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

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.

Automated summary

This paper proposes a machine learning approach using K-Means clustering and decision tree to process fungi spore images for creating a digital image database for deep learning purposes.