Indian Journal of Innovations and Developments

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SOM Based Clustering for Detecting Bacterial Spot Disease in Tomato Field


Affiliations
1 Department of Computer Applications, Karpagam University, Coimbatore, Tamil Nadu, India
 

Objectives: The main objective of introducing SOM based clustering method is to improve the classification accuracy and detection of bacterial spot disease in tomato field.

Methods: There are various image processing methods used to identify disease and severity of disease in plants. One of such methods uses visible spectrum Images for automatically detecting and classifying the severity of bacterial spot in tomato fields. Centroid-based K-means clustering was widely used for automatic segmentation.

Findings: Plant diseases are one of the major responsibilities for economic degradation in the agricultural industry. So regular monitoring of plant health and early detection of disease causing pathogens are required for minimizing disease spread and assist effective management practices. Centroid-based K-means clustering for segmentation always does not chose centroids that provide best results and also different initial set of centroids affect the shape and effectiveness of the final cluster.

Application/Improvements: To overcome the limitations of Centroid-based K-means clustering, Self-Organizing Maps (SOM) is introduced for achieving effective classification result and to improve the detection performance.


Keywords

Plant Diseases, Visible Spectrum Images, Centroid-Based K-Means Clustering, Self-Organizing Maps.
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  • D. L. Borges, S. T. D. M. Guedes, A. R. Nascimento, P. Melo-Pinto. Detecting and grading severity of bacterial spot caused by Xanthomonas spp. in tomato (Solanum lycopersicum) fields using visible spectrum images. Computers and Electronics in Agriculture, 2016; 125, 149-159.

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  • SOM Based Clustering for Detecting Bacterial Spot Disease in Tomato Field

Abstract Views: 250  |  PDF Views: 0

Authors

X. Agnes Kala Rani
Department of Computer Applications, Karpagam University, Coimbatore, Tamil Nadu, India
R. Nagaraj
Department of Computer Applications, Karpagam University, Coimbatore, Tamil Nadu, India

Abstract


Objectives: The main objective of introducing SOM based clustering method is to improve the classification accuracy and detection of bacterial spot disease in tomato field.

Methods: There are various image processing methods used to identify disease and severity of disease in plants. One of such methods uses visible spectrum Images for automatically detecting and classifying the severity of bacterial spot in tomato fields. Centroid-based K-means clustering was widely used for automatic segmentation.

Findings: Plant diseases are one of the major responsibilities for economic degradation in the agricultural industry. So regular monitoring of plant health and early detection of disease causing pathogens are required for minimizing disease spread and assist effective management practices. Centroid-based K-means clustering for segmentation always does not chose centroids that provide best results and also different initial set of centroids affect the shape and effectiveness of the final cluster.

Application/Improvements: To overcome the limitations of Centroid-based K-means clustering, Self-Organizing Maps (SOM) is introduced for achieving effective classification result and to improve the detection performance.


Keywords


Plant Diseases, Visible Spectrum Images, Centroid-Based K-Means Clustering, Self-Organizing Maps.

References