A cost‐effective maize ear phenotyping platform enables rapid categorization and quantification of kernels. (19th March 2021)

• Record Type: Journal Article
• Authors: Warman, Cedar; Sullivan, Christopher M.; Preece, Justin; Buchanan, Michaela E.; Vejlupkova, Zuzana; Jaiswal, Pankaj; Fowler, John E.
• Is Part Of: Plant journal. Volume 106:Number 2(2021)
• Page Start: 566
• Page End: 579
• Publication Date: 2021-03-19
• Subjects: Zea mays -- digital imaging -- deep learning -- ear -- high‐throughput phenotyping -- kernel -- pollen -- technical advance; Plant molecular biology -- Periodicals; Plant cells and tissues -- Periodicals; Botany -- Periodicals; 580
• DOI: 10.1111/tpj.15166
• Languages: English
• ISSNs: 0960-7412

Abstract

Significance Statement: A maize ( Zea mays ) ear phenotyping system built from commonly available parts creates images of the surface of ears and identifies kernel phenotypes with a deep‐learning‐based computer vision pipeline. The system increases the scope of feasible experiments addressing maize reproductive biology and related agricultural traits by reducing a bottleneck in data acquisition and quantification, paving the way for enhanced high‐throughput phenotyping in this area. SUMMARY: High‐throughput phenotyping systems are powerful, dramatically changing our ability to document, measure, and detect biological phenomena. Here, we describe a cost‐effective combination of a custom‐built imaging platform and deep‐learning‐based computer vision pipeline. A minimal version of the maize ( Zea mays ) ear scanner was built with low‐cost and readily available parts. The scanner rotates a maize ear while a digital camera captures a video of the surface of the ear, which is then digitally flattened into a two‐dimensional projection. Segregating GFP and anthocyanin kernel phenotypes are clearly distinguishable in ear projections and can be manually annotated and analyzed using image analysis software. Increased throughput was attained by designing and implementing an automated kernel counting system using transfer learning and a deep learning object detection model. The computer vision model was able to rapidly assess over 390 000 kernels, identifying male‐specific transmission defects across a wide range of GFP‐marked mutant alleles. This includes a previously undescribed defect putatively associated with mutation of Zm00001d002824, a gene predicted to encode a vacuolar processing enzyme. Thus, by using this system, the quantification of transmission data and other ear and kernel phenotypes can be accelerated and scaled to generate large datasets for robust analyses.