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.
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.
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.
IEEE Access Special Section Editorial: Advanced Data Mining Methods for Social Computing