# Multiscale Inversion of Leaf Area Index in Citrus Tree by Merging UAV LiDAR with Multispectral Remote Sensing Data

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## Abstract

**:**

^{2}, RMSE, and MAE of the BP neural network at the community scale and individual scale were 0.896, 0.112, 0.086, and 0.794, 0.408, 0.328, respectively. By adding the three-dimensional gap fraction feature to the two-dimensional vegetation index features, the R

^{2}at community scale and individual scale increased by 4.43% and 7.29%, respectively. The conclusion of this study suggests that the fusion of point cloud and multispectral data exhibits superior accuracy in multiscale citrus LAI inversion compared to relying solely on a single data source. This study proposes a fast and efficient multiscale LAI inversion method for citrus, which provides a new idea for the orchard precise management and the precision of plant protection operation.

## 1. Introduction

^{2}is 6 m

^{2}, the LAI of the plant on this land is 3. The crop yield is positively correlated with the LAI within a certain threshold. However, when the LAI exceeds a certain threshold, insufficient light penetration leads to decreased photosynthetic efficiency and ultimately reduces the crop yield. Additionally, LAI can be used to regulate the amount of pesticide spraying per unit land area, which is crucial for precise pest control and cost reduction [6,7,8]. Furthermore, the LAI serves as an important phenotype for citrus trees, facilitating yield prediction and evaluating their health status. Conventional remote sensing techniques relying on single vegetation indices exhibit limited accuracy due to saturation issues. Research has indicated that estimation methods based on multiple vegetation indices provide higher accuracy in inverting LAI [9].

## 2. Materials and Methods

#### 2.1. Research Area and Technical Route

#### 2.1.1. Research Area

#### 2.1.2. Technical Route

#### 2.2. Data Acquisition

#### 2.2.1. Ground-Truth LAI Acquisition

#### 2.2.2. Remote Sensing Data Collection

#### 2.3. Remote Sensing Data Preprocessing

#### 2.3.1. Multispectral Data Preprocessing

#### Vegetation Index

#### Removing Soil Background

#### 2.3.2. LiDAR Data Preprocessing

#### Gap Fraction

#### Height Normalization

#### 2.4. Extract Features

#### 2.4.1. Community Scale

#### Vegetation Indices in Each ROI

#### Gap Fraction in Each ROI

#### Pearson Correlation Coefficient and Scatter Plot

#### 2.4.2. Individual Tree Scale

#### Instance Segmentation

#### Extracting Boundary of Individual Tree

#### Pearson Correlation Coefficient and Scatter Plot

#### 2.5. Regression Model

#### 2.5.1. BP Neural Network Model

#### 2.5.2. Other Models

## 3. Results

#### 3.1. Model Comparison

^{2}), mean absolute error (MAE), and root-mean-square error (RMSE). We compared the R

^{2}, MAE, and RMSE of each model. The scatter plots between the predicted values and the true values at the community and individual scales are shown in Figure 15. Python 3.9.7 was used to complete the modeling and results evaluation of this experiment.

#### 3.2. Remove Redundant Features

^{2}of 0.888 at the community scale and 0.791 at the individual scale. In order to remove redundant features, the one-by-one removal method was used for all input features of the BP neural network model to conduct redundant analysis. After each feature was removed, R

^{2}was counted (Table 6). If R

^{2}increased or remained unchanged after removing a certain feature, then that feature was considered as a redundant feature.

#### 3.3. The Effect of Gap Fraction on Results

^{2}) exhibited an improvement from 0.858 to 0.896, corresponding to a 4.43% increase at the community scale. Additionally, R

^{2}increased from 0.740 to 0.794, representing a 7.29% increase at the individual scale. These findings indicate that the fusion approach combining two-dimensional multispectral data with three-dimensional spatial information is superior to conventional two-dimensional multispectral methods.

#### 3.4. Model Application

## 4. Discussion

## 5. Conclusions

- (1)
- The R
^{2}values of the six models at both the community scale and individual scale, before removing redundant features, were as follows: 0.808 (SVR), 0.841 (GBR), 0.859 (LR), 0.859 (RF), 0.859 (Bayesian), and 0.888 (BP) for the community scale; and 0.681 (SVR), 0.680 (GBR), 0.738 (LR), 0.689 (RF), 0.748 (Bayesian), and 0.791 (BP) for the individual scale. The BP neural network demonstrated the best performance among the models at both scales. - (2)
- The R
^{2}values of the BP neural network model, after removing redundant features, were found to be 0.896 at the community scale and 0.794 at the individual scale. It was observed that the model achieved higher accuracy at the community scale compared to the individual scale. - (3)
- By integrating LiDAR data with multispectral data, we observed a substantial improvement in the R
^{2}values. Specifically, at the community scale, there was a notable increase of 4.43%, while at the individual scale, the improvement reached an impressive 7.29%. These results strongly suggest that the fusion approach, which combines the two-dimensional multispectral information with the three-dimensional spatial information, outperforms the conventional two-dimensional multispectral methods.

## Author Contributions

## Funding

## Data Availability Statement

## Conflicts of Interest

## References

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**Figure 2.**Schematic diagram of the distribution of the sampling points. (

**a**) Individual scale; (

**b**) community scale.

**Figure 4.**(

**a**) CI–110 plant canopy analyzer; (

**b**) set azimuth divisions; (

**c**) result of LAI; (

**d**) Using Ice River 660RTK to record the longitude and latitude coordinates.

**Figure 5.**Ground-truth LAI. (

**a**) Sampling point distribution at community scale; (

**b**) average LAI of 3 points in each community; (

**c**) average LAI of 4 points in each tree (individual tree scale).

**Figure 6.**Equipment for collecting remote sensing data. (

**a**) DJI Phantom 4 with multispectral camera; (

**b**) multispectral grey-plate calibration; (

**c**) DJI M300 with ZENMUSE LI.

**Figure 11.**Instance segmentation results. (

**a**) Watershed algorithm results; (

**b**) manual secondary segmentation results; (

**c**) hexagonal tool.

**Figure 15.**Ten-fold cross-validation results at community scale. (

**a**) Community scale; (

**b**) individual scale.

**Figure 16.**Ten-fold cross-validation results after removing redundant features. (

**a**) Community scale; (

**b**) individual tree scale.

**Figure 18.**Ten-fold cross-validation results after removing gap fraction. (

**a**) Community scale; (

**b**) individual tree scale.

Scale | Number of Samples | Max | Min | Mean | Standard Deviation |
---|---|---|---|---|---|

Community | 220 | 1.963 | 0.160 | 0.927 | 0.348 |

Individual | 341 | 5.00 | 1.080 | 2.372 | 0.901 |

Parameter Name | Value |
---|---|

CMOS pixel | 1600 × 1300 |

Flight altitude | 35 m |

Course overlap | 75% |

Lateral overlap | 75% |

Speed of flight | 3 m/s |

Photography mode | Equidistant photography |

Pan tilt angle | −90° |

Band | Blue (450 nm ± 16 nm) Green (560 nm ± 16 nm) Red (650 nm ± 16 nm) Red edge (730 nm ± 16 nm) Near infrared (840 nm ± 26 nm) |

Parameter Name | Value |
---|---|

Point cloud density | 4041 points/m^{2} |

Flight altitude | 35 m |

Scan mode | Repeat scan |

Lateral overlap | 20% |

Speed of flight | 1 m/s |

Vegetation Index | Full Name | Formula | Reference |
---|---|---|---|

GNDVI | Green Normalized Difference Vegetation Index | $\frac{NIR-G}{NIR+G}$ | [39] |

NDVI | Normalized Difference Vegetation Index | $\frac{NIR-R}{NIR+R}$ | [40] |

NDRE | Normalized Difference Red Edge | $\frac{NIR-REG}{NIR+REG}$ | [41] |

OSAVI | Optimized Soil Adjusted Vegetation Index | $\frac{NIR-R}{NIR+R+0.16}$ | [42] |

RVI | Ratio Vegetation Index | $\frac{NIR}{R}$ | [43] |

GDVI | Green Difference Vegetation Index | $NIR-G$ | [44] |

DVI | Difference Vegetation Index | $NIR-R$ | [45] |

GCI | Green Chlorophyll Index | $\frac{NIR}{G}$ − 1 | [46] |

RNDVI | Red Normalized Difference Vegetation Index | $\frac{REG-R}{REG+R}$ | [47] |

VDVI | Visible Difference Vegetation Index | $\frac{2G-R-B}{2G+R+B}$ | [48] |

RRI | Red Ratio Index | $\frac{NIR}{REG}$ | [49] |

${\mathit{N}}_{\mathit{c}}$ | ${\mathit{N}}_{\mathit{l}}$ | ${\mathit{N}}_{\mathit{o}}$ | $\mathit{r}$ | $\mathit{p}$ | $\mathit{F}$ |
---|---|---|---|---|---|

921 | 330 | 468 | 0.736 | 0.663 | 0.698 |

Feature Removed | R^{2} (Community Scale) | R^{2} (Individual Tree Scale) | Whether Redundant (Community Scale, Individual Scale) |
---|---|---|---|

Gap fraction | 0.855 | 0.738 | ×, × |

DVI | 0.890 | 0.780 | √, × |

GCI | 0.886 | 0.765 | ×, × |

GDVI | 0.883 | 0.764 | ×, × |

GNDVI | 0.877 | 0.757 | ×, × |

NDRE | 0.889 | 0.785 | √, × |

NDVI | 0.872 | 0.770 | ×, × |

OSAVI | 0.885 | 0.794 | ×, √ |

GREEN | 0.879 | 0.789 | ×, × |

NIR | 0.889 | 0.788 | √, × |

RED | 0.892 | 0.784 | √, × |

REDEDGE | 0.882 | 0.751 | ×, × |

RNDVI | 0.887 | 0.778 | ×, × |

RRI | 0.882 | 0.789 | ×, × |

RVI | 0.882 | 0.758 | ×, × |

VDVI | 0.879 | 0.784 | ×, × |

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**MDPI and ACS Style**

Xu, W.; Yang, F.; Ma, G.; Wu, J.; Wu, J.; Lan, Y.
Multiscale Inversion of Leaf Area Index in Citrus Tree by Merging UAV LiDAR with Multispectral Remote Sensing Data. *Agronomy* **2023**, *13*, 2747.
https://doi.org/10.3390/agronomy13112747

**AMA Style**

Xu W, Yang F, Ma G, Wu J, Wu J, Lan Y.
Multiscale Inversion of Leaf Area Index in Citrus Tree by Merging UAV LiDAR with Multispectral Remote Sensing Data. *Agronomy*. 2023; 13(11):2747.
https://doi.org/10.3390/agronomy13112747

**Chicago/Turabian Style**

Xu, Weicheng, Feifan Yang, Guangchao Ma, Jinhao Wu, Jiapei Wu, and Yubin Lan.
2023. "Multiscale Inversion of Leaf Area Index in Citrus Tree by Merging UAV LiDAR with Multispectral Remote Sensing Data" *Agronomy* 13, no. 11: 2747.
https://doi.org/10.3390/agronomy13112747