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Can Distributed Ledgers Help to Overcome the Need of Labeled Data for Agricultural Machine Learning Tasks?

作者信息

Paulus Stefan, Leiding Benjamin

机构信息

Institute of Sugar Beet Research, Holtenser Landstr. 77, 37079 Göttingen, Germany.

Institute for Software and Systems Engineering, TU Clausthal, Wallstr. 6, 38640 Goslar, Germany.

出版信息

Plant Phenomics. 2023 Jul 10;5:0070. doi: 10.34133/plantphenomics.0070. eCollection 2023.

DOI:10.34133/plantphenomics.0070
PMID:37434757
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC10332799/
Abstract
摘要
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a7d7/10332799/7fdb0039c215/plantphenomics.0070.fig.002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a7d7/10332799/fd9ff7b38c41/plantphenomics.0070.fig.001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a7d7/10332799/7fdb0039c215/plantphenomics.0070.fig.002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a7d7/10332799/fd9ff7b38c41/plantphenomics.0070.fig.001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a7d7/10332799/7fdb0039c215/plantphenomics.0070.fig.002.jpg

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本文引用的文献

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Machine learning in plant science and plant breeding.植物科学与植物育种中的机器学习
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2
A High-Throughput Phenotyping Pipeline for Image Processing and Functional Growth Curve Analysis.一种用于图像处理和功能生长曲线分析的高通量表型分析流程
Plant Phenomics. 2020 Jul 14;2020:7481687. doi: 10.34133/2020/7481687. eCollection 2020.
3
Enabling reusability of plant phenomic datasets with MIAPPE 1.1.利用MIAPPE 1.1实现植物表型组学数据集的可重复使用性。
New Phytol. 2020 Jul;227(1):260-273. doi: 10.1111/nph.16544. Epub 2020 Apr 25.
4
The Phenotyping Dilemma-The Challenges of a Diversified Phenotyping Community.表型分型困境——多元化表型分型群体面临的挑战
Front Plant Sci. 2019 Feb 28;10:163. doi: 10.3389/fpls.2019.00163. eCollection 2019.
5
Sharing the Right Data Right: A Symbiosis with Machine Learning.分享正确的数据：与机器学习共生。
Trends Plant Sci. 2019 Feb;24(2):99-102. doi: 10.1016/j.tplants.2018.10.016. Epub 2018 Nov 26.
6
Phenomics--technologies to relieve the phenotyping bottleneck.表型组学——缓解表型分析瓶颈的技术。
Trends Plant Sci. 2011 Dec;16(12):635-44. doi: 10.1016/j.tplants.2011.09.005. Epub 2011 Nov 9.