The Detection of Rice Leaf Folder using Black White – Red Green Blue (BW-RGB) Masking and Colour Code Detection

Authors

  • Wan Nor Syazwina Wan Jazlan Zuhairi -
  • Nik Shahidah Afifi Md Taujuddin
  • Suhaila Sari

Keywords:

Rice Leaf Folder, Image Processing Technique, BW-RGB Masking Detection, Colour Code Detection

Abstract

The main objectives of this research is to develop an algorithm and to detect the presence of a Rice Leaf Folder at paddy plants. This paper focuses on the image processing technique to increase the quality of the image to detect the pest. The methodology involves image acquisition, image pre-processing, analysis and detection of pest attacks. There are two methods used namely Black White – Red Green Blue (BW-RGB) Masking and Color Code Detection. The BW-RGB masking detection is the process of taking grayscale image and convert them to black and white images by replacing all pixels in the input image with luminance greater than one (white) while replacing other pixels with value zero (black). The function of BW is to separate an object in the image from the background when it is often produced by threshold, grayscale and colour image. Colour code is a system to display the information by using different colours. The colour code is translated into two conditions; Hexadecimal code and RGB value. The RGB value uses three numbers with a range of 0 to 255. All sample images will be converted into binary data in Graphic User Interface (GUI). This system can be used practically to be further improved in the future. The presence of Rice Leaf Folder is accurately detected by using BW-RGB masking and 33.33% accurate using Colour Code detection.

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Published

14-11-2021

How to Cite

Wan Jazlan Zuhairi, W. N. S., Md Taujuddin, N. S. A. ., & Sari, S. . (2021). The Detection of Rice Leaf Folder using Black White – Red Green Blue (BW-RGB) Masking and Colour Code Detection. Evolution in Electrical and Electronic Engineering, 2(2), 300–307. Retrieved from https://publisher.uthm.edu.my/periodicals/index.php/eeee/article/view/1706

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Articles