Three-Dimensional Morphological Measurement Method for a Fruit Tree Canopy Based on Kinect Sensor Self-Calibration

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.

Combination of an Automated 3D Field Phenotyping Workflow and Predictive Modelling for High-Throughput and Non-Invasive Phenotyping of Grape Bunches

“…For 3D plant phenotyping, few studies investigate the impact of the recording angles on the estimation of phenotypic traits. Depending on the plant and the measured trait, single recording angles can affect phenotypic measurements [31,32]. The comparison between the field and the 360 • scans of the 48 grape bunches revealed that approximately only 30-40% of the bunch structure can be recorded (due to the recording angle and depending on size and shape of the bunch and the presence of secondary bunches).…”

Canopy Parameter Estimation of Citrus grandis var. Longanyou Based on LiDAR 3D Point Clouds

“…For C. grandis var. Longanyou, because its tree height is much lower than tree height in natural forests and the branches droop to touch the ground, the crown height of grapefruit was recorded as the distance from the highest point to the lowest point of the fruit tree point cloud according to Yang's definition [2]. Therefore, crown height was calculated using Equation ( 1):…”

“…The canopy adsorbs light, interacts with the external environment, and is the main place where fruit trees perform respiration and photosynthesis. Information perception studies have shown that the crown determines the growth and development of trees, which in turn affects their yield and economic benefits [2]. Canopy characteristics have important guiding significance for research related to trees, because they can usually be used for growth assessment [3], biomass estimation [4,5], pruning effect analysis [6], yield prediction [7], health assessment early disease detection [8,9] and in the use of plant protection products such as insecticides [10].…”

Optimization of 3D Point Clouds of Oilseed Rape Plants Based on Time-of-Flight Cameras

“…Some other detection methods have been proposed, and laser scanning has become a common means of evaluating these methods [ 18 , 19 , 20 ]. Compared to laser scanning, the time-of-flight (TOF) camera has the advantages of speed, simplicity and low cost and the potential for use in 3D phenotyping research [ 20 , 21 , 22 , 23 , 24 , 25 , 26 ]. For example, Microsoft Kinect is widely used as a typical TOF camera.…”