A deep learning method for differentiating safflower germplasm using optimal leaf structure features

Abstract

Medicinal plants, such as safflower (Carthamustinctorius), are essential in both conventional and modern healthcare. This study evaluatesthe reliability of leaf image classification for differentiating safflower germplasm accessions, despite the challenge posed by similar leaf structures across varieties. Traditional classification methods can be time-consuming and error prone, underscoring the need for fine-grained classification techniques. To address this, we introduce a novel comprehensive leaf database of safflower varieties that is meticulously curated by experts. This database can be used to support future research. We evaluate state-of-the-art deep learning methods for classifying safflower varieties and propose a novel approach using a Vision Transformer (ViT) model with an optimal leaf structure feature (OLSF). The OLSF, calculated as the average response of a multidirectional Gabor filter bank and optimized with the structural similarity index measure (SSIM), enhances complex leaf features, such as veins, texture, and frequency variations, to enhance the classification performance of deep learning models. The experimental results reveal that the OLSF-ViT model achieves excellent accuracy scores of 100 %, 99.05 %, and 89.65 % on the Folio, UCI Leaves, and JNUSafflower datasets, respectively. These findings demonstrate that leaf image analysis is an effective and affordable tool for investigating the phenotypic diversity among safflower cultivars. This study highlights the potential of OLSF-ViT in automatic plant classification, and the results can be used in plant science, herbal medicine, and biodiversity conservation.

Keywords

• Safflower
• Plant classification
• Gabor filter
• Vision transformer
• Deep learning