Plant species identification from leaf patterns using histogram of oriented gradients feature space and convolution neural networks

Bao, Truong Quoc; Kiet, Nguyen Thanh Tan; Dinh, Truong Quoc; Hiep, Huynh Xuan

doi:10.1080/24751839.2019.1666625

Cited by 34 publications

(15 citation statements)

References 14 publications

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“…Traditional Chinese medicine manufacturers can establish an APP for traditional Chinese medicine recognition, mainly using fine-grained image recognition technology, users only need to take photos of traditional Chinese medicines to identify, improve work efficiency, and efficiently identify and learn the characteristics and efficacy value of various traditional Chinese medicines [3] .…”

Section: Extraction and Identification Of Digital Images Of Chinese M...mentioning

confidence: 99%

Research on the application of intelligent Chinese herbal medicine identification technology

Cui,

Song,

Sun

et al. 2024

International Conference on Computer Graphics, Artificial Intelligence, and Data Processing (ICCAID 2023)

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Chinese medicinal materials play an important role in the health care of our people, and the quality of Chinese medicine has received more and more attention. The quality of Chinese medicinal materials and the authenticity of drugs are related to the therapeutic effect, so the identification of authenticity of Chinese medicinal materials is very important. China is very rich in traditional Chinese medicine resources, and many kinds of traditional Chinese medicines have brought great difficulties to the identification of traditional Chinese medicinal materials. The characteristics of traditional Chinese medicines are not only the appearance characteristics of medicinal materials, but also the external manifestations of the internal organizational structure of medicinal materials and the chemical components contained in them. In research, the essence of the trait identification of traditional Chinese medicine is to explain the correlation between trait characteristics and internal tissue structure and its intrinsic chemical composition in modern science. At present, some new methods and technologies are applied to the identification of traditional Chinese medicine traits, such as simulating the electronic nose and electronic tongue generated by the human olfactory organ and taste system, and image recognition and identification are also used. These new methods and technologies are all extensions of trait identification and are conducive to the development of Chinese medicine.

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Section: Extraction and Identification Of Digital Images Of Chinese M...mentioning

confidence: 99%

Research on the application of intelligent Chinese herbal medicine identification technology

Cui,

Song,

Sun

et al. 2024

International Conference on Computer Graphics, Artificial Intelligence, and Data Processing (ICCAID 2023)

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“…Bao et al [ 74 ] devised a system for recognizing plants based on their leaf patterns that use two methods: a Histogram of Oriented Gradient (HoG) and a deep convolutional neural network. HoG was used to classify the features, while CNN was utilized to identify them.…”

Section: Literature Reviewmentioning

confidence: 99%

A Novel Computer Vision Model for Medicinal Plant Identification Using Log-Gabor Filters and Deep Learning Algorithms

Oppong

Twum

Hayfron-Acquah

et al. 2022

Computational Intelligence and Neuroscience

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Computer vision is the science that enables computers and machines to see and perceive image content on a semantic level. It combines concepts, techniques, and ideas from various fields such as digital image processing, pattern matching, artificial intelligence, and computer graphics. A computer vision system is designed to model the human visual system on a functional basis as closely as possible. Deep learning and Convolutional Neural Networks (CNNs) in particular which are biologically inspired have significantly contributed to computer vision studies. This research develops a computer vision system that uses CNNs and handcrafted filters from Log-Gabor filters to identify medicinal plants based on their leaf textural features in an ensemble manner. The system was tested on a dataset developed from the Centre of Plant Medicine Research, Ghana (MyDataset) consisting of forty-nine (49) plant species. Using the concept of transfer learning, ten pretrained networks including Alexnet, GoogLeNet, DenseNet201, Inceptionv3, Mobilenetv2, Restnet18, Resnet50, Resnet101, vgg16, and vgg19 were used as feature extractors. The DenseNet201 architecture resulted with the best outcome of 87% accuracy and GoogLeNet with 79% preforming the worse averaged across six supervised learning algorithms. The proposed model (OTAMNet), created by fusing a Log-Gabor layer into the transition layers of the DenseNet201 architecture achieved 98% accuracy when tested on MyDataset. OTAMNet was tested on other benchmark datasets; Flavia, Swedish Leaf, MD2020, and the Folio dataset. The Flavia dataset achieved 99%, Swedish Leaf 100%, MD2020 99%, and the Folio dataset 97%. A false-positive rate of less than 0.1% was achieved in all cases.

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“…The same thing is also found in some plants, where some morphological characters can be used as distinguishing characters between one clone or plant species with other plants, especially for the selection of plant breeding programs. In cacao plants, it is known that there are significant differences in leaf length and leaf area on the characterization of 6 cocoa clones [15], flavia & Swedish leaf [16], chili pepper accesions [17], Okra accessions [18], pamelo leaves [19], corchorus accession [20], local durio [21], taro [22],…”

Section: A Leaf Size Characteristicsmentioning

confidence: 99%

Leaf Characterizations of IRR 400 Series, BPM 24, and RRIC 100 Rubber (Hevea brasiliensis Muell. Arg.) Clone Using Leafgram Method

Pasaribu¹,

Basyuni²,

Purba³

et al. 2022

Int. J. Adv. Sci. Eng. Inf. Technol.

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Leaf characterization of rubber clones needs to be done from the start, especially for plants in the budwood garden. This function is to produce genetically pure and original plants. The purpose of this study was to characterize the IRR series 400, RRIC 100, and BPM 24 clones based on leaf character using the leafgram method. The types of clones that were characterized were IRR 425, IRR 428, IRR 429, IRR 434, IRR 440, RRIC 100, and BPM 24. The leaves characterized were the middle leaves of each clone as many as 50 leaves per clone, divided into three replications. The leaf characters observed were length, width, top fold point, center point, left strand, large, apex angle, vein angle, basal angle, apex kite angle, basal kite angle, ratio length/width, ratio length/top fold point, ratio left strand/large/ ratio left strand top fold point, ratio left strand/center point, ratio top fold point/large, ratio top fold point/center point, ratio apex/basal angle, ratio vein/apex angle, ratio vein/basal angle and ratio apex kite/basal kite angle. Data were analyzed using MINITAB 16 software and Microsoft Office 2010. The characterization results showed that the distinguishing characters that can be used as characteristic of characters are width, left strand, apex angle, basal angle, ratio length /width, ratio left strand/top fold point, ratio left strand ratio/center point, ratio top fold point/large, ratio apex/basal angle, ratio vein/apex angle, and ratio vein/ basal angle.

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Plant species identification from leaf patterns using histogram of oriented gradients feature space and convolution neural networks

Cited by 34 publications

References 14 publications

Research on the application of intelligent Chinese herbal medicine identification technology

Research on the application of intelligent Chinese herbal medicine identification technology

A Novel Computer Vision Model for Medicinal Plant Identification Using Log-Gabor Filters and Deep Learning Algorithms

Leaf Characterizations of IRR 400 Series, BPM 24, and RRIC 100 Rubber (Hevea brasiliensis Muell. Arg.) Clone Using Leafgram Method

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