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dc.contributor.authorSharma, Rohit
dc.contributor.authorKamble, Sachin
dc.contributor.authorKumar, Vikas
dc.contributor.authorGunasekaran, Angappa
dc.contributor.authorKumar, Anil
dc.date.accessioned2020-03-16T12:07:44Z
dc.date.available2020-03-16T12:07:44Z
dc.date.issued2020-02-24
dc.identifier.citationKumar, V., Sharma, R., Kamble, S.S., Gunasekaran, A. and Kumar, A., (2020). 'A systematic literature review on machine learning applications for sustainable agriculture supply chain performance'.Computers & Operations Research, pp. 1-47.en_US
dc.identifier.issn0305-0548
dc.identifier.doi10.1016/j.cor.2020.104926
dc.identifier.urihttp://hdl.handle.net/10545/624595
dc.description.abstractAgriculture 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.en_US
dc.description.sponsorshipNAen_US
dc.language.isoenen_US
dc.publisherElsevieren_US
dc.relation.urlhttps://uwe-repository.worktribe.com/output/5430248/a-systematic-literature-review-on-machine-learning-applications-for-sustainable-agriculture-supply-chain-performanceen_US
dc.relation.urlhttps://www.sciencedirect.com/science/article/pii/S0305054820300435en_US
dc.subjectAgricultural supply chainen_US
dc.subjectMachine learningen_US
dc.subjectSustainabilityen_US
dc.subjectSmart farmingen_US
dc.subjectSystematic Literature Reviewen_US
dc.titleA systematic literature review on machine learning applications for sustainable agriculture supply chain performanceen_US
dc.typeArticleen_US
dc.identifier.eissn1873-765X
dc.contributor.departmentNational Institute of Industrial Engineeringen_US
dc.contributor.departmentUniversity of the West of Englanden_US
dc.contributor.departmentCalifornia State University, Bakersfielden_US
dc.contributor.departmentUniversity of Derby
dc.identifier.journalComputers & Operations Researchen_US
dcterms.dateAccepted2020-02-17
dc.author.detail786345en_US


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