The diseases in the Brinjal can be identified through the symptoms occur in Brinjal leaf. The indication in touch difference bin of various plant diseases. The designation of disease detection need the specialist's opinion. The inappropriate identification can result in tremendous quantity of economic loss for farmers. Rather than manual identification, computers are accustomed to give automatic detection and classifying differing kinds of diseases. In this paper, lesion areas affected by diseases are segmented using different techniques, namely DeltaE, Otsu, FCM, k-means algorithm are employed. The proposed method is the image blend by discrete wavelet transforms to increase the excellence of image and reduce uncertainty and redundancy for identification and assessment of agricultural yield which can be done by DeltaE. Further color, texture and structural based features are mixed collectively for getting better performance when compared with single feature extraction.
Segmentation separates an image into different
sections badsed on the desire of the user. Segmentation will be
carried out in an image, until the region of interest (ROI) of an
object is extracted. Segmentation reliability predicts the progress
of the various segmentation techniques. In this paper, various
segmentation methods are proposed and quality of segmentation
is verified by using quality metrics like Mean Squared Error
(MSE),Signal to Noise Ratio (SNR), Peak- Signal to Noise Ratio
(PSNR), Edge Preservation Index (EPI) and Structural
Similarity Index Metric (SSIM).
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