Identificaion of Leaf Disease Using Machine Learning Algorithm for Improving the Agricultural System


Keerthi Kethineni, 
Research Scholor, Koneru Lakshmaiah Education Foundation, Vaddeswaram, Guntur, India
Assistant Professor, Department of CSE,V R Siddhartha Engineering College, Vijayawada,India.

G Pradeepini,
Dept of CSE, Koneru Lakshmaiah Education Foundation, Vaddeswaram, Guntur, India.


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.