Detection of Canopy Chlorophyll Content of Corn Based on Continuous Wavelet Transform Analysis

Abstract

The content of chlorophyll, an important substance for photosynthesis in plants, is an important index used to characterize the photosynthetic rate and nutrient grade of plants. The real-time rapid acquisition of crop chlorophyll content is of great significance for guiding fine management and differentiated fertilization in the field. This study used the method of continuous wavelet transform (CWT) to process the collected visible and near-infrared spectra of a corn canopy. This task was conducted to extract the valuable information in the spectral data and improve the sensitivity of chlorophyll content assessment. First, a Savitzky–Golay filter and standard normal variable processing were applied to the spectral data to eliminate the influence of random noise and limit drift on spectral reflectance. Second, CWT was performed on the spectral reflection curve with 10 frequency scales to obtain the wavelet energy coefficient of the spectral data. The characteristic bands related to chlorophyll content in the spectral data and the wavelet energy coefficients were screened using the maximum correlation coefficient and the local correlation coefficient extrema, respectively. A partial least-square regression model was established. Results showed that the characteristic bands selected via local correlation coefficient extrema in a wavelet energy coefficient created a detection model with optimal accuracy. The determination coefficient (Rc2) of the calibration set was 0.7856, and the root-mean-square error (RMSE) of the calibration set (RMSEC) was 3.0408. The determination coefficient (Rv2) of the validation set is was 0.7364, and the RMSE of the validation set (RMSEV) was 3.3032. Continuous wavelet transform is a process of data dimension enhancement which can effectively extract the sensitive variables from spectral datasets and improve the detection accuracy of models.

Detection of Larch Forest Stress from Jas’s Larch Inchworm (Erannis jacobsoni Djak) Attack Using Hyperspectral Remote Sensing

“…Hence, when we use Pearson correlation to analyze the relationship between the hyperspectral continuous wavelet coefficient and the chlorophyll content and water content of leaves, we find that there are many sensitive band regions of different degrees. Some recent study also confirmed that hyperspectral continuous wavelet coefficients are sensitive to changes in plant biochemical parameters [19,53,54]. Such a large number of sensitive bands extracted will affect the stability and accuracy of later modeling because it contains a large number of continuous bands.…”

“…They concluded that the relative moisture content of oak leaves and the absorption characteristic parameters exhibit linear relationships at 975 nm, 1200 nm, and 1750 nm, which indicates that hyperspectral can capture changes in moisture content. Zhang et al [19] used spectral continuous wavelet coefficients to estimate the chlorophyll content of corn pests, and the results confirmed the potential of hyperspectral inversion for determining chlorophyll content. Asner et al [20] developed a spectrum feature of Rapid Ohia Death (ROD) and found that 80% of plants infected with fungal pathogens had reduced water content and chlorophyll content.…”

“…This experiment aims to explore a new method for extracting continuous wavelet spectral features sensitive to chlorophyll and moisture content. Pearson correlation analysis is usually used to extract sensitive spectral features [19,[24][25][26]. Then the more sensitive spectral bands are further screened out by statistical analysis.…”

Semi-Automatic Spectral Image Stitching for a Compact Hybrid Linescan Hyperspectral Camera towards Near Field Remote Monitoring of Potato Crop Leaves

“…Plant disease monitoring is of fundamental interest for crop management. Several studies related to different kinds of cultures have thus emerged, e.g., corn [1], wheat [2], grapes [3], or potatoes [4].…”

Analysis of Chlorophyll Concentration in Potato Crop by Coupling Continuous Wavelet Transform and Spectral Variable Optimization

“…Three major problems of crop chlorophyll content analysis based on proximal spectroscopy methods include spectral signal-noise reduction, characteristic variable analysis, and analysis model establishment [13,14]. Among them, the noise reduction of spectral data is the primary step to improve the spectral data performance.…”