Abstract:
Plant disease classification is essential for
food productivity and disease diagnosis in
agricultural domain. The probability distribution and
statistical properties are essential in image
processing to define the features of typical image.
The general usage of (Scale Invariant Feature
Transform) SIFT has local feature extraction and
global feature extraction (bag-Of-Features
approach) for classification, and its classification
result for unknown data also depends on code book
(global feature) generation. Instead of using bag-Of-
Feature approach, we proposed to apply Beta
probability distribution model for SIFT to be directly
represent the image information and then formed
SIFT-Beta. The color statistics feature is extracted
from RGB color space and then combines with SIFTBeta
to produce proposed features. The proposed
feature is applied in Support Vector Machine
classifier. The classifier is trained for seven labels of
tomato with six diseases and healthy.
