Severity Estimation of Plant Leaf Diseases Using Segmentation Method

• Main Authors: Chyntia Jaby, Entuni, Tengku Mohd Afendi, Zulcaffle, Kuryati, Kipli, Fatih, Kurugollu
• Format: Article
• Language: English
• Published: King Mongkut’s University of Technology North Bangkok (KMUTNB) 2020
• Subjects: QA75 Electronic computers. Computer science; T Technology (General)
• Online Access: http://ir.unimas.my/id/eprint/32703/1/Severity%20Estimation%20of%20Plant%20Leaf%20Diseases%20Using%20Segmentation%20Method%20%28Chyntia%20Jaby%20Entuni%2C%20Tengku%20Mohd%20Afendi%20Zulcaffle%2C%20Fatih%20Kurugollu%2C%20Kuryati%20Kipli%29.pdf; http://ir.unimas.my/id/eprint/32703/; http://ojs.kmutnb.ac.th/index.php/ijst
• Institution: Universiti Malaysia Sarawak

Plants have assumed a significant role in the history of humankind, for the most part as a source of nourishment for human and animals. However, plants typically powerless to different sort of diseases such as leaf blight, gray spot and rust. It will cause a great loss to farmers and ranchers. Therefore, an appropriate method to estimate the severity of diseases in plant leaf is needed to overcome the problem. This paper presents the fusion of the Fuzzy C-Means segmentation method with four different color spaces namely RGB, HSV, L*a*b and YCbCr to estimate plant leaf disease severity. The percentage of performance of proposed algorithms are recorded and compared with the previous method which are K-Means and Otsu's thresholding. The best severity estimation algorithm and color space used to estimate the diseases severity of plant leaf is the combination of Fuzzy C-Means and YCbCr color space. The average performance of Fuzzy C-Means is 91.08% while the average performance of YCbCr is 83.74%. Combination of Fuzzy C-Means and YCbCr produce 96.81% accuracy. This algorithm is more effective than other algorithms in terms of not only better segmentation performance but also low time complexity that us 34.75s in average with 0.2697s standard deviation.