Supervised segmentation on fusarium macroconidia spore in microscopic images via analytical approaches

• Published in: Multimedia tools and applications Vol. 83; no. 14; pp. 42545 - 42560
• Main Authors: Azuddin, K. A., Junoh, A. K., Zakaria, A., Rahman, M. T. A., Nor, N. M. I. M., Nishizaki, H., Latiffah, Z., Azuddin, N. F., Abdullah, M. Z., Terna, T. P.
• Format: Journal Article
• Language: English
• Published: New York Springer US 01-04-2024; Springer Nature B.V
• Subjects: Accuracy; Artificial intelligence; Cluster analysis; Clustering; Computer Communication Networks; Computer Science; Data Structures and Information Theory; Decision trees; Deep learning; Digital imaging; Fungi; Machine learning; Morphology; Multimedia; Multimedia Information Systems; Plant diseases; Special Purpose and Application-Based Systems; Track 2: Medical Applications of Multimedia; Vector quantization; Pattern Recognition; Supervised and Unsupervised Learning Methods; Machine Learning; Artificial Intelligence
• ISSN: 1573-7721; 1380-7501; 1573-7721
• DOI: 10.1007/s11042-023-17008-y

Fungi are one of the major causes that contributed to plant diseases. There are lots of fungi species but it is estimated that only 10% have been described. There are two major approaches to identifying fungi species, morphological identification, and molecular test which need cautious clarification to make good interpretations and are time-consuming. In this paper, we propose a Machine Learning approach that involves the use of the K-Means clustering technique, and Decision Tree to highlight the observed fungi spore images taken under the microscopic view and discard background pixels to produce digital images database which later can be used for Deep Learning.