High Throughput Phenotyping of Blueberry Bush Morphological Traits Using Unmanned Aerial Systems

Abstract

Phenotyping morphological traits of blueberry bushes in the field is important for selecting genotypes that are easily harvested by mechanical harvesters. Morphological data can also be used to assess the effects of crop treatments such as plant growth regulators, fertilizers, and environmental conditions. This paper investigates the feasibility and accuracy of an inexpensive unmanned aerial system in determining the morphological characteristics of blueberry bushes. Color images collected by a quadcopter are processed into three-dimensional point clouds via structure from motion algorithms. Bush height, extents, canopy area, and volume, in addition to crown diameter and width, are derived and referenced to ground truth. In an experimental farm, twenty-five bushes were imaged by a quadcopter. Height and width dimensions achieved a mean absolute error of 9.85 cm before and 5.82 cm after systematic under-estimation correction. Strong correlation was found between manual and image derived bush volumes and their traditional growth indices. Hedgerows of three Southern Highbush varieties were imaged at a commercial farm to extract five morphological features (base angle, blockiness, crown percent height, crown ratio, and vegetation ratio) associated with cultivation and machine harvestability. The bushes were found to be partially separable by multivariate analysis. The methodology developed from this study is not only valuable for plant breeders to screen genotypes with bush morphological traits that are suitable for machine harvest, but can also aid producers in crop management such as pruning and plot layout organization.

Development and Validation of a Model to Combine NDVI and Plant Height for High-Throughput Phenotyping of Herbage Yield in a Perennial Ryegrass Breeding Program

“…Current, HY phenotyping is time-consuming and relies mainly on visual scoring. HTP platforms that would improve the speed and accuracy of HY phenotyping have, so far, only been applied at the plot level [20,29], and no one has conducted phenotyping at the individual plant level except on trees [38][39][40]. Various sensor-based assessments of phenotyping at the individual plant level is required to implement molecular breeding techniques, like GS.…”

Quantitative Estimation of Wheat Phenotyping Traits Using Ground and Aerial Imagery

“…Novel image analysis systems are now being designed and implemented to automatically capture the ensuing morphometric changes in plant traits in the field [2]. State of the art imaging hardware and image analysis methods have attracted considerable interest from the plant phenotyping community.…”

Editorial for the Special Issue “Estimation of Crop Phenotyping Traits using Unmanned Ground Vehicle and Unmanned Aerial Vehicle Imagery”

“…Above referred methods and other image segmentation methods in this special issue were mainly used for extracting the target morphological traits, while reducing the interference from the background information. Other morphological traits such as crown height, extent, volume, and diameter were mainly derived and processed from three-dimensional point clouds by structure from motion algorithms [8,19,30,31,33]. artificial neural network (ANN), etc., were usually preferred and recommended [10,[14][15][16][17][18]21,[23][24]32].…”

“…These structural traits were determined by three-dimensional point clouds from RGB imagery or LiDAR scans. Tu et al [31] and Patrick et al [8] established a structure from motion algorithm (SfM) for accurately estimating crown height, extent, plant projective cover in avocado tree and Blueberry bush, respectively. Wang et al [30] compared three representative 3D data acquisition approaches, including 3D Laser scanning, multi-view stereo reconstruction, and 3D digitizing estimates, and these approaches with respect to leaf length, width, inclination angle, azimuth, area, and height in Maize.…”