Investigation on Image Processing Techniques for Diagnosing Paddy Diseases

The main objective of this research is to develop a prototype system for diagnosing paddy diseases, which are blast disease (BD), brown-spot disease (BSD), and narrow brown-spot disease (NBSD). This paper concentrates on extracting paddy features through off-line image. The methodology involves image acquisition, converting the RGB images into a binary image using automatic thresholding based on local entropy threshold and Otsu method. A morphological algorithm is used to remove noises by using region filling technique. Then, the image characteristics consisting of lesion type, boundary colour, spot colour, and broken paddy leaf colour are extracted from paddy leaf images. Consequently, by employing production rule technique, the paddy diseases are recognized about 94.7 percent of accuracy rates. This prototype has a very great potential to be further improved in the future.