利用非成像高光谱数据识别植物物种

Amarsinh Varpe, Yogesh D. Rajendra, Amol D. Vibhute, S. Gaikwad, K. Kale
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引用次数: 6

摘要

高光谱非成像数据提供400-2500nm的光谱范围,能够识别表面上的每一种独特材料。植物物种鉴定是一项人工和计算相结合的重要工作。本文提出了基于非成像高光谱数据的植物物种识别系统,并设计了自己的实验数据库。此外,我们还对多种植物进行了识别,并对其进行了支持向量机(SVM)算法的识别。支持向量机的总体准确率达到91%。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Identification of plant species using non-imaging hyperspectral data
Hyperspectral non-imaging data provides the spectral range from 400-2500nm which has the ability to identify each and every unique materials on the surface. The plant species identification is critical task manually and computationally. In the present paper, we have proposed plant species identification system based on non-imaging hyperspectral data and designed our own database for experiment. Also we have identified various plant species and performed support vector machine (SVM) algorithm on it for recognition. The overall accuracy 91% was achieved through SVM.
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