This article describes SpykProps, an inexpensive Python-based imaging system that quantifies morphological properties in unilateral inflorescences of grasses. The system offers an alternative to conventional phenotyping, enabling higher throughput and reducing subjectivity. It provides valuable insights into the determinants of seed yield and aids breeding decisions.
Inflorescence properties such length, spikelet number, and their spatial distribution across the rachis, are fundamental indicators of seed productivity in grasses and have been a target of selection throughout domestication and crop improvement. However, quantifying such complex morphology is laborious, time-consuming, and commonly limited to human-perceived traits.
These limitations can be exacerbated by unfavorable trait correlations between inflorescence architecture and seed yield that can be unconsciously selected for. Computer vision offers an alternative to conventional phenotyping, enabling higher throughput and reducing subjectivity. These approaches provide valuable insights into the determinants of seed yield, and thus, aid breeding decisions. Here, we described SpykProps, an inexpensive Python-based imaging system to quantify morphological properties in unilateral inflorescences, that was developed and tested on images of perennial grass (= 0.61
Inflorescence Seed Productivity Grasses Phenotyping Computer Vision Breeding Decisions
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