Investigating pea (Pisum sativum L.) flowering with high throughput field phenotyping and object detection

Abstract

Flowering is one of the most important and sensitive processes throughout a plant's life and marks the start of the reproductive phase. Flowering traits largely define yield potential and are therefore crucial for crop breeding. To observe flowering dynamics under field conditions, visual ratings have been a standard method for decades. Today, high-throughput field phenotyping (HTFP) methods provide opportunities for objective and efficient data collection. We developed an object detection approach (based on YOLOv8) that allows to collect detailed data about flower and pod density. RGB-images from 12 pea breeding lines were automatically acquired by the field phenotyping platform (FIP) of ETH Zurich in two years. The trained model reached high accuracy for open flower detection, which allowed to monitor flowering dynamics and flower density over time. Maximal flower density (Max.Fl.Dens) was highly correlated (R 2 = 0.967) to ground truth data taken in the field. Clear differences in timing of flowering and flower density were detected between breeding lines and years. Furthermore, a high correlation was observed between the maximal flower density and yield components. This automated, data-driven method of flower and pod detection proved itself as a reliable tool. Therefore, the results are promising for the use of RGB imaging methods to objectively assess not only flowering dynamics but also flower density and fruiting efficiency. Maximal flower density allows to predict seed amount and therefore has potential as selection trait in breeding programs. Fruiting efficiency could be used to identify stress-tolerant breeding lines.

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