Double Line Clustering based Colour Image Segmentation Technique for Plant Disease Detection.
Leaf images
adaptive neuro fuzzy neural network
akritean distance
crop disease
deep and statistical features
double line clustering approach
non-local median filter
Journal
Current medical imaging reviews
Titre abrégé: Curr Med Imaging Rev
Pays: United Arab Emirates
ID NLM: 101272516
Informations de publication
Date de publication:
Historique:
received:
24
08
2017
revised:
29
09
2017
accepted:
07
02
2018
entrez:
4
2
2020
pubmed:
6
2
2020
medline:
31
10
2020
Statut:
ppublish
Résumé
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. 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. 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. 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.
Sections du résumé
BACKGROUND
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
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
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
CONCLUSIONS
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.
Identifiants
pubmed: 32008544
pii: CMIR-EPUB-89257
doi: 10.2174/1573405614666180322130242
doi:
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
769-776Informations de copyright
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