Plant Specie Classification Using Sinuosity Coefficients of Leaves

Kala, Jules Raymond; Viriri, Serestina

doi:10.5566/ias.1821

Cited by 10 publications

(6 citation statements)

References 12 publications

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“…Plants exhibit substantial genotypic diversity during the developmental processes. Leaves are one of the most visible and vital organs, with genotypic variations in shape, colour, margin and texture ( Bruno et al , 2008 ), and this valuable morphological information can help identify species ( Arturo et al , 2015 ; Kala and Viriri, 2018 ). In this study, leaves from the six provenances differed in shape, colour and area.…”

Section: Discussionmentioning

confidence: 99%

Plant growth, physiological variation and homological relationship of Cyclocarya species in ex situ conservation

Feng

Zheng²,

Lin³

et al. 2022

Conservation Physiology

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Natural forests of Cyclocarya paliurus have been seriously damaged because of the extreme demand for leaf medicinal uses, making conservation of this valuable, medicinal woody species necessary. Because of geographical differentiation and diverse adaptability, in this study we analysed the variations in plant growth and physiological response to environmental factors at a resource plantation of ex situ conservation and determined the homological relationships between local provenance (from Fujian Province, FJ) and introduced provenances showing high-survival rate and better growth (from Zhejiang, Hubei, Guizhou and Jiangxi Province). Our results suggested the following: (i) Plant growth: FJ had the highest plant height but not the largest basal diameter in comparison to that of other provenances. (ii) Physiological responses during the growth periods: water content in leaf of FJ had similar change with that of other provenances, except for the provenance from Guizhou Province; total soluble sugar content in leaf of FJ was more than that of other provenances; calcium content in leaf of all provenances was higher as compared to K, Mg and Na; the highest activity among four kinds of antioxidant enzymes in all provenances was superoxide dismutase, then was polyphenol oxidase and peroxidase, finally was catalase; and total flavonoid among three kinds of secondary metabolites in all provenances showed the greatest content, followed by polysaccharides and total triterpenoid. (iii) Relation analysis: plant growth and physiological responses related with environmental factors, especially temperature and precipitation. (iv) Homological relationships: leaf characteristics among six provenances varied in colour, area and common petiole length, but not the shape of leaf base or apex. Cyclocarya paliurus distributed in Fujian Province showed a very close homological relationship with that distributed in Zhejiang Province by simple sequence repeat. These findings will provide knowledge on physiological response to environmental factors and aid to select suitable provenances for Cyclocarya cultivation.

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Section: Discussionmentioning

confidence: 99%

Plant growth, physiological variation and homological relationship of Cyclocarya species in ex situ conservation

Feng

Zheng²,

Lin³

et al. 2022

Conservation Physiology

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“…Ambarwari et al [3] called SVM to classify plant species using leaf venation features and achieved an accuracy of 82.6% with a precision of 84% and recall of 83%. Furthermore, Chaki et al [4] experimented on bag-of-features, fuzzy-color, and edge-texture histogram descriptors of a multi-layer perceptron for a fragmented leaf recognition problem, and Kala and Viriri [5] classified plant species based on the sinuosity coefficients of leaves. Moreover, Bao et al [6] used a convolutional neural network (CNN) over histogram-of-oriented-gradients features with the SVM classifier on the Swedish and Flavia datasets.…”

Section: Introductionmentioning

confidence: 99%

Convolutional Neural Networks for Herb Identification: Plain Background and Natural Environment

Chaivivatrakul¹,

Moonrinta²,

Chaiwiwatrakul³

2022

International Journal on Advanced Science, Engineering and Information Technology

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Convolutional neural networks have achieved success in resolving object identification problems. This study contributes a suitable new approach to herb identification for educational and research purposes based on a small dataset and small-sized images. Two self-collected Thai herb datasets with either plain or natural environment backgrounds were used for experimentation to realize this objective. The plain background dataset includes 4,400 images of 11 leaf types, and the natural dataset contains 1,620 images of nine leaf types. The images were divided into a training set containing 75% of the images and a separate test set with the remaining 25%. The experiments included five-fold cross-validation applied to the training set; the InceptionV3, MobileNetV2, ResNet50V2, VGG16, and Xception convolutional neural network models RMSprop and Adam optimizers. Further, dropout rates of 0.3, 0.5, and 0.7 were considered along with five and ten epochs. Transfer learning was applied using pre-trained weights. The model with the best outcome, based on the average accuracy of the cross-validation results on both datasets (the plain background dataset was 94.55%, and the natural dataset was 90.37%), was the VGG16 with the RMSprop optimizer, which exhibited a dropout rate of 0.5 over ten epochs. The model achieved 96.64% and 92.00% accuracy on the plain background training and test sets, and 99.59% and 91.36% on the natural environment training and test sets, respectively. The results show that the method has a high potential for objective tasks and application in identifying herbs based on visual leaf information.

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“…In 2012, Kumar et al designed a mobile application Leafsnap, where histograms of curvature over scale (HoCS) [3] as a single (shape) feature was employed for plant identification. Other shape features are also used for leaf recognition, such as centroid-contour distance (CCD) [4], aspect ratio [5], Hu invariant moments [6], polar Fourier transform (PFT) [6], inner distance shape context (IDSC) [7], sinuosity coefficients [8], multiscale region transform (MReT) [9], etc. However, some leaves from different kinds of plants are very similar; the shapes of those leaves even cannot be differentiated by the naked eye.…”

Section: Introductionmentioning

confidence: 99%

Leaf Image Recognition Based on Bag of Features

et al. 2020

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Plants are ubiquitous in human life. Recognizing an unknown plant by its leaf image quickly is a very interesting and challenging research. With the development of image processing and pattern recognition, plant recognition based on image processing has become possible. Bag of features (BOF) is one of the most powerful models for classification, which has been used for many projects and studies. Dual-output pulse-coupled neural network (DPCNN) has shown a good ability for texture features in image processing such as image segmentation. In this paper, a method based on BOF and DPCNN (BOF_DP) is proposed for leaf classification. BOF_DP achieved satisfactory results in many leaf image datasets. As it is hard to get a satisfactory effect on the large dataset by a single feature, a method (BOF_SC) improved from bag of contour fragments is used for shape feature extraction. BOF_DP and LDA (linear discriminant analysis) algorithms are, respectively, employed for textual feature extraction and reducing the feature dimensionality. Finally, both features are used for classification by a linear support vector machine (SVM), and the proposed method obtained higher accuracy on several typical leaf datasets than existing methods.

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Plant Specie Classification Using Sinuosity Coefficients of Leaves

Cited by 10 publications

References 12 publications

Plant growth, physiological variation and homological relationship of Cyclocarya species in ex situ conservation

Plant growth, physiological variation and homological relationship of Cyclocarya species in ex situ conservation

Convolutional Neural Networks for Herb Identification: Plain Background and Natural Environment

Leaf Image Recognition Based on Bag of Features

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