Zhang, Y., Liu, K., Wang, X. et al. (2 more authors) (2024) Precision fertilisation via spatio-temporal tensor multi-task learning and one-shot learning. IEEE Transactions on AgriFood Electronics. pp. 1-10. ISSN 2771-9529
Abstract
Precision fertilization is essential in agricultural systems for balancing soil nutrients, conserving fertilizer, decreasing emissions, and increasing crop yields. Access to comprehensive and diverse agricultural data is problematic due to the lack of sophisticated sensor and network technologies on the majority of farms, and available agricultural data are generally unstructured and difficult to mine. The absence of agricultural data is, consequently, a significant impediment to the utilization of machine learning approaches for precision fertilization. In this research, we investigate newly gathered genuine agricultural dataset from nine real winter wheat farms in the United Kingdom, which encompass an extensive variety of agricultural variables, including climate, soil nutrients, and farming data. To deal with the spatio-temporal characteristics of agricultural dataset and to address the problem of scarcity in agricultural data, we propose a novel machine learning approach integrating multi-task learning and one-shot learning, which utilizes a multi-dimensional tensor constructed from original data combined with fertilization temporal patterns extracted by contrasting with environmental information from existing real farms to accurately predict the quantity and timing of base and top dressing fertilization. Specifically, agricultural data are converted into a 3-D tensor and tensor decomposition technique is utilized to derive a set of comprehensible spatio-temporal latent factors from the original data. The latent factors are subsequently utilized to construct the spatio-temporal tensor prediction model as multi-task relationships. The proposed one-shot learning approach utilizes the Mahalanobis distance to evaluate the similarity of environmental information between the target farm and existing real-world farms as a determinant of whether to transfer the fertilization temporal pattern of existing farm to the target farm. Comprehensive experiments are conducted to compare the proposed approach with standard regression models utilizing the real-world agricultural dataset. The experimental results demonstrate that our proposed approach presents superior accuracy and stability for fertilization prediction.
Metadata
Item Type: | Article |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © 2024 The Author(s). Except as otherwise noted, this author-accepted version of a journal article published in IEEE Transactions on AgriFood Electronics is made available via the University of Sheffield Research Publications and Copyright Policy under the terms of the Creative Commons Attribution 4.0 International License (CC-BY 4.0), which permits unrestricted use, distribution and reproduction in any medium, provided the original work is properly cited. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ |
Keywords: | Multi-task learning; One-shot learning; Precision fertilisation; Real-world agricultural data; Spatio-temporal tensor |
Dates: |
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Institution: | The University of Sheffield |
Academic Units: | The University of Sheffield > Faculty of Engineering (Sheffield) > Department of Computer Science (Sheffield) |
Funding Information: | Funder Grant number INNOVATE UK 10081615 TS/Y016483/1 INNOVATE UK TS/V002953/1 |
Depositing User: | Symplectic Sheffield |
Date Deposited: | 22 Oct 2024 13:21 |
Last Modified: | 20 Dec 2024 10:43 |
Status: | Published online |
Publisher: | Institute of Electrical and Electronics Engineers |
Refereed: | Yes |
Identification Number: | 10.1109/TAFE.2024.3485949 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:218240 |
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Filename: IEEE_Transactions_on_AgriFood_Electronics_Main_Manuscript_Second_Review.pdf
Licence: CC-BY 4.0