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

Zhongao Lu, Lijun Qi, Hao Zhang, Junjie Wan and Jiarui Zhou
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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

Agriculture, 2022, vol. 12, issue 7, 1-16

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.

Keywords: natural illumination environment; fruit tree canopy; unsupervised image segmentation; ensemble clustering (search for similar items in EconPapers)
JEL-codes: Q1 Q10 Q11 Q12 Q13 Q14 Q15 Q16 Q17 Q18 (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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