An image-based automated pipeline for maize ear and silk detection in a highthroughput phenotyping platform
2017
Brichet, Nicolas | Cabrera Bosquet, Llorenç | Turc, Olivier | Welcker, Claude | Tardieu, Francois
Water deficit strongly impacts silk growth and silk emergence inmaize (Zea mays L.), which in turn determines the final numberof ovaries developing grains (Turc et al. 2016, Oury et al. 2016).However, phenotyping silk growth and silk expansion is difficultat throughput needed for genetic analyses. We have developedan image-based automated pipeline for maize ear and silk detectionin a high-throughput phenotyping platform. The first stepconsists of selecting the best whole plant side images containingmaximum information for each plant and day as that containingthe most leaves and whole stem, based on top view images. Inthe second step, the best side images are segmented and skeletonized,and potential ear positions are determined based onchanges in stem widths. The x, y, z ear position identified in thisway serves to pilot the movement of a mobile camera able totake a detailed picture taken at 30 cm from the ear, with the finalaim of determining silk emergence and silk growth duration.These methods were tested at the PhenoArch plant phenotypingplatform (www6.montpellier.inra.fr/lepse/M3P) in a panel of300 maize hybrids. First results showed that in >80% of cases,ears were successfully detected before silking and durationof silk expansion significantly correlated with visual scores. Theimage pipeline presented here opens up the way for large-scalegenetic analyses of control of reproductive growth to changes inenvironmental conditions in reproductive structures.
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