Study on the extraction method of sugar tangerine fruit trees based on UAV remote sensing images

QI Yuan; XU Weicheng; WANG Linlin; JIA Ruichang; LAN Yubin; ZHANG Yali

doi:10.7671/j.issn.1001-411X.202007032

Journal of South China Agricultural University > 2020 > 41(6): 126-133. > DOI: 10.7671/j.issn.1001-411X.202007032

QI Yuan, XU Weicheng, WANG Linlin, et al. Study on the extraction method of sugar tangerine fruit trees based on UAV remote sensing images[J]. Journal of South China Agricultural University, 2020, 41(6): 126-133. DOI: 10.7671/j.issn.1001-411X.202007032

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Study on the extraction method of sugar tangerine fruit trees based on UAV remote sensing images

QI Yuan^{1, 2,},
XU Weicheng^{2, 3},
WANG Linlin^{2, 3},
JIA Ruichang^{1, 2},
LAN Yubin^{2, 3, ,},
ZHANG Yali^{1, 2, ,}

1.
College of Engineering, South China Agricultural University, Guangzhou 510642, China
2.
National Center for International Collaboration Research on Precision Agricultural Aviation Pesticides Spraying Technology, Guangzhou 510642, China
3.
College of Electronic Engineering /College of Artificial Intelligence, South China Agricultural University, Guangzhou 510642, China

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Received Date: July 21, 2020
Available Online: May 17, 2023

Graphical Abstract

Abstract

Abstract

Objective
To obtain remote sensing image of sand sugar tangerine orchard by UAV, rapidly extract the distribution position of fruit trees, and provide references for growth monitoring and yield prediction of fruit trees.

Method
The visible light remote sensing images taken by drones were used as the research object. Six visible light vegetation indexes of excess red index, excess green index, excess blue index, visible band differential vegetation index, red-green ratio index and blue-green ratio index were calculated. We used the double peak threshold method to select the threshold for fruit tree extraction. Based on the spectral index identification, digital surface model was added as input variable of the identification model, and the comparative test was conducted.

Result
Compared with using a single spectral index, the addition of digital surface model improved the extraction accuracies of fruit tree and non fruit tree pixels. The total accuracies of six band fusions were all greater than 97%. The total accuracy of excess red index combined with digital surface model was the highest (98.77%) with Kappa coefficient of 0.956 7, and the vegetation extraction accuracies were superior to those of other five combinations of visible light vegetation indexes with digital surface model.

Conclusion
The combination of digital surface model with visible light vegetation index can excavate more deeply the information contained in the remote sensing data, and provide a reference for the extraction of similar tonal features in the image.
- UAV remote sensing,
- fruit tree extraction,
- visible light vegetation index,
- digital surface model,
- object-oriented method

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