1. Introduction
Flowers are a kind of ornamental plant. In recent years, the fresh-cut flower industry in the Yunnan Province of China has made certain achievements in the whole industrial chain, such as improved seed breeding, green production, primary processing, and deep processing. The daily trading volume of the Yunnan Dounan Flower Auction Center reaches 3–3.5 million, and millions of flowers are sent all over the world every day. Flowers can be seen everywhere in daily life, thus bringing economic value to the flower industry.
During the process of flower sales, flower quality grade identification is an important and arduous task. Traditionally, the classification and grade identification of fresh cut flowers have relied on manual work, which has low efficiency and poor accuracy. Manual classification/grading cannot meet the needs of the insurance period of fresh flowers, the rapid growth of transportation, and market demand. Moreover, manual classification suffers from subjectivity and fatigue and requires professionally trained personnel. The lack of flower classification methods and sorting technology and equipment has hindered the deep processing of cut flowers and the development of China’s flower industry.
In the competitive global flower market, an effective evaluation of flower product quality is key to maintaining high standards. The quality of flower products has an important impact on flower industrial sales, and non-destructive testing (NDT) is one of the effective ways of ensuring the quality of flowers. In recent years, the development of sensor technology and computer vision technology has made NDT more effective and convenient [
1,
2]. Therefore, computer vision technology is applied to automatically identify the quality grade of flowers with a smart camera and intelligent algorithms.
Machine learning methods have been used with some success in the classification and variety identification of flowers; these methods include the support vector machine [
3], k-nearest neighbor [
4], random forest classifier [
5], and some combination methods [
6]. However, these traditional machine learning methods based on manually designed features did not achieve high classification accuracy, partly due to the limitation of the feature design. Therefore, some other methods with an automatic feature extractor were proposed to improve the classification accuracy such as through the scale invariant feature transform and segmentation-based fractal texture analysis [
7]. Albadarneh et al. [
8] proposed an automatic flower species detection method based on digital images by extracting the features of color, texture, and shape in the selected part of the image, and the recognition accuracy was better than 83% on the Oxfoed17 dataset.
In order to further improve the classification accuracy, deep learning was used to identify or classify flowers. Abu et al. [
9] used a deep neural network to classify five kinds of flower images, and the overall accuracy rate was over 90%. Hu et al. [
10] used convolutional neural networks (CNN) to learn the salient features for detecting flower varieties. However, using common CNN directly in identifying flower species for industry applications is still unsatisfactory. Therefore, some combination methods were developed. Cıbuk et al. [
11] proposed a combination method of CNN and SVM with an RBF kernel to classify flower species and achieved 96% accuracy in the Flower102 dataset. Hiary et al. [
12] used the Fully Convolutional Neural Network (FCN) segmentation method and the VGG16 pretraining model to classify flowers, and obtained an accuracy of more than 97%. Tian et al. [
13] proposed a deep learning method based on an improved tiny darknet and the accuracy on the Oxford 17-flower dataset was 92%. The high accuracy resulted from the differences between different types of flowers, which were obvious in this dataset and made them easy to detect. Anjani et al. [
14] used a CNN with RGB images for classifying rose flowers, and the classification accuracy reached 96% in the small number of classes. Wang et al. [
15] proposed a deep learning method based on pre-trained MobileNet with the weighted average method for flower image classification, and the mean accuracy of classification on one test set was 92%. However, the accuracy on the other test set was only 87%. Prasad et al. [
16] proposed a deep CNN method to classify the flower images. The method achieved 98% recognition rate in the flower dataset. Gavai et al. [
17] proposed a MobileNet model on the Tensorflow platform to retrain the flower category datasets. Although MobileNet could make the network structure smaller and more efficient, it sacrificed the performance accuracy.
From the research above, some novel CNN models or CNN combined with some other methods, such as VGG, MobileNet, and ResNet, have performed well in flower classification. On the other hand, transfer learning can improve the initial performance, convergence ability, and training speed of the neural network. Mehdipour et al. [
18] used the transfer learning method with a deep neural network for flower species identification. Cengil et al. [
19] used the transfer VGG16 model to achieve the best performance in the multiple classification problem of flowers, and the best validation accuracy was 94%. The prediction task with less training data can be learned by loading the pretrained model. Moreover, the use of transfer learning can improve the generalization ability of the model, providing good performance on a new dataset.
The research above mainly identifies the species of flowers, and the use of deep convolutional networks can improve the accuracy of the recognition of flowers. However, there are few studies applied to the detection of fresh flower quality. This research mainly discusses the grading of flower quality. The method of deep convolution neural network combined with transfer learning is used to classify the grade of flowers.
In this study, a method of flower sorting based on deep learning is used to collect color images and depth information of flowers; an improved convolution neural network is subsequently used to comprehensively analyze the image and depth data. According to the analysis results of the algorithm, the quality grade of fresh flowers is determined. In particular, the contribution of this research is as follows: (1) a method of grading the quality of fresh flowers is proposed based on fused depth information; (2) a set of classification models of four-dimensional deep learning convolutional neural network is proposed.
This paper is organized as follows: the data collection and the flower classification algorithms are introduced in
Section 2, the experimental results and discussion are provided in
Section 3, and the conclusion is given in
Section 4.