Citrus Tree Segmentation from UAV Images Based on Monocular Machine Vision in a Natural Orchard Environment

Abstract

The segmentation of citrus trees in a natural orchard environment is a key technology for achieving the fully autonomous operation of agricultural unmanned aerial vehicles (UAVs). Therefore, a tree segmentation method based on monocular machine vision technology and a support vector machine (SVM) algorithm are proposed in this paper to segment citrus trees precisely under different brightness and weed coverage conditions. To reduce the sensitivity to environmental brightness, a selective illumination histogram equalization method was developed to compensate for the illumination, thereby improving the brightness contrast for the foreground without changing its hue and saturation. To accurately differentiate fruit trees from different weed coverage backgrounds, a chromatic aberration segmentation algorithm and the Otsu threshold method were combined to extract potential fruit tree regions. Then, 14 color features, five statistical texture features, and local binary pattern features of those regions were calculated to establish an SVM segmentation model. The proposed method was verified on a dataset with different brightness and weed coverage conditions, and the results show that the citrus tree segmentation accuracy reached 85.27% ± 9.43%; thus, the proposed method achieved better performance than two similar methods.

Orchard management with small unmanned aerial vehicles: a survey of sensing and analysis approaches

“…SVM performed well in the classification of both RGB and hyperspectral datasets. To segment citrus trees from the background in RGB images, an SVM model established by the calculation of 14 color features and 5 statistical texture features could result in a accuracy of 85.27 + _9.43% (Chen, Hou, et al, 2019). For the later case, misclassification of sunlit and shaded areas could be overcome with the help of SVM, achieving a classification accuracy of 94.5% of mango trees (Ishida et al 2018).…”

“…The complexity of orchard environment, e.g., the changing solar illumination, seasonal vegetation in high density modern orchard, makes semi-automated methods (Chen, Hou, et al, 2019) the optimal solution for current orchard management tasks. Studies with more focus on the automated and simultaneous classification are therefore recommended.…”

Orchard Mapping with Deep Learning Semantic Segmentation

“…Applications of image segmentation with images acquired by UAS have used several machine learning algorithms: point-cloud data with the use of deep neural networks (DNNs) for tree canopy segmentation [ 25 ], support vector machines and image pre-processing filters for citrus trees segmentation [ 26 ], random forest (RF) super pixel classification for tree canopy extraction [ 27 ], and for the automatic segmentation of canopies with Deeplab v3+, a type of encoder-decoder network, for automatic segmentation of canopies [ 28 ].…”

Deep Learning-Based Instance Segmentation Method of Litchi Canopy from UAV-Acquired Images

“…Previous studies on the identification of individual trees have been focused on several species, including citrus [4,5,8,[25][26][27][28], apple [23], palm [10,14,29], cranberry [21], and urban trees [13,24]. However, although there are studies on the semantic segmentation of litchi flowers [30] and branches [31], the studies on litchi canopy segmentation based on remote sensing, as far as we know, have not been proposed.…”