Abstract
Perception of the fruit tree canopy is a vital technology for the intelligent control of a modern standardized orchard. Due to the complex three-dimensional (3D) structure of the fruit tree canopy, morphological parameters extracted from two-dimensional (2D) or single-perspective 3D images are not comprehensive enough. Three-dimensional information from different perspectives must be combined in order to perceive the canopy information efficiently and accurately in complex orchard field environment. The algorithms used for the registration and fusion of data from different perspectives and the subsequent extraction of fruit tree canopy related parameters are the keys to the problem. This study proposed a 3D morphological measurement method for a fruit tree canopy based on Kinect sensor self-calibration, including 3D point cloud generation, point cloud registration and canopy information extraction of apple tree canopy. Using 32 apple trees (Yanfu 3 variety) morphological parameters of the height (H), maximum canopy width (W) and canopy thickness (D) were calculated. The accuracy and applicability of this method for extraction of morphological parameters were statistically analyzed. The results showed that, on both sides of the fruit trees, the average relative error (ARE) values of the morphological parameters including the fruit tree height (H), maximum tree width (W) and canopy thickness (D) between the calculated values and measured values were 3.8%, 12.7% and 5.0%, respectively, under the V1 mode; the ARE values under the V2 mode were 3.3%, 9.5% and 4.9%, respectively; and the ARE values under the V1 and V2 merged mode were 2.5%, 3.6% and 3.2%, respectively. The measurement accuracy of the tree width (W) under the double visual angle mode had a significant advantage over that under the single visual angle mode. The 3D point cloud reconstruction method based on Kinect self-calibration proposed in this study has high precision and stable performance, and the auxiliary calibration objects are readily portable and easy to install. It can be applied to different experimental scenes to extract 3D information of fruit tree canopies and has important implications to achieve the intelligent control of standardized orchards.
Highlights
The canopy is the first part of fruit trees to contact with light and the outside environment; it is the main place to carry out respiration and photosynthesis
Canopy morphology and structure are visualized on computer, and relevant parameter information such as canopy height and volume are extracted by the developed object-based image analysis (OBIA) algorithm
The analysis shows that the calculated values of the fruit tree height, the maximum tree width, and the canopy thickness were significantly correlated with their corresponding measured values
Summary
The canopy is the first part of fruit trees to contact with light and the outside environment; it is the main place to carry out respiration and photosynthesis. Studies of information perception show that the canopy determines the growth of the fruit tree, and affects the yield and economic benefits [1,2]. Canopy morphology and structure are visualized on computer, and relevant parameter information such as canopy height and volume are extracted by the developed object-based image analysis (OBIA) algorithm. These data can provide theoretical basis and technical support for the intelligent control, monitoring, and management of orchards [5,6]
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