Multi-Information Model for Large-Flowered Chrysanthemum Cultivar Recognition and Classification

Jue Wang; Yuankai Tian; Ruisong Zhang; Zhilan Liu; Ye Tian; Silan Dai

doi:10.3389/fpls.2022.806711

Multi-Information Model for Large-Flowered Chrysanthemum Cultivar Recognition and Classification

Front Plant Sci. 2022 Jun 6:13:806711. doi: 10.3389/fpls.2022.806711. eCollection 2022.

Authors

Jue Wang¹, Yuankai Tian¹, Ruisong Zhang², Zhilan Liu¹, Ye Tian², Silan Dai¹

Affiliations

¹ Beijing Key Laboratory of Ornamental Plants Germplasm Innovation and Molecular Breeding, Beijing Laboratory of Urban and Rural Ecological Environment, Key Laboratory of Genetics and Breeding in Forest Trees and Ornamental Plants of Ministry of Education, National Engineering Research Center for Floriculture, School of Landscape Architecture, Beijing Forestry University, Beijing, China.
² College of Technology, Beijing Forestry University, Beijing, China.

Abstract

The traditional Chinese large-flowered chrysanthemum is one of the cultivar groups of chrysanthemum (Chrysanthemum × morifolium Ramat.) with great morphological variation based on many cultivars. Some experts have established several large-flowered chrysanthemum classification systems by using the method of comparative morphology. However, for many cultivars, accurate recognition and classification are still a problem. Combined with the comparative morphological traits of selected samples, we proposed a multi-information model based on deep learning to recognize and classify large-flowered chrysanthemum. In this study, we collected the images of 213 large-flowered chrysanthemum cultivars in two consecutive years, 2018 and 2019. Based on the 2018 dataset, we constructed a multi-information classification model using non-pre-trained ResNet18 as the backbone network. The model achieves 70.62% top-5 test accuracy for the 2019 dataset. We explored the ability of image features to represent the characteristics of large-flowered chrysanthemum. The affinity propagation (AP) clustering shows that the features are sufficient to discriminate flower colors. The principal component analysis (PCA) shows the petal type has a better interpretation than the flower type. The training sample processing, model training scheme, and learning rate adjustment method affected the convergence and generalization of the model. The non-pre-trained model overcomes the problem of focusing on texture by ignoring colors with the ImageNet pre-trained model. These results lay a foundation for the automated recognition and classification of large-flowered chrysanthemum cultivars based on image classification.

Keywords: cultivar classification; cultivar recognition; deep learning; image classification; large-flowered chrysanthemum.