AI Chat Paper
Note: Please note that the following content is generated by AMiner AI. SciOpen does not take any responsibility related to this content.
{{lang === 'zh_CN' ? '文章概述' : 'Summary'}}
{{lang === 'en_US' ? '中' : 'Eng'}}
Chat more with AI
Powered by AMiner. Clicking this button will take you away from SciOpen.
Home Plant Phenomics Article
Article Link
Cite
EndNote(RIS) BibTeX
Collect
Submit Manuscript
Show Outline
Outline
Show full outline
Hide outline
Outline
Show full outline
Hide outline
Perspective | Open Access

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

Stefan Paulus1,†, Benjamin Leiding2,†( )
Institute of Sugar Beet Research, Holtenser Landstr. 77, 37079 Göttingen, Germany
Institute for Software and Systems Engineering, TU Clausthal, Wallstr. 6, 38640 Goslar, Germany

†These author contributed equally to this work.

Show Author Information

References

1

Furbank RT, Tester M. Phenomics—Technologies to relieve the phenotyping bottleneck. Trends Plant Sci. 2011;16(12):635–644.
Crossref Google Scholar
2

Wang R, Qiu Y, Zhou Y, Liang Z, Schnable JC. A high-throughput phenotyping pipeline for image processing and functional growth curve analysis. Plant Phenomics. 2020;2020: Article 7481687.
Crossref Google Scholar
3

Rosenqvist E, Großkinsky DK, Ottosen C-O, van de Zedde R. The phenotyping dilemma—The challenges of a diversified phenotyping community. Front Plant Sci. 2019;10;Article 163.
Crossref Google Scholar
4

Papoutsoglou EA, Faria D, Arend D, Arnaud E, Athanasiadis IN, Chaves I, Coppens F, Cornut G, Costa BV, Ćwiek-Kupczyńska H, et al. Enabling reusability of plant phenomic datasets with MIAPPE 1.1. New Phytol. 2020;227(1):260–273.
Crossref Google Scholar
5

Martone ME. FORCE11: Building the future for research communications and e-scholarship. Bioscience. 2015;65(7):635–635.
Crossref Google Scholar
6

van Dijk ADJ, Kootstra G, Kruijer W, de Ridder D. Machine learning in plant science and plant breeding. iScience. 2021;24(1):Article 101890.
Crossref Google Scholar
7

Tsaftaris SA, Scharr H. Sharing the right data right: A symbiosis with machine learning. Trends Plant Sci. 2019;24(2):99–102.
Crossref Google Scholar
8

Bali N, Singla A. Emerging trends in machine learning to predict crop yield and study its influential factors: A survey. Arch Comput Methods Eng. 2021;29(1):95–112.
Crossref Google Scholar
9

Spiekermann M. Data marketplaces: Trends and monetisation of data goods. Intereconomics. 2019;54(4):208–216.
Crossref Google Scholar
10

Ugochukwu AI, Phillips PWB. Data sharing in plant phenotyping research: Perceptions, practices, enablers, barriers and implications for science policy on data management. Plant Phenome J. 2022;5(1):Article e20056.
Crossref Google Scholar
11
Hales D. From selfish nodes to cooperative networksemergent link-based incentives in peer-to-peer networks. Paper presented at: Proceedings of the Fourth International Conference on Peer-to-Peer Computing; 2004 Aug 27–27; Zurich, Switzerland.
12
Lawrenz S, Sharma P, Rausch A. Blockchain technology as an approach for data marketplaces. Paper presented at: ICBCT 2019. Proceedings of the 2019 International Conference on Blockchain Technology; 2019 Mar 15–18; Honolulu, HI.
Crossref
13
Tzianos P, Pipelidis G, Tsiamitros N. Hermes: An open and transparent marketplace for IoT Sensor data over distributed ledgers. Paper presented at: ICBC 2019. Proceedings of the 2019 IEEE International Conference on Blockchain and Cryptocurrency; 2019 May 14–17; Seoul, South Korea.
Crossref
14
Ramachandran GS, Radhakrishnan R, Krishnamachari B. Towards a decentralized data marketplace for smart cities. Paper presented at: ISC2 2018. Proceedings of the 2018 IEEE International Smart Cities Conference; 2018 Sep 16–19; Kansas City, MO.
Crossref
15
Samaniego M, Espana C, Deters R. Access control management for plant phenotyping using integrated blockchain. Paper presented at: BSCI 2019. Proceedings of the 2019 ACM International Symposium on Blockchain and Secure Critical Infrastructure; 2019 Jul 8; Auckland, New Zealand.
Crossref
16

Leiding B, Sharma P, Norta A. The machine-to-everything (M2X) economy: Business enactments, collaborations, and e-governance. Future Internet. 2021;13(12):319.
Crossref Google Scholar
17
Mason P. PostCapitalism: A guide to our future. London (UK): Penguin; 2015.
18

Cap CH, Leiding B. Blogchain—Disruptives Publizieren auf der Blockchain. HMD Praxis der Wirtschaftsinformatik. 2018;55(6):1326–1340.
Crossref Google Scholar
19

Liu C, Xiao Y, Javangula V, Hu Q, Wang S, Cheng X. Normachain: A blockchain-based normalized autonomous transaction settlement system for iot-based e-commerce. IEEE Internet Things J. 2018;6(3):4680–4693.
Crossref Google Scholar
20
Blocher W, Braegelmann TH, Braunberger V, Finck M, Fries M, Gabriel T, Hauck R, Heizmann M, Jentzsch C, Koch J, et al. Rechtshandbuch smart contracts. Munich (Germany): CH Beck; 2019.
Plant Phenomics
Volume 5,
2023
Article number: 0070
DOI: 10.34133/plantphenomics.0070
Cite this article:
Paulus S, Leiding B. Can Distributed Ledgers Help to Overcome the Need of Labeled Data for Agricultural Machine Learning Tasks?. Plant Phenomics, 2023, 5: 0070. https://doi.org/10.34133/plantphenomics.0070

112

Views

3

Crossref

3

Web of Science

3

Scopus

0

CSCD

Altmetrics
Received: 06 April 2022
Accepted: 25 June 2023
Published: 10 July 2023
© 2023 Stefan Paulus and Benjamin Leiding. Exclusive licensee Nanjing Agricultural University. No claim to original U.S. Government Works.

Distributed under a Creative Commons Attribution License (CC BY 4.0).
Return