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

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.

Automated summary

This paper presents an effective method for fast acquisition of fruit tree canopy from UAV images under natural illumination conditions. The proposed method preprocesses the image to reduce the interference of shadow areas, and then utilizes ensemble clustering with multiple high-quality color features obtained from 10 color models to complete image segmentation. Experimental results on apple tree images show that the proposed method achieves significantly better segmentation quality compared to ordinary K-means and GMM algorithms.