Abstract
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 (R520, R528), RSI (R748, R561), DSI (R758, R602) and SASI (R753, R624). 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.
Highlights
Chlorophyll is the primary substance used by green plants to absorb, transform and transmit light energy via photosynthesis and is associated with processes related to plant growth and senescence, photosynthetic capacity, disease, nutrition and environmental stress [1]
Chlorophyll absorption provides the necessary link between remote sensing observations and canopy state variables, so canopy state variables are used as indicators of plant N status and photosynthesis [3]
Peanut chlorophyll content under density treatment D2 was the highest, reaching 47.76 (Figure 3). These results showed that chlorophyll content in peanut leaves is affected by too high and too low seeding densities
Summary
Chlorophyll is the primary substance used by green plants to absorb, transform and transmit light energy via photosynthesis and is associated with processes related to plant growth and senescence, photosynthetic capacity, disease, nutrition and environmental stress [1]. The evaluation of chlorophyll content is of great significance in the study of plant physiology and ecology, as it is a measure of photosynthetic capacity, nitrogen levels and developmental status. Hyperspectral technology, a developed and mature technology that is increasingly being widely used in crop monitoring, carries advantages of low consumption, velocity and no vegetation damage, it provides new opportunities to obtain plant physiological information [4,5,6]. Hyperspectral spectrometry predicts chlorophyll content by measuring the reflectance of plant leaves. The response of leaf and canopy spectral reflectance or transmittance to photosynthetic pigments can be used as a powerful means to monitor crop growth, regulate fertilizer application and estimate expected yield. Two methods are generally employed for estimating vegetation physiological parameters using hyperspectral data: (i) Optical radiation transmission model [7,8]; and (ii) determining the empirical relationship between vegetation physiological parameters and spectral vegetation indices [9]
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