Huang Kuo-Yi, Chien Mao-Chien
Department of Bio-Industrial Mechatronics Engineering, National Chung Hsing University, Tai-Chung 402, Taiwan.
Sensors (Basel). 2017 Apr 9;17(4):809. doi: 10.3390/s17040809.
This paper presents a novel method for identifying three varieties (Taikong 9, Tainan 11, and Taikong 14) of foundation paddy seeds. Taikong 9, Tainan 11, and Taikong 14 paddy seeds are indistinguishable by inspectors during seed purity inspections. The proposed method uses image segmentation and a key point identification algorithm that can segment paddy seed images and extract seed features. A back propagation neural network was used to establish a classifier based on seven features that could classify the three paddy seed varieties. The classification accuracies of the resultant classifier for varieties Taikong 9, Tainan 11, and Taikong 14 were 92.68%, 97.35% and 96.57%, respectively. The experimental results indicated that the three paddy seeds can be differentiated efficiently using the developed system.
本文提出了一种鉴定三种基础水稻种子品种（台农9号、台南11号和台农14号）的新方法。在种子纯度检验过程中，检验员无法区分台农9号、台南11号和台农14号水稻种子。所提出的方法使用图像分割和关键点识别算法，该算法可以分割水稻种子图像并提取种子特征。使用反向传播神经网络基于七个特征建立了一个分类器，该分类器可以对这三种水稻种子品种进行分类。所得分类器对台农9号、台南11号和台农14号品种的分类准确率分别为92.68%、97.35%和96.57%。实验结果表明，使用所开发的系统可以有效地区分这三种水稻种子。
