基于改进YOLO v3的玉米叶片气孔自动识别与测量方法  

Automatic Identification and Measurement of Maize Leaves Stomata Based on YOLO v3

作　　者：张帆[1] 郭思媛 任方涛 张新红[3] 李结平 ZHANG Fan;GUO Siyuan;REN Fangtao;ZHANG Xinhong;LI Jieping(Henan Key Laboratory of Big Data Analysis and Processing,Henan University,Kaifeng 475004,China;School of Computer and Information Engineering,Henan University,Kaifeng 475004,China;School of Software,Henan University,Kaifeng 475004,China;College of Agriculture,Henan University,Kaifeng 475004,China)

机构地区：[1]河南大学河南省大数据分析与处理重点实验室,开封475004 [2]河南大学计算机与信息工程学院,开封475004 [3]河南大学软件学院,开封475004 [4]河南大学农学院,开封475004

出　　处：《农业机械学报》2023年第2期216-222,共7页Transactions of the Chinese Society for Agricultural Machinery

基　　金：河南省自然科学基金项目(202300410092、202300410093);河南省科技攻关计划项目(222102310090)。

摘　　要：气孔是植物叶片与外界环境交换气体和水分的重要结构。针对现有气孔性状分析主要采用人工测量,过程繁琐、效率低下、容易出现人为误差的问题,本文采用YOLO(You only look once)深度学习模型完成了玉米叶片气孔的自动识别与自动测量工作。结合玉米叶片气孔数据集的特点,对YOLO深度学习模型进行了改进,有效地提高了气孔识别和测量的精确率。对YOLO深度学习模型中的预测端进行了优化,降低了误检率;同时,结合气孔特征对16倍、32倍下采样层进行简化,提高了识别效率。实验结果表明,改进后的YOLO深度学习模型在玉米叶片气孔数据集上识别精确率达到95%,参数测量的平均精确率达到90%以上。本文方法能够自动完成玉米叶片气孔的识别、计数与测量,解决了传统气孔分析方法的低效率问题,为农业科学家、植物学家开展植物气孔分析研究提供了技术支撑。Stomata are the important structure for plant leaves to exchange gas and water with environment.In order to solve the problem that traditional analysis methods of stomatal traits adopt manual observation and measurement,which causes tedious process,low efficiency and prone to human error,you only look once(YOLO) deep learning model was adopted to complete automatic identification and automatic measurement of stomata in maize(Zea mays L.) leaves.Combined with the characteristics of stomata data set,the YOLO deep learning model was improved to effectively improve the precision of stomata identification and measurement.The prediction end in YOLO deep learning model was optimized,which reduced the false detection rate.At the same time,the 16-fold and 32-fold down-sampling layers were simplified according to the characteristics of stomata,which improved the recognition efficiency.Experimental results showed that the identification precision of the improved YOLO deep learning model reached 95% on the maize leaves stomatal data set,and the average accuracy of parameter measurement was above 90%.The proposed method can automatically complete the identification,counting and measurement of stomata of maize,which solved the low efficiency of traditional stomatal analysis methods,and it can help agricultural scientists and botanists to conduct the analysis and research related to plant stomata.

关 键 词：玉米叶片 气孔 深度学习 自动识别 

分 类 号：TP391.41[自动化与计算机技术—计算机应用技术]