ASHS 2025 Conference

This study developed an AI-powered image preprocessing tool to analyze daylily flower traits, providing a user-friendly and feature-rich solution for accurate image segmentation- a process that separates the flower from the background in photographs. The tool's design combines a pre-trained TRACER convolutional neural network (CNN) with an EfficientNet backbone to ensure robust and precise segmentation. It features a user-friendly graphical interface built with PyQt that facilitates seamless image uploads, real-time visualization of segmentation results, and customization options. Furthermore, a built-in manual refinement pencil function allows for precise corrections to automated masks, ensuring accuracy when needed. It supports multiple output formats, including masks and annotated images, enabling easy integration into subsequent analyses. A dataset comprising 1,450 daylily color images was employed for training and evaluating the tool. A comparative analysis of four deep learning models (TRACER, BASNet, U2 Net, and DeepLabV3), using metrics such as Accuracy, Precision, Recall, Dice Score, Jaccard Index, and XOR Error, highlighted the superior performance of the TRACER model in terms of accuracy and reliability. The findings show that the tool successfully separates flowers from intricate backgrounds, effectively addressing issues such as petal overlap, various phenotypes, and environmental variations. This automation tool has the potential to replace the labor-intensive and error-prone manual segmentation process, which has posed a significant limitation in AI applications for daylily research, including accurate trait prediction and classification.