The diagnosis of plant disease is the foundation for effective and accurate plant disease
prevention in a complicated environment. Smart farming is one of the fast growing processes in
agricultural system with identification of disease in plants being major one to help farmers. The
processed data is saved in database and is been used in making decisions in advance support,
analysis of plants and helps in crop planning. Plants are one of the most important resources for
avoiding global warming. However, diseases such as blast, canker, black spot, brown spot, and
bacterial leaf damage the plants. In this paper, an integration of image processing is developed in
identifying the type of disease and help in automatic process of inspection of all the leaf batches
by storing the processed data. In some places, farmers were unaware of the experts, and they do
not have proper facilities. In such conditions, one technique can be beneficial in keeping track
and monitoring of more number of crops. This technique makes it much easier and cheaper to
detect disease. Machine learning can provide a method and algorithm to detect the disease. There
should be the training of images of all types of leaves that include the ones that are healthy and
disease leaf images. Five stage detection processes is done in this paper. The stages are
preprocessing, segmentation using k-Mean, feature extraction, features optimization using firefly
optimization algorithm (FA) and finally classified using support vector machine (SVM). The rate
of accuracy achieved using proposed technique i.e. GA-SVM is 91.3%, sensitivity is 90.72%,
specificity 91.88 and precision is 92%. The results are evaluated using matlab software tool.
