Grape leaf segmentation for disease identification through adaptive Snake algorithm model

Shantkumari, M.; Uma, S.

doi:10.1007/s11042-020-09853-y

Cited by 28 publications

(7 citation statements)

References 16 publications

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“…e rest are used as background. In actual operation, the threshold can also be selected according to experience to achieve the best effect [23,32,33]. Figure 7 shows the EXG image of grape leaves.…”

Section: Segmentation Of Grape Leaf Lesions Based Onmentioning

confidence: 99%

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Grape Leaf Disease Classification Combined with U-Net++ Network and Threshold Segmentation

Wang

et al. 2022

Computational Intelligence and Neuroscience

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Applying the method of semantic segmentation to the segmentation of grape leaves is an important method to solve how to segment grape leaves from complex backgrounds. This article uses U-net++ convolutional neural network to segment grape leaves from complex backgrounds using MIOU, PA, and mPA as evaluation metrics. After the leaves are segmented, the OTSU threshold segmentation + EXG algorithm is used to extract the diseased spots of grape leaves and healthy grape leaves by increasing the proportion of green vectors. Grape leaf disease was automatically graded by the ratio of the healthy green part of the grape to the total leaf area.

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Section: Segmentation Of Grape Leaf Lesions Based Onmentioning

confidence: 99%

“…e current grape leaf grading is divided into six grades according to the percentage of disease spots in the leaf area, and the grape leaf disease application program is automatically graded according to the grading table [32]. ere are traditional methods used in the study of disease classification of plant leaves.…”

Section: Grape Leaf Disease Classificationmentioning

confidence: 99%

Grape Leaf Disease Classification Combined with U-Net++ Network and Threshold Segmentation

Wang

et al. 2022

Computational Intelligence and Neuroscience

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“…Many methods for segmenting images have been introduced, including region growth, division, integration, neural networks (ANN), active contour (ACM) models, etc [18]. Active contour models are recognized as one of the most *Corresponding Author Institutional Email: y.baleghi@nit.ac.ir (Y. Baleghi Damavandi) successful methods for segmenting images [19,20]. Active contour models have many advantages over other algorithms [21,22].…”

Section: Introductionmentioning

confidence: 99%

A New Texture Segmentation Method with Energy-driven Parametric Active Contour Model Based on Jensen-Tsallis Divergence

2022

IJE

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Texture image segmentation plays an important role in various computer vision tasks. Active contour models are one of the most efficient and popular methods for identifying the purpose and segmentation of objects in the image. This paper presents a parametric active contour model (PACM) with a robust minimization framework based on image texture energy. First, the texture features of the original image are extracted using gray level co-occurrence matrix (GLCM). Subsequently, based on the GLCM texture features inside and outside the active contour, Jensen-Tsallis divergence of energies is calculated. The Jensen-Tsallis divergence is added to the parametric active contour using the balloon equation. The divergence is maximum at the boundary between the foreground and background of the image, which results in minimizing the active contour equation at the boundary of the target object. This global minimization energy function with texture feature can avoid the existence of local minima in the PACM models. Also, as opposed to previous models, the proposed model only requires the initial contour and is not dependent on the distance of the initial contour from the target object. In terms of segmentation accuracy and efficiency, experiments with synthetic and natural images demonstrate that the proposed approach obtains more satisfactory results than the previous state-of-the-art methods.

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“…In any case, the intricacy was rather significant. Shantkumari et al [14] segments illumination-invariant data to research a grape picture using an adaptive snake model. Utilizing several types of methods, the vegetation was extracted from the plant in the photograph.…”

Section: Introductionmentioning

confidence: 99%

Grape Leaves Segmentation Using an Improved Graph-Based Approach

Narasimman¹,

Thiyagarajan²

2022

RIA

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The study of plant pictures is very beneficial to agriculture and is sustainable. It is used to precisely and often record data on plant development, yield, breadth, and height of the plant, leaf area, etc. The quantity of leaves in the plants directly affects plant development, which is one of the most important characteristics to be examined among these plant characteristics. To extract leaves from photos of grape plants, a novel technique known as an enhanced graph-based approach is proposed in this study. The suggested procedure comprises two phases. Red, Green, and Blue (RGB) to Hue, Saturation, and Value (HSV) conversion and background removal are required in the initial phase for picture improvement. In the second stage, a novel graph-based technique and the Circular Hough Transform (CHT) are used to extract the leaf area from the plant picture. Theni District, Tamilnadu, India's grape leaf real-time datasets have been used in the experimentation for the suggested study. The segmentation pixel accuracy of the suggested approach is 92.4%, and the Mean Intersection over Union (MIoU) value is 86.2%, which is superior to the methods currently in use.

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Grape leaf segmentation for disease identification through adaptive Snake algorithm model

Cited by 28 publications

References 16 publications

Grape Leaf Disease Classification Combined with U-Net++ Network and Threshold Segmentation

Grape Leaf Disease Classification Combined with U-Net++ Network and Threshold Segmentation

A New Texture Segmentation Method with Energy-driven Parametric Active Contour Model Based on Jensen-Tsallis Divergence

Grape Leaves Segmentation Using an Improved Graph-Based Approach

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