高光谱成像
支持向量机
人工智能
随机森林
目视检查
计算机科学
模式识别(心理学)
局部二进制模式
计算机视觉
图像(数学)
直方图
作者
Hai Vu,Christos Tachtatzis,Paul Murray,David Harle,Thiên-My Dao,Thi‐Lan Le,Ivan Andonović,Stephen Marshall
标识
DOI:10.1109/rivf.2016.7800289
摘要
A conventional method to inspect the varietal purity of rice seeds is based on human visual inspection where a random sample is drawn from a batch. This is a tedious, laborious, time consuming and extremely inefficient task. This paper presents an automatic rice seed inspection method using Hyperspectral imaging and machine learning, to automatically detect unwanted seeds from other varieties which may be contained in a batch. Hyperspectral image data from Near-infrared (NER) camera are acquired for six common rice seed varieties. The results of applying two classifiers are presented, a Support Vector Machine (SVM) and a Random Forest (RF), where each consists of six one-versus-rest binary classifiers. The results show that combining spectral and shape-based features derived from the rice seeds, increase precision of the multi-label classification to 84% compared 74% when only visual features are used.
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