Zhaojun Bu Shan Lu |
| author_facet | Fan Lu Zhaojun Bu Shan Lu |
| author_sort | Fan Lu |
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| description | As a primary pigment of leafy green vegetables, chlorophyll plays a major role in indicating vegetable growth status. The application of hyperspectral remote sensing reflectance offers a quick and nondestructive method to estimate the chlorophyll content of vegetables. Reflectance of adaxial and abaxial leaf surfaces from three common leafy green vegetables: Pakchoi var. Shanghai Qing (<em>Brassica chinensis</em> L. var. Shanghai Qing), Chinese white cabbage (<em>Brassica campestris L. ssp. Chinensis Makino var. communis Tsen et Lee</em>), and Romaine lettuce (<em>Lactuca sativa</em> <em>var longifoliaf. Lam</em>) were measured to estimate the leaf chlorophyll content. Modeling based on spectral indices and the partial least squares regression (PLS) was tested using the reflectance data from the two surfaces (adaxial and abaxial) of leaves in the datasets of each individual vegetable and the three vegetables combined. The PLS regression model showed the highest accuracy in estimating leaf chlorophyll content of pakchoi var. Shanghai Qing (R<sup>2</sup> = 0.809, RMSE = 62.44 mg m<sup>−2</sup>), Chinese white cabbage (R<sup>2</sup> = 0.891, RMSE = 45.18 mg m<sup>−2</sup>) and Romaine lettuce (R<sup>2</sup> = 0.834, RMSE = 38.58 mg m<sup>−2</sup>) individually as well as of the three vegetables combined (R<sup>2</sup> = 0.811, RMSE = 55.59 mg m<sup>−2</sup>). The good predictability of the PLS regression model is considered to be due to the contribution of more spectral bands applied in it than that in the spectral indices. In addition, both the uninformative variable elimination PLS (UVE-PLS) technique and the best performed spectral index: MDATT, showed that the red-edge region (680−750 nm) was effective in estimating the chlorophyll content of vegetables with reflectance from two leaf surfaces. The combination of the PLS regression model and the red-edge region are insensitive to the difference between the adaxial and abaxial leaf structure and can be used for estimating the chlorophyll content of leafy green vegetables accurately. |
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| spelling | doaj.art-2fcc53761a4b49768ef8ee63373096e02022-12-22T04:19:55ZengMDPI AGSensors1424-82202019-09-011919405910.3390/s19194059s19194059Estimating Chlorophyll Content of Leafy Green Vegetables from Adaxial and Abaxial ReflectanceFan Lu0Zhaojun Bu1Shan Lu2Key Laboratory of Geographical Processes and Ecological Security in Changbai Mountains, Ministry of Education, School of Geographical Sciences, Northeast Normal University, Renmin 5268, Changchun 130024, ChinaKey Laboratory of Geographical Processes and Ecological Security in Changbai Mountains, Ministry of Education, School of Geographical Sciences, Northeast Normal University, Renmin 5268, Changchun 130024, ChinaKey Laboratory of Geographical Processes and Ecological Security in Changbai Mountains, Ministry of Education, School of Geographical Sciences, Northeast Normal University, Renmin 5268, Changchun 130024, ChinaAs a primary pigment of leafy green vegetables, chlorophyll plays a major role in indicating vegetable growth status. The application of hyperspectral remote sensing reflectance offers a quick and nondestructive method to estimate the chlorophyll content of vegetables. Reflectance of adaxial and abaxial leaf surfaces from three common leafy green vegetables: Pakchoi var. Shanghai Qing (<em>Brassica chinensis</em> L. var. Shanghai Qing), Chinese white cabbage (<em>Brassica campestris L. ssp. Chinensis Makino var. communis Tsen et Lee</em>), and Romaine lettuce (<em>Lactuca sativa</em> <em>var longifoliaf. Lam</em>) were measured to estimate the leaf chlorophyll content. Modeling based on spectral indices and the partial least squares regression (PLS) was tested using the reflectance data from the two surfaces (adaxial and abaxial) of leaves in the datasets of each individual vegetable and the three vegetables combined. The PLS regression model showed the highest accuracy in estimating leaf chlorophyll content of pakchoi var. Shanghai Qing (R<sup>2</sup> = 0.809, RMSE = 62.44 mg m<sup>−2</sup>), Chinese white cabbage (R<sup>2</sup> = 0.891, RMSE = 45.18 mg m<sup>−2</sup>) and Romaine lettuce (R<sup>2</sup> = 0.834, RMSE = 38.58 mg m<sup>−2</sup>) individually as well as of the three vegetables combined (R<sup>2</sup> = 0.811, RMSE = 55.59 mg m<sup>−2</sup>). The good predictability of the PLS regression model is considered to be due to the contribution of more spectral bands applied in it than that in the spectral indices. In addition, both the uninformative variable elimination PLS (UVE-PLS) technique and the best performed spectral index: MDATT, showed that the red-edge region (680−750 nm) was effective in estimating the chlorophyll content of vegetables with reflectance from two leaf surfaces. The combination of the PLS regression model and the red-edge region are insensitive to the difference between the adaxial and abaxial leaf structure and can be used for estimating the chlorophyll content of leafy green vegetables accurately.https://www.mdpi.com/1424-8220/19/19/4059adaxial and abaxialreflectancechlorophyllvegetation indexpartial least squares (PLS) |
| spellingShingle | Fan Lu Zhaojun Bu Shan Lu Estimating Chlorophyll Content of Leafy Green Vegetables from Adaxial and Abaxial Reflectance Sensors adaxial and abaxial reflectance chlorophyll vegetation index partial least squares (PLS) |
| title | Estimating Chlorophyll Content of Leafy Green Vegetables from Adaxial and Abaxial Reflectance |
| title_full | Estimating Chlorophyll Content of Leafy Green Vegetables from Adaxial and Abaxial Reflectance |
| title_fullStr | Estimating Chlorophyll Content of Leafy Green Vegetables from Adaxial and Abaxial Reflectance |
| title_full_unstemmed | Estimating Chlorophyll Content of Leafy Green Vegetables from Adaxial and Abaxial Reflectance |
| title_short | Estimating Chlorophyll Content of Leafy Green Vegetables from Adaxial and Abaxial Reflectance |
| title_sort | estimating chlorophyll content of leafy green vegetables from adaxial and abaxial reflectance |
| topic | adaxial and abaxial reflectance chlorophyll vegetation index partial least squares (PLS) |
| url | https://www.mdpi.com/1424-8220/19/19/4059 |
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