基于机器视觉技术的玉米种子精选方法研究  

Research on Refined Selection Method for Maize Seeds Based on Machine Vision Technology

作　　者：韩小伟 周江明 高英波 田雪慧 李明军 郝延杰 李伟 李树兵 刘树泽 Han Xiaowei;Zhou Jiangming;Gao Yingbo;Tian Xuehui;Li Mingjun;Hao Yanjie;Li Wei;Li Shubing;Liu Shuze(Binzhou Academy of Agricultural Sciences,Binzhou 256600,Shandong,China;Maize Research Institute,Shandong Academy of Agricultural Sciences/National Engineering Laboratory of Wheat and Maize/Key Laboratory of Biology and Genetic Improvement of Maize in Northern Huang-Huai-Hai Plain,Ministry of Agriculture and Rural Affairs,Jinan 250100,Shandong,China)

机构地区：[1]滨州市农业科学院,山东滨州256600 [2]山东省农业科学院玉米研究所/小麦玉米国家工程实验室/农业农村部黄淮海北部玉米生物学与遗传育种重点实验室,山东济南250100

出　　处：《作物杂志》2024年第6期242-248,共7页Crops

基　　金：山东省科技特派员行动计划项目(2020KJTPY057);滨州市农社领域科技创新政策引导计划项目(2023KTPY005)。

摘　　要：为进一步提高玉米种子发芽率,探讨适宜的种子精选方法及参数,以郑单958为材料,通过种子形态自动化识别软件(Seed Identification)获取单粒玉米种子物理参数,并进行单粒种子发芽试验,研究玉米种子活力指标与其形态物理参数之间的相关性,筛选最优精选指标,同时采用单一指标分类法、二元逻辑回归模型和多层感知器神经网络模型预测种子发芽率,确定最佳精选方法。结果表明,幼苗芽长、根长、鲜重与种子物理参数R、A、S、B3呈显著相关。分别按170≤R≤190、10≤A≤20、16≤S≤24、71≤B3≤79进行单一指标精选,其发芽率由未筛选前的66.0%分别提升至72.1%、73.7%、75.0%、73.6%,获选率分别为56.8%、63.6%、52.3%和50.8%;二元逻辑回归模型方法使种子发芽率提高至80.9%,种子发芽获选率88.4%,模型稳定率97.3%;多层感知器神经网络模型方法下种子发芽率提高至82.9%,种子发芽获选率89.5%,模型稳定率97.7%。综上,物理指标R、A、S和B3值可作为玉米种子精选参数;相比单一指标和二元逻辑回归模型,多层感知器神经网络模型在预测种子发芽率、获选率和稳定性方面具有较强优势,可确定为最佳精选方法。In order to further improve the germination rate of maize seeds,the more suitable selection methods and parameters were discussed.Based on this,Zhengdan 958 was used as the test material in this experiment.The physical parameters of single maize seed were obtained by Seed Identification software,and the single seed germination test was carried out to study the correlation between maize seed vigor index and its morphological and physical parameters,so as to screen the optimal selection index;At the same time,the single index classification method,binary logistic regression model and multi-layer perceptron neural network model were used to predict the seed germination rate to determine the best selection method.The bud length,root length and fresh weight of seedlings were significantly correlated with the physical parameters of R,A,S and B3.According to the single index of 170≤R≤190,10≤A≤20,16≤S≤24,71≤B3≤79,the germination rate increased from 66.0%to 72.1%,73.7%,75.0%and 73.6%respectively,and the selection rate was 56.8%,63.6%,52.3%and 50.8%,respectively;The seed germination rate of the binary logistic regression model method was increased to 80.9%,the seed germination selection rate was 88.4%,and the model stability rate was 97.3%;The seed germination rate of the multi-layer perceptron neural network model method was increased to 82.9%,the seed germination selection rate was 89.5%,and the model stability rate was 97.7%.In conclusion,the physical indexes R,A,S and B3 values can be used as the selection parameters of maize seeds;Compared with single index and binary logistic regression model,the multi-layer perceptron neural network model has strong advantages in predicting seed germination rate,selection rate and stability,and can be determined as the best selection method.

关 键 词：玉米种子 精选方法 物理参数 机器视觉技术 多层感知器神经网络模型 

分 类 号：TP391.41[自动化与计算机技术—计算机应用技术] S513[自动化与计算机技术—计算机科学与技术]