Brown Spot Disease Severity Level Detection using Image Processing - A. Halim, Nurul Hanisah Najwa; Md Taujuddin, Nik Shahidah Afifi; Sari, Suhaila (Evolution in Electrical and Electronic Engineering, 2021)
In this research project, the primary aims is to create an algorithm that will assist individuals, particularly young farmers, in detecting the disease early, and resulting in excellent quality and quantity of rice production. The paddy field data images are then processed in the MATLAB software. Image enhancement was performed on the sample image to increase the image quality. The image background is then segmented using the MATLAB image segmenter tool. Following the removal of the image background, the procedure was proceeded using a colour detection method in which a masking process was performed to the segmented image of the binary and RGB image. To continue with area detection, the pixel values from those images are used to calculate the pixel intensity difference between infected and non-infected regions. Horsfall and Heuberger’s severity level table is then used as a reference to identify the severity level of Brown Spot disease. A graphical user interface (GUI) is developed to determine the Brown Spot disease automatically. According to the findings of the research, the detection accuracy is 90%.