Flower Longevity Quantification in Greenhouses Using Deep Learning Models for Computer Vision

Abstract

Flower longevity is essential to maintain the quality and value of ornamental crops. The distribution of high-quality ornamental flowers has been established by developing various preservatives, especially for cut flowers. However, genetic improvements to promote flower longevity have not progressed, except for some ornamental crops. This is partly due to a lack of analytical methods to evaluate flower longevity in large-scale trials. This study showed that high-throughput image analysis using deep learning models for computer vision, which has rapidly progressed, can quantify the flower longevity of multiple lines in greenhouses. Seven lines of Portulaca umbraticola, a one-day-flowering crop for summer gardening, were photographed at regular intervals from the top of the greenhouse. Using trained object detection models, including the YOLO series, multiple flowers and pots were accurately detected in each image. The opening degree of flowering was calculated by trained image classification models, such as Densenet and VGG. Although the images generally contained many flowers, multiple object tracking models, such as ByteTrack and StrongSORT, enabled us to track each flower and estimate its longevity. Additionally, video classification sorted the tracked flowers into seven P. umbraticola lines. Each line’s flower longevity could be quantified and it differed each day and within the same flower line. Therefore, applying the proposed method using deep learning models could dramatically accelerate research and breeding of flowers with increased longevity. The applicability of this method to various ornamental crops and traits is also discussed.