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The Strathprints institutional repository is a digital archive of University of Strathclyde's Open Access research outputs. Strathprints provides access to thousands of Open Access research papers by Strathclyde researchers, including by researchers from the Physical Activity for Health Group based within the School of Psychological Sciences & Health. Research here seeks to better understand how and why physical activity improves health, gain a better understanding of the amount, intensity, and type of physical activity needed for health benefits, and evaluate the effect of interventions to promote physical activity.

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Spatial and spectral features utilization on a hyperspectral imaging system for rice seed varietal purity inspection

Vu, Hai and Tachtatzis, Christos and Murray, Paul and Harle, David and Dao, Trung Kien and Atkinson, Robert and Le, Thi-Lan and Andonovic, Ivan and Marshall, Stephen (2016) Spatial and spectral features utilization on a hyperspectral imaging system for rice seed varietal purity inspection. In: 12th IEEE-RIVF International Conference on Computing and Communication Technologies, 2016-11-07 - 2016-11-09, Thuyloi University. (In Press)

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A conventional method to inspect the varietal purity of rice seeds is based on evaluating 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 (NIR) and Visible cameras 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.