International Journal of Innovative Research in Science , Engineering and Technology

Agricultural yield is mainly affected by differences in climatic conditions and seasonal diseases. There is a strong need of modern agricultural systems that identify disease in the crops. The goal of this study to design, develop and evaluate GA-SVM based methodology for automatic classification of pomegranate related diseases. This innovative method would be a boon to many and would have lot of advantages over the traditional method of grading. Presently, plant pathologists follow a tedious technique that mainly relies on naked eye prediction. Manual procedure is not only time consuming but also does not give accurate results. Studies show that relying on pure naked-eye observation of experts to detect and classify pomegranate related diseases can be prohibitively expensive, especially in developing countries. This methodology is fast and accurate in detecting diseases like bacterial blight, cercosporal leaf spot and wilt of pomegranate. Present experimental results indicate that the proposed approach can significantly support an accurate and automatic detection and recognition of leaf diseases. Current paper proposes GA-SVM based methodology to deal with one of the main issues of plant pathology. The results are proved to be accurate and satisfactory in contrast to manual procedure and hopefully take a strong leap forward in establishing itself in the market as one of the most efficient and effective process. In conclusion, the proposed detection models based GA-SVM are very efficient in detecting and classifying bacterial blight, cercosporal leaf spot. The developed Genetic Algorithm based SVM classifier that based on statistical classification perform well in all sample types of leaf diseases and can successfully detect and classify the examined diseases with accuracy 94%.