基于图像识别的小麦品种分类研究

 基于数字图像分析，利用小麦籽粒的20个形态特征和12个颜色特征对来自中国4个地点7个春小麦品种共28个样本进行分类和识别。对于不同品种和地区的样本，分别利用逐步判别分析，选取显著性较大的特征参量，建立各地区和品种的贝叶斯分类器模型。结果表明，对各地区品种识别的正确回判率和测试集的正确识别率均达到100%。将各样本按品种合并，再对合并后的样本进行品种识别，除了新克旱 9号的回判率为98.3%外，其它品种的回判率均为100%。测试集中，龙麦26和青春566正确识别率分别为97.5%和95.0%，其它品种均为100%。品种来源地识别也能达到较高的水平，甘肃、宁夏、新疆和黑龙江的正确识别率分别为88.6%、92.9%、72.9%和95.7%。说明利用籽粒图像对小麦品种进行识别高效可行。

Classification of Wheat Cultivar by Digital Image Analysis

Digital image analysis was used to develop a pattern recognition algorithm to classify individual kernels of seven Chinese spring wheat cultivars grown at 4 locations.Totally,20 morphological parameters and 12 color parameters were extracted.Three hundred kernels per sample were used as the training data set to develop identification model,and another 200 kernels were used as the test set.For the test set,the classification accuracy of wheat cultivars was 100% in each growing location.Except for Xinkehan 9 with 98.3%,the correct discrimination of the training set of collective samples is 100% for wheat cultivar.For the test set,the correct discrimination of Longmai 26 and Qingchun 566 were 97.5% and 95.0%,the others is 100%.For the origin of wheat grains,the classification of Gansu,Ningxia,Xinjiang and Heilongjiang were 88.6%,92.9%,72.9% and 95.7%,respectively.The results show that it is feasible to identify and classify wheat cultivar (grains) using digital image analysis.