title: Identification of rice seed varieties using neural network.
abstract: A digital image analysis algorithm based color and morphological features was developed to identify the six varieties (ey7954, syz3, xs11, xy5968, xy9308, z903) rice seeds which are widely planted in Zhejiang Province. Seven color and fourteen morphological features were used for discriminant analysis. Two hundred and forty kernels used as the training data set and sixty kernels as the test data set in the neural network used to identify rice seed varieties. When the model was tested on the test data set, the identification accuracies were 90.00%, 88.00%, 95.00%, 82.00%, 74.00%, 80.00% for ey7954, syz3, xs11, xy5968, xy9308, z903 respectively.
authors.affiliation: School of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou 310029, China.
authors.forename: Zhao-yan
authors.initials: ZY
authors.lastname: Liu
authors.forename: Fang
authors.initials: F
authors.lastname: Cheng
authors.forename: Yi-bin
authors.initials: YB
authors.lastname: Ying
authors.forename: Xiu-qin
authors.initials: XQ
authors.lastname: Rao
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D016000:Cluster Analysis; D003124:Colorimetry / Q000379:methods*; D007089:Image Enhancement / Q000379:methods; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D016247:Information Storage and Retrieval / Q000379:methods; D012275:Oryza / Q000033:anatomy & histology* / Q000145:classification*; D010363:Pattern Recognition, Automated / Q000379:methods*; D010781:Photography / Q000379:methods; D015203:Reproducibility of Results; D012639:Seeds / Q000033:anatomy & histology* / Q000145:classification*; D012680:Sensitivity and Specificity; D013045:Species Specificity