摘要: |
为探索快速、高效检测甜玉米种子生活力的方法,利用机器视觉技术(Seed Identification)批量快速提取金菲甜玉米种子的Red(红基色)、Green(绿基色)、Blue(蓝基色)、Hues(色相)、Saturation(饱和度)、Brightness(亮度)、Light(明度)、a(红色至绿色的范围)、b(蓝色至黄色的范围)、灰度、宽度、长度和投影面积等物理特征参数,通过单粒发芽试验确定每粒种子的生活力,然后采用人工神经网络和二元逻辑回归结合主成分分析进行建模。结果表明:1)a值、b值、Saturation和投影面积与种子的活力均存在极显著或显著相关,且变异系数相对较大,其中当a ≤ 3时,发芽率可从72.7%提升至77.6%,获选率达到79.4%;投影面积≤ 77.31 mm2时,发芽率可提升至73.7%,获选率87.6%;2)用13个物理指标标准化后直接进行人工神经网络建模,双隐藏层(训练集:测试集=6:4)建模,模型整体预测正确率为74.2%,优质种子获选率达到93.8%,发芽率可提升至76.9%;3)经二元逻辑回归模型预测发芽率为74.5%,但神经网络模型稳定性优于二元逻辑回归建模。 |
关键词: 甜玉米 机器视觉技术 主成分分析 人工神经网络 二元逻辑回归 |
DOI:10.11841/j.issn.1007-4333.2018.07.01 |
投稿时间:2017-07-22 |
基金项目:北京科委项目(Z151100001015004) |
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Seed viability testing model of sweet corn based on artificial neural network and binary logistic regression |
LIU Minjie1,2, XU Xuan1,2, WANG Jianhua2, SUN Qun2, XIANG Chunyang1
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(1.Tianjin Agricultural University, Tianjin 300384, China;2.College of Agronomy and Biotechnology/Beijing Innovation Center of Crop Seeds Full Technologies Research of Ministry/Beijing Key Laboratory of Crop Genetic Improvement, China Agricultural University, Beijing 100193, China) |
Abstract: |
In order to test the vitality of sweet corn seed quickly and efficiently,several physical characteristics (red,green,blue,hues,saturation,brightness,light,a, b,width,length and projected area) of a certain cultivar of sweet corn,Jinfei,were obtained in batch and quickly went through image recognition technology (Seed Identification software).Each seed viability was confirmed through single seed germination test,and a model for seed viability discrimination of Jinfei was established by applying principle component analysis,artificial neural network and binary logistic regression.The results showed that:1) a,b,saturation,width and projection area all had significant correlation with the seed vigor and relatively high variation coefficient.For the seeds with a ≤ 3,the germination percentage was increased from 72.7% to 77.6%,and the selected rate reached to 79.4%.For the seeds with projection area ≤ 77.31 mm2,the germination percentage can be increased to 73.7% and the selected rate reached 87.6%.2) Based on 13 standardized physical indicators,artificial neural network model with double hidden layers (training set:testing set=6:4) possessed an overall predicting accuracy at 74.2%,germination percentage also reached to 76.9%,with 93.8% quality seed selection.3) The germination percentage of binary logistic regression was 74.5%,but the model stability of Artificial Neural Network model was better than binary logistic regression.These results would provide references for further research and implementation of quality seed sorting technology. |
Key words: sweet corn machine vision technology Principal Component Analysis Artificial Neural Network Binary Logic Regression |