[1]李婕,高澄,涂静敏,等.基于无人机影像和FABM-UNet网络的油菜花簇分割方法研究[J].江苏农业科学,2024,52(20):113-121.
 Li Jie,et al.Segmentation method of rape flower cluster based on UAV image and FABM-UNet network[J].Jiangsu Agricultural Sciences,2024,52(20):113-121.
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基于无人机影像和FABM-UNet网络的油菜花簇分割方法研究()

《江苏农业科学》[ISSN:1002-1302/CN:32-1214/S]

卷:
第52卷
期数:
2024年第20期
页码:
113-121
栏目:
作物遥感监测
出版日期:
2024-10-20

文章信息/Info

Title:
Segmentation method of rape flower cluster based on UAV image and FABM-UNet network
作者:
李婕1 高澄1 涂静敏1 方岳1 乔江伟2 李礼3
1.湖北工业大学,湖北武汉 430068; 2.中国农业科学院油料作物研究所,湖北武汉 430062; 3.武汉大学,湖北武汉 430072
Author(s):
Li Jieet al
关键词:
油菜花油菜花覆盖度图像分割特征融合模块油菜产量
Keywords:
-
分类号:
S126;S127;TP79
DOI:
-
文献标志码:
A
摘要:
开花是油菜生长过程中的重要阶段,花朵覆盖度可以精确反映油菜花的生长状态,为产量预测提供有用信息。为了解决传统覆盖度获取方法需要大量人工的问题,本研究将覆盖度问题转变为油菜花的分割问题,提出了一种快速、无损的油菜花簇覆盖度获取方法。首先,基于无人机(UAV)的RGB影像,针对UNet网络特征融合不充分的问题,设计了一种特征聚合桥分割网络FABM-UNet;其次,为了验证模型的有效性,构建了油菜花分割数据集RSD,该数据集由DJI Phantom 4 Pro v2.0捕获而得,包括720张油菜花的影像以及对应的分割标签;最后,与传统的4种分割方法以及7种深度学习的网络(Deeplabv3+、PSPNet、UNet、UNet++、UNet3plus、Attention-UNet和TransUNet)进行对比,结果表明,FABM-UNet网络的分割指标IoU和Dice系数分别为0.87和0.93,在本研究的油菜花数据集上实现了最优分割性能,分割结果可以直观揭示油菜花朵覆盖度的变化情况,为油菜品种的选育提供了有力的支撑。
Abstract:
-

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相似文献/References:

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 Jia Song,et al.Study on microwave drying technology of rape flower[J].Jiangsu Agricultural Sciences,2018,46(20):174.

备注/Memo

备注/Memo:
收稿日期:2023-11-14
基金项目:湖北省重点研发计划(编号:2023BB030);武汉市知识创新项目(编号:108);国家自然科学基金-青年科学基金(编号:42301515);湖北省教育厅项目-科研计划青年项目(编号:Q20320413);湖北工业大学博士科研启动基金(编号:XJ2021004501)。
作者简介:李婕(1984—),女,湖北武汉人,博士,硕士生导师,主要从事计算机视觉研究。E-mail:jielonline@hbut.edu.cn。
通信作者:乔江伟,博士,副研究员,主要从事油菜育种工作。E-mail:qiaojiangwei@caas.cn。
更新日期/Last Update: 2024-10-20