Image recognition-based petal arrangement estimation

Abstract

Flowers exhibit morphological diversity in the number and positional arrangement of their floral organs, such as petals. The petal arrangements of blooming flowers are represented by the overlap position relation between neighboring petals, an indicator of the floral developmental process; however, only specialists are capable of the petal arrangement identification. Therefore, we propose a method to support the estimation of the arrangement of the perianth organs, including petals and tepals, using image recognition techniques. The problem for realizing the method is that it is not possible to prepare a large number of image datasets: we cannot apply the latest machine learning based image processing methods, which require a large number of images. Therefore, we describe the tepal arrangement as a sequence of interior-exterior patterns of tepal overlap in the image, and estimate the tepal arrangement by matching the pattern with the known patterns. We also use methods that require less or no training data to implement the method: the fine-tuned YOLO v5 model for flower detection, GrubCut for flower segmentation, the Harris corner detector for tepal overlap detection, MAML-based interior-exterior estimation, and circular permutation matching for tepal arrangement estimation. Experimental results showed good accuracy when flower detection, segmentation, overlap location estimation, interior-exterior estimation, and circle permutation matching-based tepal arrangement estimation were evaluated independently. However, the accuracy decreased when they were integrated. Therefore, we developed a user interface for manual correction of the position of overlap estimation and interior-exterior pattern estimation, which ensures the quality of tepal arrangement estimation.

Keywords: circular permutation matching; meta-learning; plant measurement; segmentation; tepal arrangement.