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

Hai Vu, Christos Tachtatzis, Paul Murray, David Harle, Trung Kien Dao, Robert Atkinson, Thi-Lan Le, Ivan Andonovic, Stephen Marshall

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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.
Original languageEnglish
Number of pages6
Publication statusAccepted/In press - 30 Aug 2016
Event12th IEEE-RIVF International Conference on Computing and Communication Technologies - Thuyloi University, Hanoi, Viet Nam
Duration: 7 Nov 20169 Nov 2016
Conference number: 12


Conference12th IEEE-RIVF International Conference on Computing and Communication Technologies
Abbreviated titleIEEE-RIVF
Country/TerritoryViet Nam
Internet address


  • rice seed
  • inspection
  • hyperspectral imaging

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