• 文献检索
  • 文档翻译
  • 深度研究
  • 学术资讯
  • Suppr Zotero 插件Zotero 插件
  • 邀请有礼
  • 套餐&价格
  • 历史记录
应用&插件
Suppr Zotero 插件Zotero 插件浏览器插件Mac 客户端Windows 客户端微信小程序
定价
高级版会员购买积分包购买API积分包
服务
文献检索文档翻译深度研究API 文档MCP 服务
关于我们
关于 Suppr公司介绍联系我们用户协议隐私条款
关注我们

Suppr 超能文献

核心技术专利：CN118964589B侵权必究
粤ICP备2023148730 号-1Suppr @ 2026

文献检索

告别复杂PubMed语法，用中文像聊天一样搜索，搜遍4000万医学文献。AI智能推荐，让科研检索更轻松。

立即免费搜索

文件翻译

保留排版，准确专业，支持PDF/Word/PPT等文件格式，支持 12+语言互译。

免费翻译文档

深度研究

AI帮你快速写综述，25分钟生成高质量综述，智能提取关键信息，辅助科研写作。

立即免费体验

YOLOv8-segANDcal：大豆胚根特征的分割、提取与计算

YOLOv8-segANDcal: segmentation, extraction, and calculation of soybean radicle features.

作者信息

Wu Yijie, Li Zhengjun, Jiang Haoyu, Li Qianyun, Qiao Jinxin, Pan Feng, Fu Xiuqing, Guo Biao

机构信息

College of Artificial Intelligence, Nanjing Agricultural University, Nanjing, China.

College of Engineering, Nanjing Agricultural University, Nanjing, China.

出版信息

Front Plant Sci. 2024 Jul 11;15:1425100. doi: 10.3389/fpls.2024.1425100. eCollection 2024.

DOI:10.3389/fpls.2024.1425100
PMID:39055355
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC11269219/
Abstract

The high-throughput and full-time acquisition of images of crop growth processes, and the analysis of the morphological parameters of their features, is the foundation for achieving fast breeding technology, thereby accelerating the exploration of germplasm resources and variety selection by crop breeders. The evolution of embryonic soybean radicle characteristics during germination is an important indicator of soybean seed vitality, which directly affects the subsequent growth process and yield of soybeans. In order to address the time-consuming and labor-intensive manual measurement of embryonic radicle characteristics, as well as the issue of large errors, this paper utilizes continuous time-series crop growth vitality monitoring system to collect full-time sequence images of soybean germination. By introducing the attention mechanism SegNext_Attention, improving the Segment module, and adding the CAL module, a YOLOv8-segANDcal model for the segmentation and extraction of soybean embryonic radicle features and radicle length calculation was constructed. Compared to the YOLOv8-seg model, the model respectively improved the detection and segmentation of embryonic radicles by 2% and 1% in mAP, and calculated the contour features and radicle length of the embryonic radicles, obtaining the morphological evolution of the embryonic radicle contour features over germination time. This model provides a rapid and accurate method for crop breeders and agronomists to select crop varieties.

摘要

高通量、全时段获取作物生长过程图像，并分析其特征的形态参数，是实现快速育种技术的基础，从而加速作物育种工作者对种质资源的探索和品种选择。大豆萌发过程中胚根特征的演变是大豆种子活力的重要指标，直接影响大豆后续的生长进程和产量。为解决人工测量胚根特征耗时费力以及误差大的问题，本文利用连续时间序列作物生长活力监测系统采集大豆萌发的全时段序列图像。通过引入注意力机制SegNext_Attention、改进分割模块并添加CAL模块，构建了用于大豆胚根特征分割提取及胚根长度计算的YOLOv8-segANDcal模型。与YOLOv8-seg模型相比，该模型在平均精度均值（mAP）上分别将胚根检测和分割提高了2%和1%，并计算了胚根的轮廓特征和胚根长度，得到了胚根轮廓特征在萌发时间上的形态演变。该模型为作物育种工作者和农学家选择作物品种提供了一种快速准确的方法。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/e72e/11269219/8f0ed3b7bf1f/fpls-15-1425100-g015.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/e72e/11269219/e61f28ef72b3/fpls-15-1425100-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/e72e/11269219/2fd0124c9674/fpls-15-1425100-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/e72e/11269219/dd3f7f9de41e/fpls-15-1425100-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/e72e/11269219/883a3f216d8a/fpls-15-1425100-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/e72e/11269219/2399f21e20dc/fpls-15-1425100-g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/e72e/11269219/e19bf57efb48/fpls-15-1425100-g006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/e72e/11269219/fd80fdf17471/fpls-15-1425100-g007.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/e72e/11269219/49741da74812/fpls-15-1425100-g008.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/e72e/11269219/6bc46b1120e3/fpls-15-1425100-g009.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/e72e/11269219/94f2b1b9c2a1/fpls-15-1425100-g010.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/e72e/11269219/701deae03b8e/fpls-15-1425100-g011.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/e72e/11269219/290932a78930/fpls-15-1425100-g012.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/e72e/11269219/e891f8f8e5c2/fpls-15-1425100-g013.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/e72e/11269219/79012e195f7e/fpls-15-1425100-g014.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/e72e/11269219/8f0ed3b7bf1f/fpls-15-1425100-g015.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/e72e/11269219/e61f28ef72b3/fpls-15-1425100-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/e72e/11269219/2fd0124c9674/fpls-15-1425100-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/e72e/11269219/dd3f7f9de41e/fpls-15-1425100-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/e72e/11269219/883a3f216d8a/fpls-15-1425100-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/e72e/11269219/2399f21e20dc/fpls-15-1425100-g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/e72e/11269219/e19bf57efb48/fpls-15-1425100-g006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/e72e/11269219/fd80fdf17471/fpls-15-1425100-g007.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/e72e/11269219/49741da74812/fpls-15-1425100-g008.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/e72e/11269219/6bc46b1120e3/fpls-15-1425100-g009.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/e72e/11269219/94f2b1b9c2a1/fpls-15-1425100-g010.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/e72e/11269219/701deae03b8e/fpls-15-1425100-g011.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/e72e/11269219/290932a78930/fpls-15-1425100-g012.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/e72e/11269219/e891f8f8e5c2/fpls-15-1425100-g013.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/e72e/11269219/79012e195f7e/fpls-15-1425100-g014.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/e72e/11269219/8f0ed3b7bf1f/fpls-15-1425100-g015.jpg

相似文献

1
YOLOv8-segANDcal: segmentation, extraction, and calculation of soybean radicle features.YOLOv8-segANDcal：大豆胚根特征的分割、提取与计算
Front Plant Sci. 2024 Jul 11;15:1425100. doi: 10.3389/fpls.2024.1425100. eCollection 2024.
2
YOLOv8-Peas: a lightweight drought tolerance method for peas based on seed germination vigor.YOLOv8-豌豆：一种基于种子萌发活力的豌豆轻量级耐旱方法。
Front Plant Sci. 2023 Sep 28;14:1257947. doi: 10.3389/fpls.2023.1257947. eCollection 2023.
3
Evaluation of cucumber seed germination vigor under salt stress environment based on improved YOLOv8.基于改进的YOLOv8评估盐胁迫环境下黄瓜种子的发芽活力
Front Plant Sci. 2024 Sep 13;15:1447346. doi: 10.3389/fpls.2024.1447346. eCollection 2024.
4
Quantitative proteomics reveals dual effects of calcium on radicle protrusion in soybean.定量蛋白质组学揭示钙对大豆胚根突出的双重作用。
J Proteomics. 2021 Jan 6;230:103999. doi: 10.1016/j.jprot.2020.103999. Epub 2020 Oct 3.
5
Comparing the Salt Tolerance of Different Spring Soybean Varieties at the Germination Stage.不同春大豆品种萌发期耐盐性比较
Plants (Basel). 2023 Jul 27;12(15):2789. doi: 10.3390/plants12152789.
6
Quantitative analysis of the 3D cell shape changes driving soybean germination.定量分析驱动大豆萌发的三维细胞形状变化。
J Exp Bot. 2017 Mar 1;68(7):1531-1537. doi: 10.1093/jxb/erx048.
7
Leaf Segmentation Using Modified YOLOv8-Seg Models.使用改进的YOLOv8-Seg模型进行叶片分割
Life (Basel). 2024 Jun 20;14(6):780. doi: 10.3390/life14060780.
8
Improve Soybean Variety Selection Accuracy Using UAV-Based High-Throughput Phenotyping Technology.利用基于无人机的高通量表型分析技术提高大豆品种选择准确性
Front Plant Sci. 2022 Jan 11;12:768742. doi: 10.3389/fpls.2021.768742. eCollection 2021.
9
Wheat Seed Detection and Counting Method Based on Improved YOLOv8 Model.基于改进 YOLOv8 模型的小麦种子检测与计数方法。
Sensors (Basel). 2024 Mar 3;24(5):1654. doi: 10.3390/s24051654.
10
High-Precision Automated Soybean Phenotypic Feature Extraction Based on Deep Learning and Computer Vision.基于深度学习和计算机视觉的高精度自动化大豆表型特征提取
Plants (Basel). 2024 Sep 19;13(18):2613. doi: 10.3390/plants13182613.

引用本文的文献

1
Full-time sequence assessment of okra seedling vigor under salt stress based on leaf area and leaf growth rate estimation using the YOLOv11-HSECal instance segmentation model.基于使用YOLOv11-HSECal实例分割模型估计叶面积和叶片生长速率的盐胁迫下秋葵幼苗活力的全时序列评估。
Front Plant Sci. 2025 Aug 14;16:1625154. doi: 10.3389/fpls.2025.1625154. eCollection 2025.

本文引用的文献

1
YOLOv8-CML: a lightweight target detection method for color-changing melon ripening in intelligent agriculture.YOLOv8-CML：一种用于智能农业中变色甜瓜成熟度的轻量级目标检测方法。
Sci Rep. 2024 Jun 22;14(1):14400. doi: 10.1038/s41598-024-65293-w.
2
Intrarow Uncut Weed Detection Using You-Only-Look-Once Instance Segmentation for Orchard Plantations.基于单目实例分割的行间未切割杂草检测在果园种植中的应用。
Sensors (Basel). 2024 Jan 30;24(3):893. doi: 10.3390/s24030893.
3
YOLOv8-Peas: a lightweight drought tolerance method for peas based on seed germination vigor.
YOLOv8-豌豆：一种基于种子萌发活力的豌豆轻量级耐旱方法。
Front Plant Sci. 2023 Sep 28;14:1257947. doi: 10.3389/fpls.2023.1257947. eCollection 2023.
4
Segmentation and Phenotype Calculation of Rapeseed Pods Based on YOLO v8 and Mask R-Convolution Neural Networks.基于YOLO v8和Mask R卷积神经网络的油菜角果分割与表型计算
Plants (Basel). 2023 Sep 20;12(18):3328. doi: 10.3390/plants12183328.
5
Tomato Maturity Detection and Counting Model Based on MHSA-YOLOv8.基于 MHSA-YOLOv8 的番茄成熟度检测与计数模型
Sensors (Basel). 2023 Jul 26;23(15):6701. doi: 10.3390/s23156701.
6
Convolutional Neural Network for Object Detection in Garlic Root Cutting Equipment.用于大蒜根茎切割设备中目标检测的卷积神经网络
Foods. 2022 Jul 24;11(15):2197. doi: 10.3390/foods11152197.