The DOI matches its Crossref record; 11 of the 11 references it cites were found in a registry or an index.
A systematic literature review on machine learning applications for sustainable agriculture supply chain performance
Open accessAbstract
Abstract Agriculture plays an important role in sustaining all human activities. Major challenges such as overpopulation, competition for resources poses a threat to the food security of the planet. In order to tackle the ever-increasing complex problems in agricultural production systems, advancements in smart farming and precision agriculture offers important tools to address agricultural sustainability challenges. Data analytics hold the key to ensure future food security, food safety, and ecological sustainability. Disruptive information and communication technologies such as machine learning, big data analytics, cloud computing, and blockchain can address several problems such as productivity and yield improvement, water conservation, ensuring soil and plant health, and enhance environmental stewardship. The current study presents a systematic review of machine learning (ML) applications in agricultural supply chains (ASCs). Ninety three research papers were reviewed based on the applications of different ML algorithms in different phases of the ASCs. The study highlights how ASCs can benefit from ML techniques and lead to ASC sustainability. Based on the study findings an ML applications framework for sustainable ASC is proposed. The framework identifies the role of ML algorithms in providing real-time analytic insights for pro-active data-driven decision-making in the ASCs and provides the researchers, practitioners, and policymakers with guidelines on the successful management of ASCs for improved agricultural productivity and sustainability.
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Cites
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“The sustainable ASC is proposed to address the three dimensions of sustainability, namely: Economical, Social, and Environmental (Elkington, 1998).”
BackgroundMachine Learning in Agriculture: A Review
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Self-citationKummu, M., de Moel, H., Porkka, M., Siebert, S., Varis, O., & Ward, P. J. (2012) · Science of The Total Environment · doi:10.1016/j.scitotenv.2012.08.092 · Cited by 1,300
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Self-citationProcessed foods: Contributions to nutrition
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Spatio-temporal downscaling of gridded crop model yield estimates based on machine learning
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Cited by
Machine learning: Algorithms, real-world applications and research directions
Sarker, I. H. (2021) · SN Computer Science · doi:10.1007/s42979-021-00592-x · Cited by 5,583
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Self-citationBorges, A. F. S., Laurindo, F. J. B., Spínola, M. M., Gonçalves, R. F., & Mattos, C. A. (2021) · International Journal of Information Management · doi:10.1016/j.ijinfomgt.2020.102225 · Cited by 760
The digitization of agricultural industry – a systematic literature review on agriculture 4.0
Abbasi, R., Martinez, P., & Ahmad, R. (2022) · Smart Agricultural Technology · doi:10.1016/j.atech.2022.100042 · Cited by 531
A review of green artificial intelligence: Towards a more sustainable future
Bolón-Canedo, V., Morán-Fernández, L., Cancela, B., & Alonso-Betanzos, A. (2024) · Neurocomputing · doi:10.1016/j.neucom.2024.128096 · Cited by 486
Metadata
- Authors
- Sharma, R., Kamble, S. S., Gunasekaran, A., Kumar, V., & Kumar, A.
- Venue
- Computers & Operations Research
- Publisher
- Elsevier BV
- Published
- 2020-07
- Volume
- 119
- Pages
- 104926
- ISSN
- 0305-0548 (ISSN-L)
- OpenAlex
- W3007397514
- Cited by
- 766
- References
- 11
- FWCI
- 11.71
- Type
- journal article
- Language
- en
- Open access
- green
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
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