02225nas a2200313   4500000000100000000000100001008004100002260001300043653002600056653002200082653001800104653003700122653001400159653001000173653002000183653001300203100001700216700002500233700001800258700002200276700002200298700001900320700001600339245011200355300001200467490000800479520141000487022001401897       2021                            d  c2021 Apr10aCost-Benefit Analysis10aDatasets as Topic10aDeep Learning10aHigh-Throughput Screening Assays10aPhenotype10aSeeds10aVideo Recording10aZea mays1 aCedar Warman1 aChristopher Sullivan1 aJustin Preece1 aMichaela Buchanan1 aZuzana Vejlupkova1 aPankaj Jaiswal1 aJohn Fowler00aA cost-effective maize ear phenotyping platform enables rapid categorization and quantification of kernels.  a566-5790 v1063 a<p>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.</p>
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