Detection of Onion Leaf Disease Using Hybridized Feature Extraction and Feature Selection Approach

George Oludare Gbadebo, J. Alhassan, O. A. Ojerinde
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引用次数: 0

Abstract

Onion (Allium Cepa) is one of the most important vegetable and commercial plants that is being grown all around the world for more than 3000 years. Just like several other crop plants, Onion plants too can be attacked by pests and diseases of various kind, this attacks do give rise to low yields, bad quality and of course shortages of this important plants. Visual observation and analysis for detection of onion leaf diseases, if handed over to computing, using Machine Learning techniques, is more efficient, fast, cost saving, consistent, more reliable and highly accurate compare to what any human disease-expert eyes can offer. This work makes use of the prepared datasets of onion leaf digital images, after image preprocessing, some features were extracted/selected using Grey Level Co-occurrence Matrix (GLCM) and Particle Swarm Optimization (PSO) algorithms, the selected/extracted features then fed into classifier algorithms for eventual classification into healthy or unhealthy onion leaf.
基于杂交特征提取和特征选择方法的洋葱叶片病害检测
洋葱(Allium Cepa)是最重要的蔬菜和商业植物之一,在世界各地种植了3000多年。就像其他几种作物一样,洋葱也会受到各种病虫害的侵害,这种侵害确实会导致产量低、质量差,当然还会导致这种重要植物的短缺。视觉观察和分析洋葱叶疾病的检测,如果移交给计算机,使用机器学习技术,比任何人类疾病专家的眼睛所能提供的更高效、快速、节省成本、一致、更可靠和高度准确。本工作利用事先准备好的洋葱叶数字图像数据集,经过图像预处理,利用灰度共生矩阵(GLCM)和粒子群优化(PSO)算法提取/提取特征,然后将所提取/提取的特征输入分类器算法,最终分类为健康或不健康的洋葱叶。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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