Double Line Clustering based Colour Image Segmentation Technique for Plant Disease Detection

Kalaivani       Subramani; Shantharajah       Periyasamy; Padma       Theagarajan

doi:10.2174/1573405614666180322130242

Abstract

Background: Agriculture is one of the most essential industry that fullfills people’s need and also plays an important role in economic evolution of the nation. However, there is a gap between the agriculture sector and the technological industry and the agriculture plants are mostly affected by diseases, such as the bacterial, fungus and viral diseases that lead to loss in crop yield. The affected parts of the plants need to be identified at the beginning stage to eliminate the huge loss in productivity.

Methods: In the present scenario, crop cultivation system depend on the farmers experience and the man power, but it consumes more time and increases error rate. To overcome this issue, the proposed system introduces the Double Line Clustering technique based disease identification system using the image processing and data mining methods. The introduced method analyze the Anthracnose, blight disease in grapes, tomato and cucumber. The leaf images are captured and the noise has been removed by non-local median filter and the segmentation is done by double line clustering method. The segmented part compared with diseased leaf using pattern matching algorithm.

Results: The efficiency of the system is implemented in tomato, grape, cucumber plants leaf images and the results are analyzed in terms of the error rate, sensitivity, specificity, accuracy and time.

Conclusion: The result of the clustering algorithm achieved high accuracy, sensitivity, and specificity. The feature extraction is applied after the clustering process which produces minimum error rate.

Keywords: Leaf images, crop disease, non-local median filter, double line clustering approach, deep and statistical features, adaptive neuro fuzzy neural network, akritean distance.

Graphical Abstract

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DOI https://dx.doi.org/10.2174/1573405614666180322130242	Print ISSN 1573-4056
Publisher Name Bentham Science Publisher	Online ISSN 1875-6603

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