Identification of rice seed varieties using neural network.
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.
Liu
Zhao-yan
ZY
School of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou 310029, China.
Cheng
Fang
F
Ying
Yi-bin
YB
Rao
Xiu-qin
XQ
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