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基于深度学习和多摄像头的切花瓶插寿命监测系统

Vase-Life Monitoring System for Cut Flowers Using Deep Learning and Multiple Cameras.

作者信息

Ham Ji Yeong, Kim Yong-Tae, Ha Suong Tuyet Thi, In Byung-Chun

机构信息

Department of Smart Horticultural Science, Andong National University, Andong 36729, Republic of Korea.

出版信息

Plants (Basel). 2025 Apr 1;14(7):1076. doi: 10.3390/plants14071076.

DOI:10.3390/plants14071076
PMID:40219143
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC11991080/
Abstract

Here, we developed a vase-life monitoring system (VMS) to automatically and accurately assess the post-harvest quality and vase life (VL) of cut roses. The VMS integrates camera imaging with the YOLOv8 (You Only Look Once version 8) deep learning algorithm to continuously monitor major physiological parameters including flower opening, fresh weight, water uptake, and gray mold disease incidence. Our results showed that the VMS can automatically measure the main physiological factors of cut roses by obtaining precise and consistent data. The values measured for physiology and disease by the VMS closely correlated with those measured by observation (OBS). Additionally, YOLOv8 achieved a high performance in the model by obtaining an object detection accuracy of 90%. Additionally, the mAP0.5 supported the high accuracy of the model in evaluating the VL of cut roses. Regression analysis revealed a strong correlation between the VL, VMS, and OBS. The VMS incorporating the microscope detected physiological and disease factors in the early stages of development. These results show that the plant monitoring system incorporating a microscope is highly effective for evaluating the post-harvest quality of cut roses. The early detection method using the VMS could also be applied to the flower breeding process, which requires rapid measurements of important characteristics of flower species, such as VL and disease resistance, to develop superior cultivars.

摘要

在此,我们开发了一种瓶插寿命监测系统(VMS),以自动、准确地评估切花玫瑰的采后品质和瓶插寿命(VL)。该VMS将相机成像与YOLOv8(You Only Look Once版本8)深度学习算法相结合,以持续监测包括花朵开放、鲜重、水分吸收和灰霉病发病率在内的主要生理参数。我们的结果表明,VMS能够通过获取精确且一致的数据自动测量切花玫瑰的主要生理因素。VMS测量的生理和病害值与通过观察(OBS)测量的值密切相关。此外,YOLOv8通过获得90%的目标检测准确率在模型中实现了高性能。此外,mAP0.5支持该模型在评估切花玫瑰瓶插寿命方面的高精度。回归分析表明瓶插寿命、VMS和OBS之间存在强相关性。结合显微镜的VMS在发育早期检测到了生理和病害因素。这些结果表明,结合显微镜的植物监测系统在评估切花玫瑰采后品质方面非常有效。使用VMS的早期检测方法也可应用于花卉育种过程,该过程需要快速测量花卉品种的重要特征,如瓶插寿命和抗病性,以培育优良品种。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/409d/11991080/cd8ecb02b83e/plants-14-01076-g011.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/409d/11991080/8a679f93df1e/plants-14-01076-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/409d/11991080/f1cce6fe696b/plants-14-01076-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/409d/11991080/42b1aae149fb/plants-14-01076-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/409d/11991080/b5cff88701b3/plants-14-01076-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/409d/11991080/e2b5e8bf42e7/plants-14-01076-g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/409d/11991080/33262dd4a690/plants-14-01076-g006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/409d/11991080/2e0ee4f19643/plants-14-01076-g007.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/409d/11991080/8a6da0a9bd96/plants-14-01076-g008.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/409d/11991080/595ce03207ef/plants-14-01076-g009.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/409d/11991080/ae46f9951599/plants-14-01076-g010.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/409d/11991080/cd8ecb02b83e/plants-14-01076-g011.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/409d/11991080/8a679f93df1e/plants-14-01076-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/409d/11991080/f1cce6fe696b/plants-14-01076-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/409d/11991080/42b1aae149fb/plants-14-01076-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/409d/11991080/b5cff88701b3/plants-14-01076-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/409d/11991080/e2b5e8bf42e7/plants-14-01076-g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/409d/11991080/33262dd4a690/plants-14-01076-g006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/409d/11991080/2e0ee4f19643/plants-14-01076-g007.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/409d/11991080/8a6da0a9bd96/plants-14-01076-g008.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/409d/11991080/595ce03207ef/plants-14-01076-g009.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/409d/11991080/ae46f9951599/plants-14-01076-g010.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/409d/11991080/cd8ecb02b83e/plants-14-01076-g011.jpg

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Development of a longevity prediction model for cut roses using hyperspectral imaging and a convolutional neural network.
利用高光谱成像和卷积神经网络开发用于切花玫瑰的寿命预测模型
Front Plant Sci. 2024 Jan 10;14:1296473. doi: 10.3389/fpls.2023.1296473. eCollection 2023.
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Plants (Basel). 2023 Dec 6;12(24):4087. doi: 10.3390/plants12244087.
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Effect of preharvest conditions on cut-flower quality.采前条件对切花品质的影响。
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