Estimating Chlorophyll Content of Leafy Green Vegetables from Adaxial and Abaxial Reflectance.

Lu Lu,Bu Bu

doi:10.3390/s19194059

Abstract

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 (Brassica chinensis L. var. Shanghai Qing), Chinese white cabbage (Brassica campestris L. ssp. Chinensis Makino var. communis Tsen et Lee), and Romaine lettuce (Lactuca sativa var longifoliaf. Lam) 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 (R2 = 0.809, RMSE = 62.44 mg m−2), Chinese white cabbage (R2 = 0.891, RMSE = 45.18 mg m−2) and Romaine lettuce (R2 = 0.834, RMSE = 38.58 mg m−2) individually as well as of the three vegetables combined (R2 = 0.811, RMSE = 55.59 mg m−2). 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.

Highlights

The human consumption of leafy green vegetables has been increasing due to lifestyle changes in recent years, and the nutrition and health status of leafy green vegetables on the market is of important to consumers [1–3]
The results of this study presented that Modified Datt index (MDATT), formatted as the ratio of difference of reflectance is available for leaf chlorophyll content estimation on herbaceous plant leaves, such as vegetables
The reflectance from the adaxial and abaxial surfaces of three leafy green vegetables was measured in this study

Summary

Introduction

The human consumption of leafy green vegetables has been increasing due to lifestyle changes in recent years, and the nutrition and health status of leafy green vegetables on the market is of important to consumers [1–3]. Chlorophyll, as the primary pigment of leafy green vegetables, plays a major role in assessing the health status of vegetables. The nutritional status of leafy green vegetables can be monitored via quantifying chlorophyll content because most of the nitrogen is incorporated in leaf chlorophyll [4–7]. Many reflectance-based vegetation indices (VIs) that include a single band or multiple bands have been developed to estimate the chlorophyll content of plants. More sensitivity of reflectance in the red-edge region than the reflectance in the other bands to chlorophyll content of vegetation has been recognized for decades [13–17], and the red-edge bands have been widely used for biophysical parameters at leaf and canopy levels [18]. The red edge is the region of sharp change in vegetation reflectance spectra

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