Recognition of leaves of different medicinal plant species using a robust image processing algorithm and artificial neural networks classifier

Azadnia, Rahim; Kheiralipour, Kamran

doi:10.1016/j.jarmap.2021.100327

Cited by 32 publications

(25 citation statements)

References 51 publications

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“…The processed image (in RGB color space) was transformed into different spaces including L * a * b *, HSV, NRGB, CrCgCb, I1I2I3, and gray (Figure 2) [31][32][33][34]. Nineteen image channels from each color space were obtained: R, G, B, L *, a *, b *, H, S, V, NR, NG, NB, Cr, Cg, Cb, I1, I2, I3, and gray.…”

Section: Image Preprocessingmentioning

confidence: 99%

“…It provides the spatial relationship between image pixels. Then, the energy, entropy, correlation, homogeneity [31][32][33][34][35], and contrast features were extracted.…”

Section: Image Preprocessingmentioning

confidence: 99%

“…where P d is the co-occurrence matrix, P(i, j) is the color values at i column and j row, N g is the number of pixels, and σ i,j is the standard deviation associated with i column and j row. The values of eccentricity, elongation, roundness, [31][32][33][34][35], centroid length, length of the small and large axes, and length of the large axes as shape features were calculated.…”

Section: Image Preprocessingmentioning

confidence: 99%

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Detection of Unripe Kernels and Foreign Materials in Chickpea Mixtures Using Image Processing

2022

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The existence of dockage, unripe kernels, and foreign materials in chickpea mixtures is one of the main concerns during chickpea storage and marketing. Novel algorithms based on image processing were developed to detect undesirable, foreign materials, and matured chickpea kernels in the chickpea mixture. Images of 270 objects including 54 sound samples and 36 samples of each undesired object were prepared and features of these acquired images were extracted. Different models based on linear discriminant analysis (LDA), support vector machine (SVM), and artificial neural networks (ANN) methods were developed by using MATLAB. Three classification algorithms based on LDA, SVM, and ANN methods were developed. The classification accuracy in training, testing, and overall detection showed the superiority of ANN (99.4, 92.6, and 94.4%, respectively) and LDA (91.1, 94.0, and 91.9%, respectively) over the SVM (100, 53.7, and 88.5%, respectively). The developed image processing technique can be incorporated with a vision-based real-time system.

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Section: Image Preprocessingmentioning

confidence: 99%

“…It provides the spatial relationship between image pixels. Then, the energy, entropy, correlation, homogeneity [31][32][33][34][35], and contrast features were extracted.…”

Section: Image Preprocessingmentioning

confidence: 99%

Section: Image Preprocessingmentioning

confidence: 99%

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Detection of Unripe Kernels and Foreign Materials in Chickpea Mixtures Using Image Processing

2022

Self Cite

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“…Some more works can be found in publications in Chinese ( Zhang et al, 2021 ). In addition, there are also some related studies have been carried on the recognition of medicinal plants, branches, and leaves ( Sabu et al, 2017 ; Azadnia and Kheiralipour, 2021 ; Tassis et al, 2021 ) with similar image information.…”

Section: Deep Learning For the Chinese Herbal Slices Image Recognitionmentioning

confidence: 99%

Combined Channel Attention and Spatial Attention Module Network for Chinese Herbal Slices Automated Recognition

Wang

et al. 2022

Front. Neurosci.

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Chinese Herbal Slices (CHS) are critical components of Traditional Chinese Medicine (TCM); the accurate recognition of CHS is crucial for applying to medicine, production, and education. However, existing methods to recognize the CHS are mainly performed by experienced professionals, which may not meet vast CHS market demand due to time-consuming and the limited number of professionals. Although some automated CHS recognition approaches have been proposed, the performance still needs further improvement because they are primarily based on the traditional machine learning with hand-crafted features, resulting in relatively low accuracy. Additionally, few CHS datasets are available for research aimed at practical application. To comprehensively address these problems, we propose a combined channel attention and spatial attention module network (CCSM-Net) for efficiently recognizing CHS with 2-D images. The CCSM-Net integrates channel and spatial attentions, focusing on the most important information as well as the position of the information of CHS image. Especially, pairs of max-pooling and average pooling operations are used in the CA and SA module to aggregate the channel information of the feature map. Then, a dataset of 14,196 images with 182 categories of commonly used CHS is constructed. We evaluated our framework on the constructed dataset. Experimental results show that the proposed CCSM-Net indicates promising performance and outperforms other typical deep learning algorithms, achieving a recognition rate of 99.27%, a precision of 99.33%, a recall of 99.27%, and an F1-score of 99.26% with different numbers of CHS categories.

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“…Other CNN models were also used for disease detection and classification of plant leaf images such as ResNet (Fuentes et al, 2017;Karthik et al, 2020), EfficientNet (Sahu et al, 2021), YOLO-v3 (Temniranrat et al, 2021), and DenseNet (Ezat et al, 2020). An imagining and artificial neural network-based work have been done for the recognition of six different species of medicinal plants, in which different image processing algorithms were used for extraction of colour, shape and texture features for classification (Azadnia & Kheiralipour, 2021). These deep learning models give higher accuracy but still, there is a requirement for the analysis of its performance and hyperparameters to get better results which can help other researchers to select an optimum set of hyperparameters with high efficacy.…”

Section: Related Workmentioning

confidence: 99%

Deep neural network for multi‐class classification of medicinal plant leaves

2022

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Plant diseases are a critical issue in the farming industry, and early identification is essential for plant monitoring. The leaves of plants represent the majority of disease symptoms, however, leaf analysis by specialists in laboratories is expensive and time‐consuming. Hence, there is a necessity for automated and more accurate plant disease detection techniques which can help in diagnosing early symptoms to turn down the economic loss. A deep‐learning‐based technique for detecting and classifying plant diseases from leaf images is provided in this research. A diversified image dataset of plant leaves with 12 distinct crops in 22 different categories was used for this work. Several intra‐class and inter‐class variations in the training dataset make it more complex and challenging to train a deep‐learning model. An exhaustive analysis of different deep neural networks has been done with different combinations of optimizers and learning rates. Additionally, five‐fold cross‐validation and testing on separate test images have been done for a detailed investigation of the trained model in different statistical parameters. The proposed approach extended the results to an average cross‐validation accuracy of 98.68%, and average test accuracy of 97.69% is obtained on unseen images having intra‐class and inter‐class variations.

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Recognition of leaves of different medicinal plant species using a robust image processing algorithm and artificial neural networks classifier

Cited by 32 publications

References 51 publications

Detection of Unripe Kernels and Foreign Materials in Chickpea Mixtures Using Image Processing

Detection of Unripe Kernels and Foreign Materials in Chickpea Mixtures Using Image Processing

Combined Channel Attention and Spatial Attention Module Network for Chinese Herbal Slices Automated Recognition

Deep neural network for multi‐class classification of medicinal plant leaves

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