基于 k-means 聚类的叶片病害受损区域检测
计算机视觉与模式识别
2021-05-18 v1
摘要
随着人口增长，粮食危机日益加剧。在此危机时期，作物叶部病害是食品工业中的最大问题。本文针对该问题，提出一种检测叶部病害的有效方法。借助图像处理与分割，可从叶片样本图像中检测叶部病害。利用 k-means 聚类与 Otsu 方法检测叶片中的病害区域，有助于确定应采取的适当处理措施。进一步，若计算正常区域与病害区域的比例，将能够预测该叶片是否可被治愈。
关键词
引用
@article{arxiv.1810.10188,
title = {Fault Area Detection in Leaf Diseases using k-means Clustering},
author = {Subhajit Maity and Sujan Sarkar and Avinaba Tapadar and Ayan Dutta and Sanket Biswas and Sayon Nayek and Pritam Saha},
journal= {arXiv preprint arXiv:1810.10188},
year = {2021}
}
备注
This article is of 5 pages in IEEE format. It has been presented as a full paper in International Conference on Trends in Electronics and Informatics (ICOEI 2018) and is currently under the proceedings of the conference and yet to be published in IEEE Xplore