# Remote Sensing Monitoring of Winter Wheat Stripe Rust Based on mRMR-XGBoost Algorithm

^{1}

^{2}

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## Abstract

**:**

^{2}= 0.8894, RMSE = 0.1135). The R

^{2}between the measured DI and predicted DI of mRMR-XGBoost was improved by an average of 5%, 12%, and 22% compared with mRMR-GBRT, CC-XGBoost, and CC-GBRT models. These results suggested that XGBoost is more suitable for the remote sensing monitoring of wheat stripe rust, and mRMR has more advantages than the commonly used CC analysis in feature selection. Field survey data validation results also confirm that the mRMR-XGBoost algorithm has excellent monitoring applicability and scalability. The proposed model could provide a reference for data dimensionality reduction and crop disease index monitoring based on hyperspectral data.

## 1. Introduction

## 2. Materials and Methods

#### 2.1. Field Experimental Data Acquirement

#### 2.2. Extraction of Canopy SIF Parameters

_{left}and ω

_{right}represent the weight of the left and right bands. L

_{in}, L

_{left,}and L

_{right}represent the canopy reflectance radiance inside, left, and right of the absorption band. I

_{in}, I

_{left,}and I

_{right}represent the solar irradiance inside, left, and right of the absorption band. In addition to calculating the canopy SIF directly by radiance, the reflectance band at 650–800 nm, which is greatly affected by chlorophyll fluorescence, can be used to obtain a reflectance index that can reflect the intensity of fluorescence as well. Therefore, this study also elects the reflectance ratio index [19] and the reflectance first derivative index [20] as the fluorescence feature input of the model. The definitions of selected canopy SIF parameters are listed in Table 1.

#### 2.3. Calculation of Hyperspectral Vegetation Indices

#### 2.4. Extraction of Characteristic Band

#### 2.5. Methods

#### 2.5.1. The Max-Relevance and Min-Redundancy Feature Selection

_{f}can be found from the initial feature set X. That is, the features in the subset all have high relevance with DI.

_{f}and X

_{l}are features in subset S. The combination of Equations (5) and (6) is the mutual information quotient (MIQ) criteria, the selected features have maximal relevance with the disease index and minimal redundancy with each other.

#### 2.5.2. Extreme Gradient Boosting Regression

_{i}, y

_{i}), where $\left|\mathrm{D}\right|=n$, ${x}_{i}\in {R}^{m}$, ${y}_{i}\in R$. The mathematical model of the XGBoost algorithm can be regarded as an additive model composed of t regression trees. The predicted value of the model can be calculated by the following formula.

_{k}is the function represented by the kth independent tree, and f

_{k}(x

_{i}) is the space of the CART regression tree. The objective function of the XGBoost algorithm can be constructed as the Equation (9).

_{j}represents the set of samples on the leaf node whose sequence number is j. Find the optimal solution to equation (8) and bring it back to the equation to obtain the minimized objective function of the XGBoost model.

## 3. Results and Analysis

#### 3.1. Features Selected by CC

#### 3.2. Features Selected by mRMR

#### 3.3. Remote Sensing Monitoring Model of Wheat Stripe Rust

^{2}) and Root Mean Square Error (RMSE) between the predicted value and the measured value are selected as the model accuracy evaluation indicators.

#### 3.4. Model Evaluation

^{2}between the predicted DI and the measured DI is increased by an average of 5%, 12%, and 22%, and the RMSE is reduced by an average of 14%, 33%, and 52%.

#### 3.5. Field Survey Data Validation

^{2}between the predicted DI and the measured DI value in the mRMR-XGBoost model were improved by 44%, 32%, and 82% on average. It shows the highest monitoring accuracy among the four models in this study, which is consistent with the above result, indicating that the mRMR-XGBoost algorithm has excellent monitoring universality and scalability.

## 4. Discussion

^{2}between predicted DI and measured DI, and the accuracy of the XGBoost and GBRT models are listed in Table 5. When the number of features is less than 6, both models showed low accuracy. After exceeding 6, the accuracies of the two models continued to improve and stabilize as the feature increased. But they are still lower than the mRMR-XGBoost model. The reason may be that the features selected by the XGBoost algorithm were overfitted in train samples, so after transferred them into test samples, the model accuracy decreased.

## 5. Conclusions

^{2}and RMSE performance parameter values. The R

^{2}between prediction DI and measured DI of the three random groups in test samples are all above 0.87, and the RMSE is reduced by an average of 14%, 33%, and 52%. The field survey data validation experiment also confirmed the applicability of the mRMR-XGBoost algorithm. The high accuracy and regional accurate monitoring value justified the feasibility of using the mRMR-XGBoost model for monitoring wheat stripe rust, which is promising for this technology to be applied in practical wheat production management.

## Author Contributions

## Funding

## Institutional Review Board Statement

## Informed Consent Statement

## Data Availability Statement

## Acknowledgments

## Conflicts of Interest

## References

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**Figure 5.**Root Mean Square Error (RMSE) values versus critical parameter combinations in grid parameters optimal process. (

**a**) learning_rate with n_estimators; (

**b**) subsample with reg_alpha; (

**c**) min_child_weight with max_depth.

**Figure 6.**Accuracy comparison of validation set models based on mRMR-XGBoost, mRMR-GBRT, CC-XGBoost and CC-GBRT algorithms. (

**a**–

**d**), (

**e**–

**h**) and (

**i**–

**l**) represent the prediction results of the three groups, respectively.

Type | Definition | Type | Definition | Type | Definition |
---|---|---|---|---|---|

F_{relative} of O_{2}-A band | SIF-A | Reflectance ratio index | R_{740}/R_{720} | Reflectance ratio index | R_{685}/R_{655} |

F_{relative} of O_{2}-B band | SIF-B | R_{440}/R_{690} | R_{690}/R_{655} | ||

Reflectance first derivative index | D_{705}/D_{722} | R_{740}/R_{800} | R_{690}/R_{600} | ||

D_{730}/D_{706} | R_{750}/R_{800} | R_{675}*R_{690}/(R_{683})^{2} |

Type | Index | Definition | Reference |
---|---|---|---|

Vegetation index | Greenness index (GI) | R_{554}/R_{677} | [22] |

Photochemical reflectance index (PRI) | (R_{570} − R_{531})/(R_{570} + R_{531}) | [23] | |

Structural independent pigment index (SIPI) | (R_{800} − R_{445})/(R_{800} + R_{680}) | [24] | |

Plant senescence reflectance index (PSRI) | (R_{678} − R_{550})/R_{750} | [25] | |

Modified chlorophyll absorbtion reflectance index (MCARI) | [(R_{700} − R_{670}) − 0.2*(R_{700} − R_{550})]*(R_{700}/R_{670}) | [26] | |

Water index (WI) | R_{900}/R_{970} | [27] | |

Normalized difference water index (NDWI) | (R_{860} − R_{1240})/(R_{860} + R_{1240}) | [28] | |

Triangular vegetation index (TVI) | 0.5*[120*(R_{750} − R_{550}) − 200*(R_{670} − R_{550})] | [29] | |

Ration triangular vegetation index (RTVI) | [55*(R_{750} − R_{550}) − 90(R_{680} − R_{550})]/[90(R_{750} + R_{550})] | [30] | |

Healthy index (HI) | (R_{534} − R_{698})/(R_{534} + R_{698}) − 0.5 R_{704} | [3] | |

Trilateral Parameters | Db | The maximum value of the 1st order differential in 490–539 nm | [32] |

SDb | The sum of 1st order differential in 490–539 nm | [32] | |

Dy | The maximum value of the 1st order differential in 550–582 nm | [32] | |

SDy | The sum of 1st order differential in 550–582 nm | [32] | |

Dr | The maximum value of the 1st order differential in 670–737nm | [32] | |

SDr | The sum of 1st order differential in 670–737 nm | [32] |

Parameter Type | Parameter | Adjustment Range | Step | Optimal Value |
---|---|---|---|---|

learning_rate | [0, 1] | 0.01 | 0.21 | |

Booster | max_depth | [3, 10] | 1 | 3 |

parameters | min_split_weight | [1, 6] | 1 | 5 |

subsample | [0, 1] | 0.1 | 0.5 | |

reg_alpha | [0, 0.5] | 0.01 | 0.02 | |

Learning task parameters | n_estimators | [0, 800] | 1 | 11 |

Sample Group | mRMR-XGBoost | mRMR-GBRT | CC-XGBoost | CC-GBRT | ||||
---|---|---|---|---|---|---|---|---|

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

A | 0.915 | 0.181 | 0.721 | 0.157 | 0.890 | 0.161 | 0.769 | 0.166 |

B | 0.830 | 0.201 | 0.346 | 0.125 | 0.695 | 0.165 | 0.359 | 0.127 |

C | 0.676 | 0.131 | 0.608 | 0.119 | 0.245 | 0.137 | 0.200 | 0.162 |

**Table 5.**The effect of the features’ number selected based on the XGBoost algorithm on the accuracy.

Numbers | Feature Combination | R^{2} of XGBoost | R^{2} of GBRT |
---|---|---|---|

1 | GI | 0.12 | 0.16 |

2 | GI, SIF-A | 0.17 | 0.28 |

3 | GI, SIF-A, TVI | 0.25 | 0.18 |

4 | GI, SIF-A, TVI, PRI | 0.23 | 0.27 |

5 | GI, SIF-A, TVI, PRI, HI | 0.23 | 0.29 |

6 | GI, SIF-A, TVI, PRI, HI, SIPI | 0.64 | 0.62 |

7 | GI, SIF-A, TVI, PRI, HI, SIPI, SIF-B | 0.78 | 0.74 |

8 | GI, SIF-A, TVI, PRI, HI, SIPI, SIF-B, R_{740}/R_{800} | 0.83 | 0.87 |

9 | GI, SIF-A, TVI, PRI, HI, SIPI, SIF-B, R_{740}/R_{800}, R_{1086} | 0.83 | 0.84 |

10 | GI, SIF-A, TVI, PRI, HI, SIPI, SIF-B, R_{740}/R_{800}, R_{1086}, D_{b} | 0.84 | 0.88 |

11 | GI, SIF-A, TVI, PRI, HI, SIPI, SIF-B, R_{740}/R_{800}, R_{1086}, D_{b}, D_{y} | 0.81 | 0.81 |

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## Share and Cite

**MDPI and ACS Style**

Jing, X.; Zou, Q.; Yan, J.; Dong, Y.; Li, B.
Remote Sensing Monitoring of Winter Wheat Stripe Rust Based on mRMR-XGBoost Algorithm. *Remote Sens.* **2022**, *14*, 756.
https://doi.org/10.3390/rs14030756

**AMA Style**

Jing X, Zou Q, Yan J, Dong Y, Li B.
Remote Sensing Monitoring of Winter Wheat Stripe Rust Based on mRMR-XGBoost Algorithm. *Remote Sensing*. 2022; 14(3):756.
https://doi.org/10.3390/rs14030756

**Chicago/Turabian Style**

Jing, Xia, Qin Zou, Jumei Yan, Yingying Dong, and Bingyu Li.
2022. "Remote Sensing Monitoring of Winter Wheat Stripe Rust Based on mRMR-XGBoost Algorithm" *Remote Sensing* 14, no. 3: 756.
https://doi.org/10.3390/rs14030756