基于果穗图像的玉米品种分类识别  

Classification and Identification of Corn Varieties Based on Ear Image

作　　者：赵威 马睿 王佳 郭宏杰 许金普 ZHAO Wei;MA Rui;WANG Jia;GUO Hongjie;XU Jinpu(School of Animation and Media,Qingdao Agricultural University,Shandong Qingdao 266109,China)

机构地区：[1]青岛农业大学动漫与传媒学院,山东青岛266109

出　　处：《中国农业科技导报》2023年第6期97-106,共10页Journal of Agricultural Science and Technology

基　　金：山东省重点研发计划项目(2021LZGC014);山东省中央引导地方科技发展资金项目(YDZX20203700002548)。

摘　　要：优良品种对提高农业产量和收入起着关键作用,针对现有的种业安全问题,为实现玉米品种的快速识别和保护,构建一种基于玉米果穗图像的品种识别模型。将采集到的1000张玉米果穗图像经预处理后按7∶2∶1的比例划分为训练集、验证集和测试集,并对数据集进行平移、翻转等多种数据增强处理。通过迁移学习,将预训练好的权重和参数迁移到NASNet-mobile、Xception、ResNet50V2、MobileNetV2、DenseNet121、VGG16模型进行对比,结果表明,NASNet-mobile识别性能较好,识别率达90%。不同优化算法的对比表明,优化器选择Adam模型具有更好的表现。在此基础上,对多种全连接层模块进行试验,结果表明,全连接层数量为2层、维度为256时可以得到更好的玉米果穗图像特征,最终模型在全连接层模块下的识别准确率达95%,较NASNet-mobile提升5%,实现了对玉米品种的分类识别。以上结果为玉米品种的快速精准鉴定以及种质资源保护提供了智能化技术支持。Crop variety plays a key role in improving agricultural production and income.Aiming at the safety problems of seed industry,in order to realize the rapid recognition and protection of corn varieties,a variety recognition model based on ear image was proposed.After image preprocessing,1000 images of corn ears were divided into training set,validation set and test set according to the ratio of 7∶2∶1.And the data sets were enhanced by translation,flipping and other data processing.Using transfer learning technology,the pre-trained weights and parameters were transferred to NASNet-mobile,Xception,ResNet50V2,MobileNetV2,DenseNet121 and VGG16 for comparative experiments.The results showed that the performance of NASNet-mobile was best,and the recognition rate reached 90%.At the same time,different optimization algorithms were used for comparative experiments,and the result showed that Adam model performed better.Based above results,experiments were carried out under a variety of different full connection layer modules.The results showed that,when the number of full connected layers was 2 and the dimension was 256,better corn ear image features could be obtained,and the recognition accuracy of the final model under the full connection layer module reached 95%,which increased by 5%compared with NASNet-mobile.It realized the variety classification and recognition of corn ear image,which provided intelligent technical support for the rapid and accurate identification of corn varieties and the protection of germplasm resources.

关 键 词：玉米果穗 迁移学习 品种识别 NASNet-mobile 

分 类 号：S513[农业科学—作物学]