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Image Segmentation of UAV Fruit Tree Canopy in a Natural Illumination Environment

Author

Listed:
  • Zhongao Lu

    (College of Engineering, China Agricultural University, No.17 Qing Hua Dong Lu, Haidian District, Beijing 100083, China)

  • Lijun Qi

    (College of Engineering, China Agricultural University, No.17 Qing Hua Dong Lu, Haidian District, Beijing 100083, China)

  • Hao Zhang

    (College of Engineering, China Agricultural University, No.17 Qing Hua Dong Lu, Haidian District, Beijing 100083, China)

  • Junjie Wan

    (College of Engineering, China Agricultural University, No.17 Qing Hua Dong Lu, Haidian District, Beijing 100083, China)

  • Jiarui Zhou

    (College of Engineering, China Agricultural University, No.17 Qing Hua Dong Lu, Haidian District, Beijing 100083, China)

Abstract

Obtaining canopy area, crown width, position, and other information from UAV aerial images and adjusting spray parameters in real-time according to this information is an important way to achieve precise pesticide application in orchards. However, the natural illumination environment in the orchard makes extracting the fruit tree canopy difficult. Hereto, an effective unsupervised image segmentation method is developed in this paper for fast fruit tree canopy acquisition from UAV images under natural illumination conditions. Firstly, the image is preprocessed using the shadow region luminance compensation method (SRLCM) that is proposed in this paper to reduce the interference of shadow areas. Then, use Naive Bayes to obtain multiple high-quality color features from 10 color models was combined with ensemble clustering to complete image segmentation. The segmentation experiments were performed on the collected apple tree images. The results show that the proposed method’s average precision rate, recall rate, and F1-score are 95.30%, 84.45%, and 89.53%, respectively, and the segmentation quality is significantly better than ordinary K-means and GMM algorithms.

Suggested Citation

  • Zhongao Lu & Lijun Qi & Hao Zhang & Junjie Wan & Jiarui Zhou, 2022. "Image Segmentation of UAV Fruit Tree Canopy in a Natural Illumination Environment," Agriculture, MDPI, vol. 12(7), pages 1-16, July.
    • Handle: RePEc:gam:jagris:v:12:y:2022:i:7:p:1039-:d:864493
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    References listed on IDEAS

    as
    1. García-Mateos, G. & Hernández-Hernández, J.L. & Escarabajal-Henarejos, D. & Jaén-Terrones, S. & Molina-Martínez, J.M., 2015. "Study and comparison of color models for automatic image analysis in irrigation management applications," Agricultural Water Management, Elsevier, vol. 151(C), pages 158-166.
    2. Zhenzhen Cheng & Lijun Qi & Yifan Cheng, 2021. "Cherry Tree Crown Extraction from Natural Orchard Images with Complex Backgrounds," Agriculture, MDPI, vol. 11(5), pages 1-19, May.
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    Cited by:

    1. Jiaxin Gao & Feng Tan & Jiapeng Cui & Bo Ma, 2022. "A Method for Obtaining the Number of Maize Seedlings Based on the Improved YOLOv4 Lightweight Neural Network," Agriculture, MDPI, vol. 12(10), pages 1-18, October.

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