Multi-resolution mobile vision system for plant leaf disease diagnosis

Prasad, Shitala; Peddoju, Sateesh K.; Ghosh, Debashis

doi:10.1007/s11760-015-0751-y

Cited by 101 publications

(42 citation statements)

References 26 publications

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“…It is bilinear interpolation because in this technique value of four adjacent pixel are averaged to discover the novel pixel value. [11] = 1 + 2 + 3 + 4 4 where P is the new pixel value. Q1,Q2,Q3 and Q4 are four neighboring pixels.…”

Section: B Image Pre-processingmentioning

confidence: 99%

Recognition of Diseases of Leaf using SVM with Radial Basis Kernel Function

Sharma¹

2019

IJRASET

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Organic farming obtains admiration in agricultural industry. Several complications get up during farming wherein the disease affected leaf contemplated to be very powerful aspect for the shortage of agricultural products and also upshot the quality of product. The target is to diminish this problem through machine learning technique. This research showcased a methodology for Recognition and Grouping of diseased leaf and healthy leaf by Support vector machine(SVM) classifier with radial basis function. The texture features are take out from the diseased leaf and from the Healthy Leaf images separately for the grouping/classification. Here, we cast-off SVM classifier with RBF will group/classify the ailments such as Cercospora leaf spot,Bacterial blight, Anthracnose, Alternaria alternata, and healthy leaves of several species of plants. Emphasis is given on the initial recognition of diseases.

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Section: B Image Pre-processingmentioning

confidence: 99%

Recognition of Diseases of Leaf using SVM with Radial Basis Kernel Function

Sharma¹

2019

IJRASET

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“…The following literature survey exhibit the approach. In "Multi-resolution mobile vision system for plant leaf disease diagnosis" [7] the authors Shitala Prasad , Sateesh K. Peddoju and Debashis Ghosh proposed a mobile clientserver architecture for Plant leaf disease detection and diagnosis using a novel combination of Gabor wavelet transform (GWT) and gray level co-occurrence matrix (GLCM). The system is a symbol of diseased patch in multi-resolution and multi-direction feature vector.…”

Section: Fig-1 22 On-device Segmentation Approachesmentioning

confidence: 99%

A Survey and Research Proposal of Plant Leaf Disease Diagnosis and Classification on Mobile Devices

Nagarathinam¹,

Kumar²

2016

IOSR

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“…Our work mainly concentrates on proposing the enhanced texture feature fusion which combines SIFT and WSFTA, once after fusing the algorithms it is very important to perform selection process. Selection is done using PCA method to avoid dense feature vector creation that elaborates the vector length causing high computational cost [19]. The selection process results in the accurate and required features.…”

Section: Introductionmentioning

confidence: 99%

Automatic Detection of Plant Disease and Insect Attack using EFFTA Algorithm

Gangadharan¹,

Rosline²,

Dhanasekaran³

et al. 2020

IJACSA

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The diagnosis of plant disease by computer vision using digital image processing methodology is a key for timely intervention and treatment of healthy agricultural procedure and to increase the yield by natural means. Timely addressal of these ailments can be the difference between the prevention and perishing of an ecosystem. To make the system more efficient and feasible we have proposed an algorithm called Enhanced Fusion Fractal Texture Analysis (EFFTA). The proposed method consists of Feature Fusion technique which combines SIFT-Scale Invariant Feature Transform and DWT-Discrete Wavelet Transform based SFTA-Segment Based Fractal Texture Analysis. Image as a whole can be detected by shape, texture and color. SIFT is used to detect the texture feature, it extracts the set of descriptors that is very useful in local texture recognition and it captures accurate key points for detecting the diseased area. Further extraction of texture is considered and that can be performed by WSFTA method. It adopts intra-class analysis and inter-class analysis. Extracted features trained using Back Propagation Neural Network. It improves and expands the success rate and accuracy of extraction also it provides higher precision and efficiency when compared to the other traditional methods.

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Multi-resolution mobile vision system for plant leaf disease diagnosis

Cited by 101 publications

References 26 publications

Recognition of Diseases of Leaf using SVM with Radial Basis Kernel Function

Recognition of Diseases of Leaf using SVM with Radial Basis Kernel Function

A Survey and Research Proposal of Plant Leaf Disease Diagnosis and Classification on Mobile Devices

Automatic Detection of Plant Disease and Insect Attack using EFFTA Algorithm

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