A New Leaf Venation Detection Technique for Plant Species Classification

Kolivand, Hoshang; Fern, Bong Mei; Saba, Tanzila; Rahim, Mohd Shafry Mohd; Rehman, Amjad

doi:10.1007/s13369-018-3504-8

Cited by 34 publications

(8 citation statements)

References 22 publications

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“…RI: The RI is a measure of similarity between two groups of clusters, that is, between the original segmented image (S original) and the ground truth image (S ground truth). Equation (13) measures the similarity between the two segments. The RI should be at a maximum.…”

Section: Gcementioning

confidence: 99%

“…Souza et al (2016) [12] used multiscale bending energy (MBE) descriptors to classify plants, applying optimization techniques for feature selection, given that the MBE provides numerous features at different scales. Kolivand et al (2019) [13] employed venation-based morphological features, though the venation patterns are undefined in some leaves and the computational time of the venation feature is high. Turkoglu et al (2019) [14] exploited texture features using a local binary pattern of the red and green pixels of the leaves.…”

Section: Introductionmentioning

confidence: 99%

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Classifying Indian Medicinal Leaf Species Using LCFN-BRNN Model

2021

KSII TIIS

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Section: Gcementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Classifying Indian Medicinal Leaf Species Using LCFN-BRNN Model

2021

KSII TIIS

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“…These stages include canny edge detection, leaf boundary removal, curve extraction, produce hue image normalization, and finally, image fusion. Outcomes showed 91.06% accuracy for the Acer dataset (Kolivand, Fern, Saba, Rahim, & Rehman, 2019).…”

Section: Introductionmentioning

confidence: 99%

Piper Plant Classification using Deep CNN Feature Extraction and Hyperparameter Tuned Random Forest Classification

Pravin

C.Deepa²

2022

tjes

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The plant has numerous uses in medicine, food, and industry and plays a major role in environmental protection. Hence it is crucial to identify and classify the specific plant species. In the agriculture production and botanical area, plant classification for images of leaves is considered the basic research. Due to the higher dimensionality and nature complexity of leaf image data, various effective algorithms are required to perform the classification of specific plant species. Hence, in this study, all plant types and specific piper plant types are considered and classified based on the Hyperparameter tuned random forest algorithm due to its effective optimal hyperparameter tuning. Piper plants are selected in this research since they possess significant medicinal applications. Significantly the Deep CNN approach is considered an effective feature extraction of all plants such as tomato, apple, cherry, and others and also piper plants like piper mulesa, piper nigrum, and others. However, initially the effective pre-processing of data augmentation to reduce overfitting and increase the amount of data and feature scaling for data features normalization are established. The experimental results show that the proposed hyperparameter tuned random forest classifier shows better results of showing an accuracy value of 0.94 for all plants and 0.88 value for piper plant compared with other machine learning algorithms like SVM, naïve Bayes, and Logistic regression.

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“…At present, with the development of image technology, using computer technology to automatically classify leaf characters after feature extraction has become the mainstream method [6]. In the research of related fields, the characteristics of leaves are usually extracted by hand, For example, leaves are often classified by using the shape differences between different leaves [7], [8], Leaf edges, as an important feature, are often used as extraction targets [9], In addition, using the technology of leaf vein texture feature detection [10], singular value decomposition (SVD) and sparse representation (SR) are combined to process dimensionally reduced plant images [11], the moment invariant method for multicomponent shapes [12] and artificial neural network with support vector machine [13] have also been successful to some extent.…”

Section: Introductionmentioning

confidence: 99%

Plant Leaves Classification: A Few-Shot Learning Method Based on Siamese Network

Wang

Dian

2019

IEEE Access

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In recent years, the method of plant leaf classification by deep learning has gradually become mature. However, training a leaf classifier based on deep learning requires a large number of samples for supervised training. In this paper, a few-shot learning method based on the Siamese network framework is proposed to solve a leaf classification problem with a small sample size. First, the features of two different images are extracted by a parallel two-way convolutional neural network with weight sharing. Then, the network uses a loss function to learn the metric space, in which similar leaf samples are close to each other and different leaf samples are far away from each other. In addition, a spatial structure optimizer (SSO) method for constructing the metric space is proposed, which will help to improve the accuracy of leaf classification. Finally, a k-nearest neighbor (kNN) classifier is used to classify leaves in the learned metric space. The average classification accuracy is used as a performance measure. The open access Flavia, Swedish and Leafsnap datasets are used to evaluate the performance of the method. The experimental results show that the proposed method can achieve a high classification accuracy with a small size of supervised samples.

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A New Leaf Venation Detection Technique for Plant Species Classification

Abstract: The version presented here may differ from the published version or from the version of the record. Please see the repository URL above for details on accessing the published version and note that access may require a subscription.

Cited by 34 publications

References 22 publications

Classifying Indian Medicinal Leaf Species Using LCFN-BRNN Model

Classifying Indian Medicinal Leaf Species Using LCFN-BRNN Model

Piper Plant Classification using Deep CNN Feature Extraction and Hyperparameter Tuned Random Forest Classification

Plant Leaves Classification: A Few-Shot Learning Method Based on Siamese Network

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