# Rapid and Nondestructive Evaluation of Wheat Chlorophyll under Drought Stress Using Hyperspectral Imaging

^{1}

^{2}

^{*}

## Abstract

**:**

^{2}, 0.61). The models established in this study are efficient for evaluating chlorophyll content and provide insight into photosynthesis and drought resistance. This study can provide a reference for high-throughput phenotypic analysis and genetic breeding of wheat and other crops.

## 1. Introduction

## 2. Results

#### 2.1. Chlorophyll Changes in Wheat Leaves under Different Soil Moisture Conditions

#### 2.2. Hyperspectral Characteristics of Wheat Leaves under Different Soil Moisture Conditions

#### 2.2.1. Spectral Reflectance Characteristics under Different Soil Moisture Conditions

#### 2.2.2. Spectral First Derivative Values under Different Soil Moisture Conditions

#### 2.2.3. Spectral Images under Different Soil Moisture Conditions

#### 2.3. Correlation Analysis between Hyperspectral Characteristics and SPAD Values

#### 2.4. The Characteristic Bands Identified with the Successive Projections Algorithm for Estimating SPAD Values

#### 2.5. Principal Component Analysis of Hyperspectral Information

#### 2.6. Estimation of SPAD Values Based on Regression Analysis

#### 2.6.1. Estimation of SPAD Values Based on Spectral Characteristics

^{2}= 0.60, RMSE = 4.495, RE = 7.35%) among all models. The SLR model based on the CA-R had the worst fit (training set, R

^{2}= 0.401, RMSE = 5.549, RE = 9.17%) due to the underfitting of the data (Table 1). The models based on Full-R were better than those based on Full-FD (Table 1, Figure 5E,F); although, the models built with CA-FD and SPA-FD provided better fits than CA-R and SPA-R (Figure 5A–D). The models built with SPA-R, SPA-FD, PCA-R, and PCA-FD were superior to the SLR models but inferior to the Full-R and Full-FD models (Table 1). The LASSO and RR models based on SPA-R, SPA-FD, PCA-R, and PCA-FD exhibited underfitting (Table 1).

^{2}> 0.5; Figure 7). The LASSO model prediction performance of SPAD values under drought stress is the best with R

^{2}0.569, RMSE 5.159, and RE 9.28%. As a result, the hyperspectral reflectance in predicting the SPAD values of the wheat leaves under drought stress can provide a robust result and has the potential to be used in drought resistance identification in the future.

#### 2.6.2. Estimation of SPAD Values Based on Spectral and Image Characteristics

^{2}= 0.61, RMSE = 4.439, RE = 7.35%). Overall, the combination of spectral characteristics and L*a*b* features improve the estimation accuracy for SPAD values (Table 3).

## 3. Discussion

#### 3.1. Feasibility of Estimating Chlorophyll Content of Wheat Leaves Using Hyperspectral Information

#### 3.2. Models for Estimating SPAD Values in Wheat Leaves

^{2}= 0.854, root mean square error of prediction 625.7) had the best stability [55]. In the present study, the LASSO, RR, and RFR models based on Full-R were superior to all other models and had R

^{2}and RMSE values of 0.585 and 4.578, 0.585 and 4.575, and 0.60 and 4.495, respectively (Figure 6A–C, Table 1). These findings demonstrate that models based on Full-R had better stability and accuracy.

^{2}= 0.625, RMSE = 0.048) [56]. A study estimating maize yield found that full-band RR (R

^{2}= 0.54, RMSE = 2.58) and support vector regression (SVR; R

^{2}= 0.53, RMSE = 2.69) models performed better than a full-band first derivative model (R

^{2}= 0.41, RMSE = 3.51 and R

^{2}= 0.49, RMSE = 2.95, respectively); however, RFR models showed the opposite result [57].

#### 3.3. Utility of Hyperspectral Reflectance for Monitoring Wheat Growth and Evaluating Drought Resistance under Drought Stress

^{2}= 0.94, RMSE = 0.201) [49]. Similarly, previous research showed that 617, 675, and 818 nm are the optimal bands for estimating the chlorophyll content of diseased peach fruit [58]. Using the continuous wavelet transform (CWT) to estimate wheat SPAD values under low-temperature stress, a previous study found that 553, 727, 728, 729, and 734 nm are SPAD-sensitive bands, and that spectral reflectance at 553 nm can accurately estimate SPAD values (R

^{2}= 0.7444, RMSE = 7.359) [59]. One study using multiple feature selection methods to determine SPAD values in pepper leaves reported that the characteristic bands were concentrated within the regions of 415.4–431.5, 526.7–676.2, and 839.3–979.2 nm [60]. Furthermore, 548, 718, and 727 nm were the best wave bands for estimating chlorophyll content in grafted cucumber seedling leaves [61]. Taken together, these studies clearly indicate that hyperspectral information is closely related to chlorophyll content; however, it was still necessary to establish a stable, reliable, and universally applicable model for evaluating diverse species, and for within-species analyses, under different growth conditions. In the present study, full-band hyperspectral reflectance of combined L*, a*, and b* accurately estimated SPAD values (R

^{2}= 0.61, RMSE = 4.439, RE = 7.35%; Table 3). The spectral reflectance bands at 536, 549, 596, 674, and 708 nm, and the first derivative bands at 735, 756, and 778 nm, were SPAD characteristic bands (Figure 3A,B and Figure 4A,B). Nonetheless, many bands within the 446–770 nm region were closely related to drought stress (Figure 4E,F). Using SLR based on 549 nm reflectance and 735 nm first derivative to assess SPAD values in the leaf level found that the HSI can monitor leaf chlorophyll content and drought stress reliably (Figure 8). In conclusion, plant HSI has great potential for evaluating leaf chlorophyll content under stress conditions; however, studies including large-scale varieties experiencing different stress conditions are necessary to establish stable and reliable models.

## 4. Materials and Methods

#### 4.1. Plant Material and Growth Conditions

_{2}O5-K

_{2}O) at 750 kg/hm

^{2}prior to sowing. The soil drilling method was used to measure soil water content at a depth of 0.5 m. Beginning at the jointing stage of wheat growth, the relative soil water contents of the control and drought stress treatments were maintained at 75 ± 5% and 50 ± 5%, respectively. The calculation method of relative soil water content is consistent with previous research [64], and uses the formula below:

#### 4.2. Hyperspectral Image Acquisition

#### 4.3. SPAD Values Measurement

#### 4.4. Hyperspectral Image Preprocessing

#### 4.5. Data Processing

^{2}), root mean square error (RMSE), and relative error (RE). Four regression models were tested: simple linear regression (SLR) [71], least absolute shrinkage and selection operator (LASSO) regression [72], ridge regression (RR) [73], and random forest regression (RFR) models [74].

## 5. Conclusions

^{2}= 0.61, RMSE = 4.439, RE = 7.35%). The technical method established in this study has great potential for evaluating chlorophyll content and stress resistance.

## Supplementary Materials

## Author Contributions

## Funding

## Institutional Review Board Statement

## Informed Consent Statement

## Data Availability Statement

## Conflicts of Interest

## References

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**Figure 1.**SPAD values of 335 varieties of wheat leaves under different soil moisture conditions. CK, control; DS, drought stress; whisker, 5–95%; ns, no statistically significant difference in SPAD values between the CK and DS treatments (p > 0.05).

**Figure 2.**Hyperspectral curves and single-band hyperspectral images of wheat leaves under different soil moisture conditions. Hyperspectral reflectance and first derivative curves of leaves from 335 wheat varieties under control (CK) and drought stress (DS) conditions; (

**A**,

**B**) hyperspectral reflectance curves of leaves and first derivative values; (

**C**,

**D**) mean hyperspectral reflectance curves of leaves and first derivative values; (

**E**) single-band hyperspectral images; hyperspectral band region (400–1000 nm); hyperspectral images obtained using the Pika L hyperspectral imaging system; RGB images (red = 640 nm, green = 550 nm, blue = 460 nm).

**Figure 3.**Correlation analysis and fitting results between leaf hyperspectral and SPAD values. (

**A**,

**B**) Correlations of spectral reflectance and the first derivative with SPAD values; (

**C**,

**D**) simple linear regression (SLR) analysis based on spectral reflectance at 549 nm and the spectral first derivative at 735 nm; (

**E**,

**F**) fitting results of predicted and measured values of SPAD based on reflectance at 549 nm and the first derivative at 735 nm. The gray dotted line is the 1:1 fit line between the predicted and measured values. Pearson correlation analysis of hyperspectral reflectance and first derivatives with SPAD values in CK, DS, and combined data sets was performed using Prism 9. Python 3.6 was used for SLR analysis of the data. CK, control; DS, drought stress; combined, all data.

**Figure 4.**SPA feature band selection and PCA dimension reduction. (

**A**,

**B**) Spectral reflectance and first derivative bands extracted by SPA; (

**C**,

**D**) 3D spatial distribution of the first three principal components of spectral reflectance and the first derivative; (

**E**,

**F**) the first three principal components’ loadings of spectral reflectance and the first derivative. Python 3.6 was used to extract SPAD characteristic bands. Prism 9 was used to calculate PCA loadings. The percentage is the proportion of the variance explained by each principal component. SPA, successive projections algorithm; PCA, principal component analysis. Obj, first calibration object; Var, selected variables. CK, control; DS, drought stress.

**Figure 5.**Comparison of predicted and measured values SPAD based on different data sets and models. (

**A**,

**B**) Simple linear regression (SLR) model based on CA-R and CA-FD; (

**C**,

**D**) random forest regression (RFR) model based on SPA-R and SPA-FD; (

**E**,

**F**) RFR model based on Full-R and Full-FD. CA-R/FD, reflectance/first derivative with the highest correlation with SPAD values; SPA-R/FD, reflectance/first derivative of a characteristic band extracted through SPA; Full-R/FD, full-band reflectance/first derivative. CK, control; DS, drought stress.

**Figure 6.**Fitting results of predicted and measured SPAD values of wheat leaves based on a full-band hyperspectral reflectance and first derivative model. (

**A**–

**C**) Least absolute shrinkage and selection operator (LASSO) regression, ridge regression (RR), and random forest regression (RFR) models built with full-band hyperspectral reflectance; (

**D**–

**F**) LASSO regression, RR, and RFR models built with full-band hyperspectral first derivative. The gray dotted line is the 1:1 fit between the predicted and measured SPAD values, and the blue solid line is the actual fit between predicted and measured values.

**Figure 7.**Fitting results of predicted and measured SPAD values of wheat leaves based on a full-band hyperspectral reflectance model under different soil moisture conditions. (

**A**–

**C**) Least absolute shrinkage and selection operator (LASSO) regression, ridge regression (RR), and random forest regression (RFR) models under the control condition; (

**D**–

**F**) LASSO regression, RR, and RFR models under the drought stress condition. The blue and yellow solid lines are the actual fit between predicted and measured values, and the gray dotted line is the 1:1 fit between the predicted and measured SPAD values. CK, control; DS, drought stress.

**Figure 8.**SPAD values map at the leaf level estimated by the characteristic reflectance and first derivative. (

**A**,

**B**) SPAD values map at the leaf level estimated by the 549 nm reflectance and 735 nm first derivative. CK, control; DS, drought stress. ENVI 5.3 (ITT, Visual Information Solutions, Boulder, CO, USA) was used to obtain the SPAD values map.

Data Set | Model | Training Set | (n = 536) | Testing Set | (n = 134) | ||
---|---|---|---|---|---|---|---|

R^{2} | RMSE | RE | R^{2} | RMSE | RE | ||

CA-R (549 nm) | SLR | 0.401 | 5.549 | 9.17% | 0.424 | 5.390 | 8.81% |

CA-FD (735 nm) | SLR | 0.426 | 5.434 | 9.01% | 0.529 | 4.876 | 8.27% |

SPA-R | LASSO | 0.405 | 5.532 | 9.12% | 0.417 | 5.430 | 8.86% |

RR | 0.405 | 5.532 | 9.12% | 0.417 | 5.426 | 8.85% | |

RFR | 0.478 | 5.183 | 8.60% | 0.478 | 5.134 | 8.40% | |

SPA-FD | LASSO | 0.407 | 5.523 | 9.25% | 0.518 | 4.934 | 8.49% |

RR | 0.407 | 5.523 | 9.26% | 0.517 | 4.939 | 8.51% | |

RFR | 0.510 | 5.023 | 8.57% | 0.510 | 4.974 | 8.47% | |

PCA-R | LASSO | 0.484 | 5.150 | 8.39% | 0.571 | 4.655 | 7.64% |

RR | 0.488 | 5.130 | 8.20% | 0.580 | 4.607 | 7.47% | |

RFR | 0.555 | 4.788 | 7.87% | 0.555 | 4.739 | 7.89% | |

PCA-FD | LASSO | 0.454 | 5.298 | 8.62% | 0.496 | 5.045 | 7.90% |

RR | 0.454 | 5.298 | 8.64% | 0.497 | 5.039 | 7.99% | |

RFR | 0.560 | 4.755 | 7.74% | 0.560 | 4.714 | 7.62% | |

Full-R | LASSO | 0.587 | 4.609 | 7.43% | 0.585 | 4.578 | 7.51% |

RR | 0.586 | 4.617 | 7.45% | 0.585 | 4.575 | 7.46% | |

RFR | 0.600 | 4.535 | 7.40% | 0.600 | 4.495 | 7.35% | |

Full-FD | LASSO | 0.528 | 4.929 | 8.12% | 0.528 | 4.880 | 7.96% |

RR | 0.548 | 4.824 | 7.81% | 0.547 | 4.784 | 7.71% | |

RFR | 0.579 | 4.653 | 7.63% | 0.577 | 4.623 | 7.64% |

^{2}, coefficient of determination; RMSE, root mean square error; RE, relative error (percentage); SLR, simple linear regression; LASSO, least absolute shrinkage and selection operator; RR, ridge regression; RFR, random forest regression. CA-R/FD, reflectance/first derivative with the highest correlation with SPAD values; SPA-R/FD, reflectance/first derivative of a characteristic band extracted through SPA; PCA-R/FD, principal components of reflectance/first derivative; Full-R/FD, full-band reflectance/first derivative; SPA, successive projections algorithm; PCA, principal component analysis.

Variable | SPAD | L* | a* | b* |
---|---|---|---|---|

SPAD | 1 | |||

L* | −0.591 ** | |||

a* | −0.164 ** | 0.438 ** | ||

b* | −0.600 ** | 0.912 ** | 0.378 ** | 1 |

Data Set | Training Set (n = 536) | Testing Set (n = 134) | ||||
---|---|---|---|---|---|---|

R^{2} | RMSE | RE | R^{2} | RMSE | RE | |

(CA-R) + L*a*b* | 0.486 | 5.14 | 8.52% | 0.486 | 5.095 | 8.23% |

(CA-FD) + L*a*b* | 0.519 | 4.973 | 8.19% | 0.519 | 4.925 | 8.18% |

(SPA-R) + L*a*b* | 0.502 | 5.061 | 8.38% | 0.501 | 5.021 | 8.27% |

(SPA-FD) + L*a*b* | 0.51 | 5.019 | 8.37% | 0.51 | 4.972 | 8.13% |

(PCA-R) + L*a*b* | 0.46 | 5.271 | 8.76% | 0.46 | 5.218 | 8.29% |

(PCA-FD) + L*a*b* | 0.584 | 4.626 | 7.47% | 0.584 | 4.579 | 7.48% |

(Full-R) + L*a*b* | 0.61 | 4.478 | 7.30% | 0.61 | 4.439 | 7.35% |

(Full-FD) + L*a*b* | 0.578 | 4.661 | 7.64% | 0.578 | 4.617 | 7.58% |

L*a*b* | 0.435 | 5.39 | 9.06% | 0.434 | 5.346 | 8.38% |

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## Share and Cite

**MDPI and ACS Style**

Yang, Y.; Nan, R.; Mi, T.; Song, Y.; Shi, F.; Liu, X.; Wang, Y.; Sun, F.; Xi, Y.; Zhang, C. Rapid and Nondestructive Evaluation of Wheat Chlorophyll under Drought Stress Using Hyperspectral Imaging. *Int. J. Mol. Sci.* **2023**, *24*, 5825.
https://doi.org/10.3390/ijms24065825

**AMA Style**

Yang Y, Nan R, Mi T, Song Y, Shi F, Liu X, Wang Y, Sun F, Xi Y, Zhang C. Rapid and Nondestructive Evaluation of Wheat Chlorophyll under Drought Stress Using Hyperspectral Imaging. *International Journal of Molecular Sciences*. 2023; 24(6):5825.
https://doi.org/10.3390/ijms24065825

**Chicago/Turabian Style**

Yang, Yucun, Rui Nan, Tongxi Mi, Yingxin Song, Fanghui Shi, Xinran Liu, Yunqi Wang, Fengli Sun, Yajun Xi, and Chao Zhang. 2023. "Rapid and Nondestructive Evaluation of Wheat Chlorophyll under Drought Stress Using Hyperspectral Imaging" *International Journal of Molecular Sciences* 24, no. 6: 5825.
https://doi.org/10.3390/ijms24065825