# Identification of the Yield of Camellia oleifera Based on Color Space by the Optimized Mean Shift Clustering Algorithm Using Terrestrial Laser Scanning

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## Abstract

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## 1. Introduction

## 2. Materials and Methods

#### 2.1. Study Area

#### 2.2. Framework of This Research

#### 2.3. Point Cloud Acquisition and Processing

#### 2.4. Measurement of Field Data

#### 2.5. Identification of Oil Tea Fruits Point Clouds

#### 2.6. Clustering Method for Oil Tea Fruits

_{radius}(x) is the set of y points; x is the spherical coordinate matrix; and y is the coordinate matrix of a single point in the point cloud. radius was set to 2 cm, as determined by the radius of the oil tea fruit measured in the study area.

_{a}, y

_{a}, z

_{a}) is the 3D spatial coordinate of point a and (x

_{b}, y

_{b}, z

_{b}) is the coordinate of point b.

_{radius}(Ck) in the class cluster Ck, and (x

_{k}, y

_{k}, z

_{k}) is the 3D spatial coordinate of the kth point.

_{Min}is the minimum number of points in the neighborhood of point P with a radius of r. The distances between point P and the remaining points were calculated, where p is the coordinate matrix of P and p

_{i}is the coordinate matrix of the ith point, and then the number of points within the radius r of point P was counted (Equation (6)). In Equation (7), N

_{r}(P) includes all points with a distance of no more than r from point P. If the number of points included in N

_{r}(P) is not less than P

_{Min}, then P is set as the core point, and then the sample set that can reach the density of all core objects is identified as a cluster.

_{1}is randomly selected in the sample point cloud data, and the second cluster center X

_{2}is defined as the point farthest from X

_{1}. The d

_{N}is the set consisting of the distances from each cluster center to all sample points in the algorithm (Equation (8)), where d

_{Nn}is the distance from the Nth cluster center X

_{N}to the nth sample point. Equation (9) is used to determine whether there is a next cluster center, where $\mathrm{Min}\left({d}_{N}\right)$ is a distance subset of d

_{N}with the smallest sum of elements in that distance subset, m is the test parameter, $\left|{\mathrm{X}}_{N}-{\mathrm{X}}_{N-1}\right|$ is the distance from X

_{N}to X

_{N−}

_{1}, and $\mathrm{Max}\left\{\mathrm{Min}\right({d}_{N}\left)\right\}$ is the maximum value in the set of $\mathrm{Min}\left({d}_{N}\right)$. If Equation (9) holds, then the sample point corresponding to $\mathrm{Max}\left\{\mathrm{Min}\right({d}_{N}\left)\right\}$ is the (N + 1)th cluster center, otherwise it is considered that there is no new cluster center. After determining all the cluster centers, the distances between each point and the cluster center are calculated, and the points are grouped into the class cluster corresponding to the nearest cluster center (Equation (10)), where $\mathrm{Y}j$ is the jth point in the point cloud dataset D, X

_{i}is the ith cluster center, and $yj$ and $xi$ are the coordinate matrices of Y

_{j}and X

_{i}, respectively.

#### 2.7. Algorithm Accuracy Assessment

_{i}is the number of oil tea fruits detected; NP

_{i}is the total number of oil tea fruits of a single plant; and P

_{i}is the detection rate of oil tea fruits on a single oil tea tree. The larger $\overline{P}$ is, the better the clustering recognition effect of the clustering algorithm. The smaller D is, the higher the stability of the clustering algorithm.

## 3. Results

#### 3.1. Point Cloud Separation Results

#### 3.2. Clustering Analysis

## 4. Discussion

#### 4.1. Point Cloud Acquisition

#### 4.2. Clustering Algorithm for Oil Tea Fruit Identification

#### 4.3. Uncertainty, Limitations, and Prospects

## 5. Conclusions

## Author Contributions

## Funding

## Institutional Review Board Statement

## Informed Consent Statement

## Data Availability Statement

## Conflicts of Interest

## References

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**Figure 3.**Schematic diagram of the oil tea tree and fruits’ point cloud acquisition. (

**a**) Scanning stations for point cloud acquisition, (

**b**) point cloud data of the oil tea tree and fruits, and (

**c**) photograph of the oil tea tree and fruits.

**Figure 4.**Separation result for oil tea fruit in (

**a**) the RGB color space and (

**b**) the YUV color space.

**Figure 5.**Comparison of the RGB and YUV attributes extracted from the point clouds of oil tea fruit.

**Figure 6.**The fruit number results based on the RGB color space clustered by the (

**a**) improved mean shift, (

**b**) common mean shift, (

**c**) maximum–minimum distance, and (

**d**) DBSCAN clustering methods.

**Figure 8.**The fruit number results based on the YUV color space clustered by the (

**a**) improved mean shift, (

**b**) common mean shift, (

**c**) maximum–minimum distance, and (

**d**) DBSCAN clustering methods.

Total Number of Oil Tea Fruits | Mean | Maximum | Minimum | Average Radius of Oil Tea Fruits (cm) |
---|---|---|---|---|

19,066 | 241 | 593 | 38 | 2.0 |

Color Space | Attribute Characteristics | Maximum Value | Minimum Value |
---|---|---|---|

RGB | R | 255 | 200 |

G | 250 | ||

B | 250 | ||

YUV | Y | 160 | |

U | 220 | ||

V | 220 |

Color Space | Clustering Method | Highest Detection Rate/% | Minimum Detection Rate/% | $\overline{\mathit{P}}$ | Number of Detected Fruits | D |
---|---|---|---|---|---|---|

RGB | Improved Mean Shift | 96.56 | 20.27 | 79.33 | 14,929 | 0.0127 |

Mean Shift | 89.19 | 17.37 | 70.29 | 13,039 | 0.0150 | |

DBSCAN | 51.47 | 6.24 | 24.80 | 3855 | 0.0124 | |

Maximum–Minimum Distance | 98.48 | 19.31 | 75.42 | 13,992 | 0.0173 | |

YUV | Improved Mean Shift | 98.13 | 30.22 | 81.73 | 15,210 | 0.0134 |

Mean Shift | 93.46 | 26.79 | 74.68 | 13,786 | 0.0127 | |

DBSCAN | 67.39 | 1.32 | 19.38 | 2665 | 0.0178 | |

Maximum–Minimum Distance | 93.18 | 26.48 | 70.95 | 12,893 | 0.0151 |

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**MDPI and ACS Style**

Tang, J.; Jiang, F.; Long, Y.; Fu, L.; Sun, H.
Identification of the Yield of *Camellia oleifera* Based on Color Space by the Optimized Mean Shift Clustering Algorithm Using Terrestrial Laser Scanning. *Remote Sens.* **2022**, *14*, 642.
https://doi.org/10.3390/rs14030642

**AMA Style**

Tang J, Jiang F, Long Y, Fu L, Sun H.
Identification of the Yield of *Camellia oleifera* Based on Color Space by the Optimized Mean Shift Clustering Algorithm Using Terrestrial Laser Scanning. *Remote Sensing*. 2022; 14(3):642.
https://doi.org/10.3390/rs14030642

**Chicago/Turabian Style**

Tang, Jie, Fugen Jiang, Yi Long, Liyong Fu, and Hua Sun.
2022. "Identification of the Yield of *Camellia oleifera* Based on Color Space by the Optimized Mean Shift Clustering Algorithm Using Terrestrial Laser Scanning" *Remote Sensing* 14, no. 3: 642.
https://doi.org/10.3390/rs14030642