DeepHerb: A Vision Based System for Medicinal Plants Using Xception Features

Roopashree, S.; Anitha, J.

doi:10.1109/access.2021.3116207

Cited by 38 publications

(18 citation statements)

References 40 publications

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“…The Swedish dataset contains 75 samples from each of the 15 Species of Swedish plants or trees [ 88 , 89 ]. The Folio dataset contains 576 images with 18 samples each from 32 species [ 90 ] whiles the Mendeley dataset contains 1835 images from 30 species [ 91 ].…”

Section: Methodsmentioning

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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Section: Methodsmentioning

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

View full text Add to dashboard Cite

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“…It is based on Depthwise Separable Convolution [42] which is an operation consisting of a depthwise convolution followed by a pointwise convolution [43]. The benefit of this model is high performance even in comparison to similar models such as Inception, but the model is very computationally expensive to train [44,45].…”

Section: Utilized Cnn Architecturesmentioning

confidence: 99%

Quality Assessment Assistance of Lateral Knee X-rays: A Hybrid Convolutional Neural Network Approach

et al. 2023

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A common issue with X-ray examinations (XE) is the erroneous quality classification of the XE, implying that the process needs to be repeated, thus delaying the diagnostic assessment of the XE and increasing the amount of radiation the patient receives. The authors propose a system for automatic quality classification of XE based on convolutional neural networks (CNN) that would simplify this process and significantly decrease erroneous quality classification. The data used for CNN training consist of 4000 knee images obtained via radiography procedure (KXE) in total, with 2000 KXE labeled as acceptable and 2000 as unacceptable. Additionally, half of the KXE belonging to each label are right knees and left knees. Due to the sensitivity to image orientation of some CNNs, three approaches are discussed: (1) Left-right-knee (LRK) classifies XE based just on their label, without taking into consideration their orientation; (2) Orientation discriminator (OD) for the left knee (LK) and right knee (RK) analyses images based on their orientation and inserts them into two separate models regarding orientation; (3) Orientation discriminator combined with knee XRs flipped to the left or right (OD-LFK)/OD-RFK trains the models with all images being horizontally flipped to the same orientation and uses the aforementioned OD to determine whether the image needs to be flipped or not. All the approaches are tested with five CNNs (AlexNet, ResNet50, ResNet101, ResNet152, and Xception), with grid search and k-fold cross-validation. The best results are achieved using the OD-RFK hybrid approach with the Xception network architecture as the classifier and ResNet152 as the OD, with an average AUC of 0.97 (±0.01).

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“…Various research studies reveal that researchers are showing a great interest in the automatic identification and classification of plant species using plant image specimens and by employing ML and DL techniques [6][7][8][9][10]. This automatic identification is carried out in different stages viz a) Image acquisition, b) Image preprocessing, c) Feature extraction d) Classification.…”

Section: Introductionmentioning

confidence: 99%

An Effective Ensemble Convolutional Learning Model with Fine-tuning for Medicinal Plant Leaf Identification

Hajam,

Arif,

Khanday

et al. 2023

Preprint

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Accurate and efficient medicinal plant image classification is of utmost importance as these plants produce a wide variety of bioactive compounds that offer therapeutic benefits. With a long history of medicinal plant usage, different parts of plants, such as flowers, leaves, and roots, have been recognized for their medicinal properties and are used for plant identification. However, leaf images are extensively used due to their convenient accessibility and are a major source of information. In recent years, transfer learning and fine-tuning, which use pre-trained deep convolutional networks to extract pertinent features, has emerged as an extremely effective approach for image identification problems. This study is leveraging the power of three component deep convolutional neural networks, namely VGG16, VGG19 and DenseNet201, to derive features from the input images of the medicinal plant dataset, containing leaf images of 30 classes. The models were compared and ensembled to make four hybrid models to enhance the predictive performance by utilizing the averaging and weighted averaging strategies. Quantitative experiments were carried out to evaluate the models on the Mendeley medicinal leaf dataset. The resultant ensemble of VGG19+DensNet201 with fine-tuning showcases enhanced capability in identifying medicinal plant images with an improvement of 7.43% and 5.8% compared with VGG19 and VGG16. Furthermore, VGG19+DensNet201 can outperform its standalone counterparts by achieving an accuracy of 99.12% on the test set. A thorough assessment with metrics such as accuracy, recall, precision, and f1-score firmly establishes the effectiveness of the ensemble strategy.

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DeepHerb: A Vision Based System for Medicinal Plants Using Xception Features

Cited by 38 publications

References 40 publications

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

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

Quality Assessment Assistance of Lateral Knee X-rays: A Hybrid Convolutional Neural Network Approach

An Effective Ensemble Convolutional Learning Model with Fine-tuning for Medicinal Plant Leaf Identification

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