A New Paphiopedilum Orchid Database and Its Recognition Using Convolutional Neural Network

Arwatchananukul, Sujitra; Kirimasthong, Khwunta; Aunsri, Nattapol

doi:10.1007/s11277-020-07463-3

Cited by 13 publications

(8 citation statements)

References 22 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…SSAE produces 99.4% accuracy and 97.9% calibration while KNN produces 100% accuracy but only 92.6% calibration. Paphiopedilum orchid species have been recognized using CNN in a study conducted by Arwatchananukul et al [11] using 1500 images and 15 classes with an accuracy of 98.6%. Image classification also conducted using Naïve Bayes as simple statistics and probabilities in shallot quality achieved high accuracy [12].…”

Section: Research Methods 21 Related Researchmentioning

confidence: 99%

Orchid types classification using supervised learning algorithm based on feature and color extraction

et al. 2021

View full text Add to dashboard Cite

Orchid flower as ornamental plants with a variety of types where one type of orchid has various characteristics in the form of different shapes and colors. Here, we chosen support vector machine (SVM), Naïve Bayes, and k-nearest neighbor algorithm which generates text input. This system aims to assist the community in recognizing orchid plants based on their type. We used more than 2250 and 1500 images for training and testing respectively which consists of 15 types. Testing result shown impact analysis of comparison of three supervised algorithm using extraction or not and several variety distance. Here, we used SVM in Linear, Polynomial, and Gaussian kernel while k-nearest neighbor operated in distance starting from K1 until K11. Based on experimental results provide Linear kernel as best classifier and extraction process had been increase accuracy. Compared with Naïve Bayes in 66%, and a highest KNN in K=1 and d=1 is 98%, SVM had a better accuracy. SVM-GLCM-HSV better than SVM-HSV only that achieved 98.13% and 93.06% respectively both in Linear kernel. On the other side, a combination of SVM-KNN yield highest accuracy better than selected algorithm here.

show abstract

Section: Research Methods 21 Related Researchmentioning

confidence: 99%

Orchid types classification using supervised learning algorithm based on feature and color extraction

et al. 2021

View full text Add to dashboard Cite

show abstract

“…Use of the Inception-v3 feature extractor with transfer learning, together with a CNN, was proposed recently by Arwatchananukul et al in an attempt to distinguish 15 species of Paphiopedilum orchids [ 16 ]. They also built a new Paphiopedilum orchid database consisting of 1500 images, 100 images per species, each of them front-view images with the flower and labellum placed in a very similar standardized way.…”

Section: Methodsmentioning

confidence: 99%

Automated color detection in orchids using color labels and deep learning

et al. 2021

View full text Add to dashboard Cite

The color of particular parts of a flower is often employed as one of the features to differentiate between flower types. Thus, color is also used in flower-image classification. Color labels, such as ‘green’, ‘red’, and ‘yellow’, are used by taxonomists and lay people alike to describe the color of plants. Flower image datasets usually only consist of images and do not contain flower descriptions. In this research, we have built a flower-image dataset, especially regarding orchid species, which consists of human-friendly textual descriptions of features of specific flowers, on the one hand, and digital photographs indicating how a flower looks like, on the other hand. Using this dataset, a new automated color detection model was developed. It is the first research of its kind using color labels and deep learning for color detection in flower recognition. As deep learning often excels in pattern recognition in digital images, we applied transfer learning with various amounts of unfreezing of layers with five different neural network architectures (VGG16, Inception, Resnet50, Xception, Nasnet) to determine which architecture and which scheme of transfer learning performs best. In addition, various color scheme scenarios were tested, including the use of primary and secondary color together, and, in addition, the effectiveness of dealing with multi-class classification using multi-class, combined binary, and, finally, ensemble classifiers were studied. The best overall performance was achieved by the ensemble classifier. The results show that the proposed method can detect the color of flower and labellum very well without having to perform image segmentation. The result of this study can act as a foundation for the development of an image-based plant recognition system that is able to offer an explanation of a provided classification.

show abstract

“…Classically, plant image analysis was constrained to species identification using leaves or flowers as image subjects, often consisting of a single leaf or cluster of leaves laid across a white background [34,21,26]. Other organs of interest have been analyzed, with best results stemming from the identification of flowers [1,25]. More recently, plant species identification has spread to more difficult and sizable datasets, increasing the number of available tasks in the field.…”

Section: Related Workmentioning

confidence: 99%

Fine-Grained Visual Classification of Plant Species In The Wild: Object Detection as A Reinforced Means of Attention

Keaton,

Zaveri,

Kovur

et al. 2021

Preprint

View full text Add to dashboard Cite

Plant species identification in the wild is a difficult problem in part due to the high variability of the input data, but also because of complications induced by the long-tail effects of the datasets distribution. Inspired by the most recent fine-grained visual classification approaches which are based on attention to mitigate the effects of data variability, we explore the idea of using object detection as a form of attention. We introduce a bottom-up approach based on detecting plant organs and fusing the predictions of a variable number of organ-based species classifiers. We also curate a new dataset with a long-tail distribution for evaluating plant organ detection and organ-based species identification, which is publicly available 1 .

show abstract

A New Paphiopedilum Orchid Database and Its Recognition Using Convolutional Neural Network

Cited by 13 publications

References 22 publications

Orchid types classification using supervised learning algorithm based on feature and color extraction

Orchid types classification using supervised learning algorithm based on feature and color extraction

Automated color detection in orchids using color labels and deep learning

Fine-Grained Visual Classification of Plant Species In The Wild: Object Detection as A Reinforced Means of Attention

Contact Info

Product

Resources

About