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.

Plant phenotyping relevance

UAS画像とSfM点群からブルーベリー樹体の形態形質を抽出し、地上真値と精度検証しており、植物フェノタイピング手法が研究の中心です。

Code and data availability

The supplied blocks describe UAS image collection, Photoscan/MATLAB processing, and manual measurements of blueberry bushes, but contain no data availability statement, no public dataset or code deposit, and no author-provided URLs. No paper-specific public asset is identified.

No evidence-backed public reproduction asset is currently recorded.