In agriculture the major problem is leaf disease
identifying these disease in early stage increases the yield. To
reduce the loss identifying the various disease is very important.
In this work , an efficient technique for identifying unhealthy
tomato leaves using a machine learning algorithm is proposed.
Support Vector Machines (SVM) is the methodology of machine
learning , and have been successfully applied to a number of
applications to identify region of interest, classify the region. The
proposed algorithm has three main staggers, namely preprocessing, feature extraction and classification. In preprocessing, the images are converted to RGB and the average
filter is used to eliminate the noise in the input image. After the
pre-processing stage, features such as texture, color and shape
are extracted from each image. Then, the extracted features are
presented to the classifier to classify an input tomato leaf as a
healthy or unhealthy image. For classification, in this paper, a
multi-kernel support vector machine (MKSVM) is used. The
performance of the proposed method is analysed on the basis of
different metrics, such as accuracy, sensitivity and specificity.
The images used in the test are collected from the plant village.
The proposed method implemented in MATLAB.
An end-to-end graph convolutional kernel support vector machine
