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

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.

Chrysanthemum Classification Method Integrating Deep Visual Features from both the Front and Back Sides

“…However, the clustering and visualization of extracted deep features did not manifest distinct distribution patterns. Wang et al [41] integrated AP clustering analysis with deep features, proposing a multi-information model based on deep learning for the identification and classification of large-flowered Chrysanthemums. Building upon the traditional VGG16 convolutional neural network, Huang et al [42] introduced an enhanced multi-scale, multi-parallel convolutional neural network termed VGG-Inception.…”

Feature extraction of 3D Chinese rose model based on color and shape features

“…Tigistu ( Tigistu and Abebe, 2021 ) studied a Chinese rose’s variety classification scheme using color and shape features, in which the color information was described by three statistical moments of the RGB, and the flower shape was described by Fourier coefficients. Wang ( Wang et al., 2022 ) collected images of large-flowered chrysanthemum cultivars in two consecutive years and proposed a multi-information model based on deep learning to recognize and classify large-flowered chrysanthemums. Zhang ( Zhang et al., 2021 ) employed Deep Convolutional Neural Networks (DCNN) to train a chrysanthemum variety recognition system, which converted the image into embedded space, and used the metric learning method to identify unknown chrysanthemum varieties.…”