Automatic Leaf Vein Feature Extraction for First Degree Veins

Chakkaravarthy, S. Sibi; Sajeevan, G.; Kamalanaban, E.; Kumar, K. A. Varun

doi:10.1007/978-3-319-28658-7_49

Cited by 11 publications

(3 citation statements)

References 19 publications

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“…With the development of image processing techniques, more and more machine learningbased algorithms have been employed for leaf vein segmentation. Sibi Chakkaravarthy et al [7] used the angle between the main vein and secondary veins and the centroid vein angle as key parameters, combined with Hough lines, to extract the leaf veins. Radha et al [8] utilized the canny edge detection algorithm to determine the nutritional status of plants by detecting the leaf edges and veins.…”

Section: Introductionmentioning

confidence: 99%

TTH-Net: Two-Stage Transformer–CNN Hybrid Network for Leaf Vein Segmentation

Song,

Yu,

Zhang

2023

Applied Sciences

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Leaf vein segmentation is crucial in species classification and smart agriculture. The existing methods combine manual features and machine learning techniques to segment coarse leaf veins. However, the extraction of the intricate patterns is time consuming. To address the issues, we propose a coarse-to-fine two-stage hybrid network termed TTH-Net, which combines a transformer and CNN to accurately extract veins. Specifically, the proposed TTH-Net consists of two stages and a cross-stage semantic enhancement module. The first stage utilizes the Vision Transformer (base version) to extract globally high-level feature representations. Based on these features, the second stage identifies fine-grained vein features via CNN. To enhance the interaction between the two stages, a cross-stage semantic enhancement module is designed to integrate the strengths of the transformer and CNN, which also improves the segmentation accuracy of the decoder. Extensive experiments on the public dataset LVN are conducted, and the results prove that TTH-Net has significant advantages over other methods in leaf vein segmentation.

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Section: Introductionmentioning

confidence: 99%

TTH-Net: Two-Stage Transformer–CNN Hybrid Network for Leaf Vein Segmentation

Song,

Yu,

Zhang

2023

Applied Sciences

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“…Parts of plants that have different characteristics from each plant and commonly used for plant identification are leaves [4][5]. Leaves have an important role for plants to adapt to their environment [6].…”

Section: Introductionmentioning

confidence: 99%

“…Scale Projection (P i,j ) is a measurement of projection length between coincident segments. In Figure 4, the projected length between segments i and j is calculated using ( 4) and (5).…”

mentioning

confidence: 99%

Plant species identification based on leaf venation features using SVM

Ambarwari¹,

Adrian

Herdiyeni³

et al. 2020

TELKOMNIKA

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The purpose of this study is to identify plant species using leaf venation features. Leaf venation features were obtained through the extraction of leaf venation features. The leaf image segmentation was performed to obtain the binary image of the leaf venation which is then determined the branching point and ending point. From these points, the extraction of leaf venation feature was performed by calculating the value of straightness, a different angle, length ratio, scale projection, skeleton length, number of segments, total skeleton length, number of branching points and number of ending points. So that from the extraction of leaf venation features 19 features were obtained. Identification of plant species was carried out using Support Vector Machine (SVM) with RBF kernel. The learning model was built using 75% of the training data. The testing results using 25% of the data on the training model, obtained an accuracy of 82.67%, with an average of precision of 84% and recall of 83%.

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