Deep learning for image-based large-flowered chrysanthemum cultivar recognition

Liu, Zhilan; Wang, Jue; Tian, Yongchao; Dai, Silan

doi:10.1186/s13007-019-0532-7

Cited by 28 publications

(38 citation statements)

References 26 publications

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“…Flowers with ornamental phenotype have brought a pleasing visual feast to humans due to their unique shapes, rich colors, and varied textures, which is important for flower phenotype research through computer vision. Recent studies have applied deep learning-based methods to flower recognition and made a series of important progress [ 9 – 14 ]. In particular, Lee [ 9 ] studied Convolutional Neural Networks (CNN) to learn unsupervised feature representations for 44 different plant species.…”

Section: Introductionmentioning

confidence: 99%

“…Deep Convolutional Neural Networks (DCNN) based hybrid method is applied to flower species classification on Flower17 and Flower102 datasets in [ 13 ]. Liu [ 14 ] proposed a method of large-flowered chrysanthemum cultivar recognition. In the context of deep learning [ 15 ], at least thousands of training samples are required for each class to saturate the performance of DCNN on known classes.…”

Section: Introductionmentioning

confidence: 99%

“…On the other hand, sometimes it is difficult to label samples. Some cultivars’ phenotype changed dramatically during their flower opening process [ 14 ], if labeled as the same cultivar will cause ambiguity, as shown in Fig. 1 .…”

Section: Introductionmentioning

confidence: 99%

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Metric learning for image-based flower cultivars identification

et al. 2021

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Background The study of plant phenotype by deep learning has received increased interest in recent years, which impressive progress has been made in the fields of plant breeding. Deep learning extremely relies on a large amount of training data to extract and recognize target features in the field of plant phenotype classification and recognition tasks. However, for some flower cultivars identification tasks with a huge number of cultivars, it is difficult for traditional deep learning methods to achieve better recognition results with limited sample data. Thus, a method based on metric learning for flower cultivars identification is proposed to solve this problem. Results We added center loss to the classification network to make inter-class samples disperse and intra-class samples compact, the script of ResNet18, ResNet50, and DenseNet121 were used for feature extraction. To evaluate the effectiveness of the proposed method, a public dataset Oxford 102 Flowers dataset and two novel datasets constructed by us are chosen. For the method of joint supervision of center loss and L2-softmax loss, the test accuracy rate is 91.88%, 97.34%, and 99.82% across three datasets, respectively. Feature distribution observed by T-distributed stochastic neighbor embedding (T-SNE) verifies the effectiveness of the method presented above. Conclusions An efficient metric learning method has been described for flower cultivars identification task, which not only provides high recognition rates but also makes the feature extracted from the recognition network interpretable. This study demonstrated that the proposed method provides new ideas for the application of a small amount of data in the field of identification, and has important reference significance for the flower cultivars identification research.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Metric learning for image-based flower cultivars identification

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“…It is very effective for real-life object detection, recognition and classification [7] . In the field of agricultural research, deep learning technology in agriculture includes crop/weed recognition, fruit harvesting [8] , and plant recognition [9] . Similarly, recent studies have also focused on the identification of plant diseases.…”

Section: Introductionmentioning

confidence: 99%

DBA_SSD ： A Novel End-to-End Object Detection Using Deep Attention Module for Helping Smart Device with Vegetable and Fruit Leaf Plant Disease Detection

Wang¹,

Yang²,

Yu³

et al. 2021

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In response to the difficulty of detecting and classifying pests and vegetable and fruit leaves with pests and diseases, this study proposes a novel vegetable and fruit leaf pest detection method called deep block attention SSD (DBA_SSD) for the identification of pests and diseases and classification of the degree of pests and diseases of vegetable and fruit leaves. We propose three vegetable and fruit leaf pest detection methods, namely, squeeze-and excitation SSD (Se_SSD), DB_SSD, and DBA_SSD. Se_SSD fuses SSD feature extraction network and attention mechanism channel, DB_SSD improves VGG feature extraction network, and DBA_SSD fuses the improved VGG network and channel attention mechanism. To reduce the training time and accelerate the training process, the convolutional layers trained in the Image Net image dataset by the VGG model are migrated to this model, whereas the collected vegetable and fruit disease image dataset is randomly divided into training set, validation set, and test set in the ratio of 8:1:1. In addition, data enhancement methods, such as histogram equalization and horizontal flip were used to expand the image data. The performance of the three improved algorithms is compared and analyzed in the same environment and with the classical target detection algorithms YOLOv4, YOLOv3, Faster RCNN, and YOLOv4 tiny. Experiments show that DBA_SSD outperforms the two other improved algorithms, and its performance in comparative analysis is superior to other target detection algorithms.

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“…In this study, it has been shown that even in field environments, the images collected by typical smart phones or digital cameras can be used to train deep CNN by transfer learning for an accurate wine grape species recognition.Specifically, in this study, the accuracy of our trained model can reach 99.91%, and the early dataset augmentation plays an important role in achieving the performance. In the case of using transfer learning to solve the problem of object recognition, in order to improve the accuracy of the detection model, images are often acquired in a controlled environment in the laboratory[19]. The image preprocessing method is used to enhance the object or weaken the background on the other hand.…”

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Identification of grapevine(Vitis vinifera L.)cultivars by vine leaf image via deep learning and mobile devices

Liu

Guo-qian

et al. 2020

Preprint

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Background Traditional vine variety identification methods usually rely on destructive sampling of vine leaves followed by physical, physiological, biochemical and molecular measurement, which is destructive, time-consuming, labor-intensive and requires experienced grape phenotype analysts. To mitigate these problems, this study aims to develop an application (App) running on Android client to identify wine grape automatically and in real time, which can help the growers to quickly obtain the variety information. Results Experimental results show that all Convolutional Neural Network (CNN) classification algorithms can achieve an accuracy of over 94% for 21 categories on validation data, which proves the feasibility of using transfer learning to identify grape species in field environments. In particular, the classification model with the highest average accuracy is Googlenet (97.4%) with learning rate (0.001), mini-batch size (32) and maximum number of epochs (80). Testing results of the App on Android device also confirms these results. Conclusions An App running on Android client is developed to identify the wine grape in real time and field condition, which can help the growers to quickly obtain the variety information of wine grape. A total of 4200 leaf images were first collected in the field environment, which contain 21 types of typical grapes. Then both image complement preprocessing and data augmentation are adopted to enhance image features and augment the training dataset. On this basis, a number of typical CNN models (including VGG-16, DenseNet, ResNet101, ResNet18, and GoogLeNet) are compared in transfer learning training to identify the suitable one while with model parameter tuning. It is shown that GoogLeNet model outperformed other models in terms of accuracy, model complexity, and robustness with a fine-tuned accuracy of 99.91%. The effect of image complement preprocessing is also assessed by using Grad-CAM algorithm. The developed App is also shown to be feasible for real-life applications with a processing time of less than 1 second.

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Deep learning for image-based large-flowered chrysanthemum cultivar recognition

Cited by 28 publications

References 26 publications

Metric learning for image-based flower cultivars identification

Metric learning for image-based flower cultivars identification

DBA_SSD ： A Novel End-to-End Object Detection Using Deep Attention Module for Helping Smart Device with Vegetable and Fruit Leaf Plant Disease Detection

Identification of grapevine(Vitis vinifera L.)cultivars by vine leaf image via deep learning and mobile devices

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