| Summary: | Sun flower (Helianthus annuus L.) is one of the important oil seed crops and potentially fit in agricultural system and oil production sector of India. Sunflower crop gets damaged by the impact of various diseases, insects and nematodes resulting in wide range of loss in production. Disease detection is possible through naked eye observation, but this method is unsuccessful when one has to monitor the large farms. As a solution to this problem, we developed and present a system for segmentation and classification of Sunflower leaf images. This research paper presents surveys conducted on different diseases classification techniques that can be used for sunflower leaf disease detection. Segmentation of Sunflower leaf images, which is an important aspect for disease classification, is done by using Particle swarm optimization algorithm. Satisfactory results have been given by the experiments done on leaf images. The average accuracy of classification of proposed algorithm is 98.0% compared to 97.6 and 92.7% reported in state-of-the-art methods. Keywords: Image segmentation, Soft computing techniques, Sunflower leaf diseases, Particle swarm optimization
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