Harumanis mango leaf disease recognition system using image processing technique

• Published in: Indonesian Journal of Electrical Engineering and Computer Science
• Main Author: Gining R.A.J.M.; Fauzi S.S.M.; Yusoff N.M.; Razak T.R.; Ismail M.H.; Zaki N.A.; Abdullah F.
• Format: Article
• Language: English
• Published: Institute of Advanced Engineering and Science 2021
• Online Access: https://www.scopus.com/inward/record.uri?eid=2-s2.0-85109449586&doi=10.11591%2fijeecs.v23.i1.pp378-386&partnerID=40&md5=5aa9cfc36922e97d8dcb82e99026f6bf

Current Harumanis mango farming technique in Malaysia still mostly depends on the farmers' own expertise to monitor the crops from the attack of pests and insects. This approach is susceptible to human errors, and those who do not possess this skill may not be able to detect the disease at the right time. As leaf diseases seriously affect the crop's growth and the quality of the yield, this study aims to develop a recognition system that detects the presence of disease in the mango leaf using image processing technique. First, the image is acquired through a smartphone camera; once it has been pre-processed, it is then segmented in which the RGB image is converted to an HSI image, then the features are extracted. Lastly, the classification of disease is done to determine the type of leaf disease. The proposed system effectively detects and classify the disease with an accuracy of 68.89%. The findings of this project will contribute to farmers and society's benefit, and researchers can use the approach to address similar issues in future works. © 2021 Institute of Advanced Engineering and Science. All rights reserved.