An image recognition system for crop disease identification of paddy fields in Sri Lanka

The classification and recognition of paddy diseases are of the major technical and economical importance in the agricultural industry. To automate these activities, like texture, color and shape, disease recognition system is feasible. The goal of this research is to develop an image recognition system that can recognize paddy diseases. Images were acquired under laboratory condition using digital camera. Three major diseases commonly found in Sri Lanka, Rice blast (Magnaporthe grisea), Rice sheath blight (Rhizoctonia solani) and Brown spot (Cochiobolus miyabeanus) were selected for this research. Image processing starts with the digitized a color image of paddy disease leaf. Then a method of mathematics morphology is used to segment these images. Then texture, shape and color features of color image of disease spot on leaf were extracted, and a classification method of membership function was used to discriminate between the three types of diseases. The analysis of the results showed over 70 percent classification accuracy around 50 sample images.