Hyperspectral wavelength selection for estimating chlorophyll content of muskmelon leaves

Abstract

Quantifying chlorophyll content, an effective indicator of disease as well as nutritional and environmental stresses on plants, may enable optimal fertilization while managing crops. Hyperspectral remote-sensing is commonly used to estimate chlorophyll content. In this context, the process of variable selection is crucial since it is necessary to identify variables relevant to chlorophyll and eliminate redundant variables. In this study, 14 wavelength selection methods based on partial least squares (PLS; namely, backward variable elimination, backward and forward interval-PLS, competitive adaptive reweighted sampling, genetic algorithm, iterative predictive weighting, loading-weights, PLS with Martens' uncertainty test, regression coefficient, regularized elimination procedure, sparse-PLS, sub-window permutation analysis, uninformative variable elimination and variable importance in projection) were combined with one of five machine learning algorithms (Cubist, deep belief nets, random forests, stochastic gradient boosting and support vector machine) and then evaluated. According to the ratio of performance to deviation (RPD), the best combination of variable selection method and machine learning algorithm was regularized elimination procedure and Cubist achieving an RPD of 1.76 and an RMSE of 2.42 μg cm −2 .

Monitoring of Chlorophyll Content of Potato in Northern Shaanxi Based on Different Spectral Parameters

“…Chlorophyll content directly determines the photosynthetic activity of crops and is an important physiological indicator for evaluating crop growth status. Combining hyperspectral remote sensing to estimate crop LCC is beneficial for accurately assessing its estimation capability and comprehensively evaluating crop growth status [36].…”

Evaluation of a One-Dimensional Convolution Neural Network for Chlorophyll Content Estimation Using a Compact Spectrometer

“…DBNs have been applied to extract vegetation properties, such as quality (chlorophylla content) and stress (chlorophyll-a: b), from hyperspectral data for improved tea tree management, and some pre-processing could be reduced [54,67]. The initial configurations were the learning rate (0.1), the maximum iteration number of the pre-training dataset (100), the learning rate of the pre-training dataset (0.01), the maximum iteration number of the training dataset (100), and the batch data size (10) following previous studies [67,68]. DBN regression was implemented using the "darch" package in R version 4.0.6 [69].…”

“…However, the leaf dry weight and thickness often make the results of such meters ambiguous [4,5], and the use of these devices is restricted. Alternative techniques based on hyperspectral remote reflectance using portable spectroradiometers, such as the Ocean Optics hyperspectral visible and near-infrared (Vis-NIR) spectroradiometer [6,7] and the Analytical Spectral Devices (ASD) FieldSpec series [8][9][10], have been proposed. Reflectance in the blue (420-470 nm) and red (640-680 nm) wavelength ranges depends on the leaf pigment, especially that due to chlorophyll, and a peak in the green region (520-580 nm) indicates a high chlorophyll content [11].…”

Effect Analysis of Hydrogen Peroxide Using Hyperspectral Reflectance in Sorghum [Sorghum bicolor (L.) Moench] under Drought Stress

“…This technology can extract relevant parts of information about huge data matrices and generate the most reliable models for other calibration methods [ 16 , 17 ]. The PLS method has been used in plant science to predict plant biomass [ 8 ], LAI, nitrogen [ 10 ], and chlorophyll concentrations [ 18 ]. The relationship between spectral properties and reflectance to the functional characteristics of plants is not yet clear or known, and the spectral reflectance of plants related to plant water stress or physiological changes is very complicated.…”