A novel 3D imaging system for strawberry phenotyping
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He et al. Plant Methods (2017) 13:93 DOI 10.1186/s13007-017-0243-x
Open Access
METHODOLOGY ARTICLE
A novel 3D imaging system for strawberry phenotyping Joe Q. He1,2, Richard J. Harrison1,2 and Bo Li1*
Abstract Background: Accurate and quantitative phenotypic data in plant breeding programmes is vital in breeding to assess the performance of genotypes and to make selections. Traditional strawberry phenotyping relies on the human eye to assess most external fruit quality attributes, which is time-consuming and subjective. 3D imaging is a promising high-throughput technique that allows multiple external fruit quality attributes to be measured simultaneously. Results: A low cost multi-view stereo (MVS) imaging system was developed, which captured data from 360° around a target strawberry fruit. A 3D point cloud of the sample was derived and analysed with custom-developed software to estimate berry height, length, width, volume, calyx size, colour and achene number. Analysis of these traits in 100 fruits showed good concordance with manual assessment methods. Conclusion: This study demonstrates the feasibility of an MVS based 3D imaging system for the rapid and quantitative phenotyping of seven agronomically important external strawberry traits. With further improvement, this method could be applied in strawberry breeding programmes as a cost effective phenotyping technique. Keywords: 3D imaging, Multi-view stereo, Point cloud analysis, High-throughput phenotyping Background A successful strawberry breeding programme generates and selects genotypes with traits suitable for the industry in its target geographic region [1]. As often genotypes cannot be directly observed, traditional breeding selects on the basis of a weighted selection index of phenotypes [2]. In order to maximise the accuracy of selection, heritable traits of interest must be measured precisely and accurately. Currently, most external fruit quality phenotyping approaches in strawberry breeding relies on the human eye to make assessments [1]. This approach is labour-intensive, prone to human bias and typically generates ordinal data less suitable for the most powerful quantitative statistical models [3]. Use of image analysis has the potential to overcome some of these limitations, with previous studies showing success in utilising 2D high-throughput imaging systems to assess external fruit quality [4]. Most studies were
*Correspondence: [email protected] 1 NIAB EMR, New Road, East Malling ME19 6BJ, UK Full list of author information is available at the end of the article
focussed on colour analysis of fruits, including apple [5], citrus [6], mango [7] and banana [8], but some systems have assessed morphological attributes, including the size of apples [9] and the shape of oranges [10]. For strawberry, an automated grading system was developed by Liming et al. [11] that assesses colour, size and four degrees of shape. In another 2D strawberry imaging system, developed by Nagata et al., the maximum fruit diameter could be derived by automatical
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