ICTACT Journal on Soft Computing

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Plant Disease Recognition and Clustering Using Fuzzy Algorithm on Data Mining


Affiliations
1 Department of Electronics and Communication Engineering, Hindustan College of Engineering and Technology, India
2 Department of Computer Science and Engineering, IES College of Engineering, India
     

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Due to large size and intensive processing needs, deep learning models are not suited for mobile and handheld devices. Our goal is to develop a process that begins with pre-processing, diagnoses diseased leaf areas, uses the GLCM to choose and classify features, and culminates in a conclusion. We developed fuzzy decision methods for assigning photos of common rust to various severity levels, using data on diseased leaf regions isolated by threshold segmentation. The outcomes of these experiments were determined by six different colour and texture attributes. In plant disease clustering, the Fuzzy Algorithm is utilised. The test results demonstrate that the new method is more efficient than the conventional approaches and ranks first for feature extraction techniques. This appears to say that plant disease diagnosis using leaves should be utilised. Additional disease classifications or crop/disease classifications can be added to define these capabilities.

Keywords

Plant Disease, Plant Leaf, Recognition, Clustering.
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  • Plant Disease Recognition and Clustering Using Fuzzy Algorithm on Data Mining

Abstract Views: 210  |  PDF Views: 2

Authors

R. Sabitha
Department of Electronics and Communication Engineering, Hindustan College of Engineering and Technology, India
G. Kiruthiga
Department of Computer Science and Engineering, IES College of Engineering, India

Abstract


Due to large size and intensive processing needs, deep learning models are not suited for mobile and handheld devices. Our goal is to develop a process that begins with pre-processing, diagnoses diseased leaf areas, uses the GLCM to choose and classify features, and culminates in a conclusion. We developed fuzzy decision methods for assigning photos of common rust to various severity levels, using data on diseased leaf regions isolated by threshold segmentation. The outcomes of these experiments were determined by six different colour and texture attributes. In plant disease clustering, the Fuzzy Algorithm is utilised. The test results demonstrate that the new method is more efficient than the conventional approaches and ranks first for feature extraction techniques. This appears to say that plant disease diagnosis using leaves should be utilised. Additional disease classifications or crop/disease classifications can be added to define these capabilities.

Keywords


Plant Disease, Plant Leaf, Recognition, Clustering.

References