摘要
种子发芽试验是检验作物品质的重要环节。为提高种子发芽检测效率,实现种子发芽检测自动化,以小麦为研究对象,通过机器视觉技术结合深度学习方法,构建基于YOLOv5的种子发芽判别的模型,在此基础上通过小麦7 d发芽试验图像组合分析,设计一套基于YOLOv5的种子发芽检测改进判别方法(DB-YOLOv5),实现对小麦种子发芽率、发芽势、发芽指数、平均发芽天数的快速检测,并开展检测试验。结果表明,YOLOv5模型对小麦种子发芽判别精确率为92.5%,DB-YOLOv5模型对小麦种子发芽判别精确率为98.5%,发芽势、发芽指数、平均发芽天数与人工检测误差为0.5%、2.39、0.1 d。上述结果表明,DB-YOLOv5模型可实现对小麦种子发芽率、发芽势、发芽指数、平均发芽天数的快速检测,为农作物种子发芽快速检测提供参考。
Seed germination test is an important part of testing the quality of crops.In order to improve the efficiency of seed germination detection and realize the automation of seed germination detection,taking wheat as the research object,a model based on YOLOv5 seed germination discrimination was constructed by machine vision technology combined with deep learning methods.Based on this,a set of improved discriminative methods for seed germination detection based on YOLOv5 was designed through the combined analysis of wheat germination test images in 7 days.The rapid detection of wheat seed germination rate,germination potential,germination index and average germination days was realized and the detection experiment was carried out.The results showed that the YOLOv5 model had 92.5%accuracy for wheat seed germination.By using the improved discrimination method of seed germination detection based on YOLOv5,the accuracy of seed germination discrimination was 98.5%,and the errors of germination potential,germination index,and average germination days were 0.5%,2.39,and 0.1 d compared with manual detection.The improved discrimination method based on YOLOv5 seed germination detection proposed in this study could realize the rapid detection of seed germination rate,germination potential,germination index and average germination days,and provide a reference for the rapid detection of crop seed germination.
作者
白卫卫
赵雪妮
罗斌
赵薇
黄硕
张晗
BAI Weiwei;ZHAO Xueni;LUO Bin;ZHAO Wei;HUANG Shuo;ZHANG Han(Shaanxi University of Science and Technology,Xi an 710016,China;Research Center of Intelligent Equipment,Beijing Academy of Agriculture and Forestry Sciences,Beijing 100097,China;Research Center of Information Technology,Beijing Academy of Agriculture and Forestry Sciences,Beijing 100097,China)
出处
《浙江农业学报》
CSCD
北大核心
2023年第2期445-454,共10页
Acta Agriculturae Zhejiangensis
基金
国家重点研发计划(2017YFD0701205)
北京市农林科学院青年基金(QNJJ202104)
北京市农林科学院2022年度科研创新平台建设项目(PT2022-34)。
作者简介
白卫卫(1995-),男,陕西宝鸡人,硕士研究生,研究方向为智能检测与自动化控制技术。E-mail:985782044@qq.com;通信作者:张晗,E-mail:zhangha@nercita.org.cn。
