Projects per year
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.
|Number of pages||6|
|Publication status||Accepted/In press - 30 Aug 2016|
|Event||12th IEEE-RIVF International Conference on Computing and Communication Technologies - Thuyloi University, Hanoi, Viet Nam|
Duration: 7 Nov 2016 → 9 Nov 2016
Conference number: 12
|Conference||12th IEEE-RIVF International Conference on Computing and Communication Technologies|
|Period||7/11/16 → 9/11/16|
- rice seed
- hyperspectral imaging
FingerprintDive into the research topics of 'Spatial and spectral features utilization on a hyperspectral imaging system for rice seed varietal purity inspection'. Together they form a unique fingerprint.
- 1 Finished
Automatic Rice Seed Inspection Using Hyper-Spectral Imaging (Newton Fund)
Tachtatzis, C., Harle, D., Marshall, S. & Murray, P.
Royal Academy of Engineering RAE
15/11/15 → 14/02/17
- 1 Visiting an external academic institution
MICA International Research Institute, Hanoi University of Science and Technology
Christos Tachtatzis (Visiting researcher)14 Aug 2016 → 20 Aug 2016
Activity: Visiting an external institution types › Visiting an external academic institution