Implementing artificial intelligence and machine learning algorithms for optimized crop management: a systematic review on data-driven approach to enhancing resource use and agricultural sustainability
<p>Artificial intelligence (AI) and machine learning (ML) are transforming agriculture by enabling data-driven decisions that elevate productivity and sustainability. This review synthesises 95 studies published between 2013 and 2023 that evaluate applications across crop monitoring, yield pre...
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| مؤلفون آخرون: | , , , , , , , , , |
| منشور في: |
2025
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| _version_ | 1852015773043654656 |
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| author | Okechukwu Paul-Chima Ugwu (21578037) |
| author2 | Fabian C. Ogenyi (21578049) Esther Ugo Alum (20880703) Val Hyginus Udoka Eze (21578043) Mariam Basajja (22188196) Jovita Nnenna Ugwu (21578040) Chinyere N. Ugwu (21578046) Regina Idu Ejemot-Nwadiaro (15245416) Michael Ben Okon (21578052) Simeon Ikechukwu Egba (22438849) Uti Daniel Ejim (22438852) |
| author2_role | author author author author author author author author author author |
| author_facet | Okechukwu Paul-Chima Ugwu (21578037) Fabian C. Ogenyi (21578049) Esther Ugo Alum (20880703) Val Hyginus Udoka Eze (21578043) Mariam Basajja (22188196) Jovita Nnenna Ugwu (21578040) Chinyere N. Ugwu (21578046) Regina Idu Ejemot-Nwadiaro (15245416) Michael Ben Okon (21578052) Simeon Ikechukwu Egba (22438849) Uti Daniel Ejim (22438852) |
| author_role | author |
| dc.creator.none.fl_str_mv | Okechukwu Paul-Chima Ugwu (21578037) Fabian C. Ogenyi (21578049) Esther Ugo Alum (20880703) Val Hyginus Udoka Eze (21578043) Mariam Basajja (22188196) Jovita Nnenna Ugwu (21578040) Chinyere N. Ugwu (21578046) Regina Idu Ejemot-Nwadiaro (15245416) Michael Ben Okon (21578052) Simeon Ikechukwu Egba (22438849) Uti Daniel Ejim (22438852) |
| dc.date.none.fl_str_mv | 2025-10-15T16:20:10Z |
| dc.identifier.none.fl_str_mv | 10.6084/m9.figshare.30366451.v1 |
| dc.relation.none.fl_str_mv | https://figshare.com/articles/dataset/Implementing_artificial_intelligence_and_machine_learning_algorithms_for_optimized_crop_management_a_systematic_review_on_data-driven_approach_to_enhancing_resource_use_and_agricultural_sustainability/30366451 |
| dc.rights.none.fl_str_mv | CC BY 4.0 info:eu-repo/semantics/openAccess |
| dc.subject.none.fl_str_mv | Medicine Biotechnology Ecology Science Policy Plant Biology Information Systems not elsewhere classified Artificial intelligence (AI) machine learning (ML) precision agriculture neural networks crop yield prediction smart irrigation and sustainable farming Artificial Intelligence Computer Science (General) Computer Engineering Databases Information & Communication Technology (ICT) Computing & IT Security Software Engineering & Systems Development Supercomputing Networks Plant Engineering & Maintenance |
| dc.title.none.fl_str_mv | Implementing artificial intelligence and machine learning algorithms for optimized crop management: a systematic review on data-driven approach to enhancing resource use and agricultural sustainability |
| dc.type.none.fl_str_mv | Dataset info:eu-repo/semantics/publishedVersion dataset |
| description | <p>Artificial intelligence (AI) and machine learning (ML) are transforming agriculture by enabling data-driven decisions that elevate productivity and sustainability. This review synthesises 95 studies published between 2013 and 2023 that evaluate applications across crop monitoring, yield prediction, and resource optimisation. Reported model accuracies for neural networks, decision trees, and deep learning reached up to 93 percent; deep learning was most accurate but least interpretable. Reported benefits include a 25 percent increase in yield, a 28 percent reduction in costs, 40 percent efficiency gains, 22 percent water savings, 28 percent fertilizer savings, and 35 percent lower nitrogen runoff. Adoption barriers persist, including poor data quality, expensive infrastructure, limited digital literacy, and ethical concerns around data ownership and bias. Integrated, enterprise-scale platforms favor large farms, while mobile AI applications yield 15-30 percent gains for smallholders. Converging technologies blockchain, IoT, and robotics enable integration, and automation can lower labor and input requirements by 35 percent. The review points to the importance of inclusion policies, transparent systems, and global governance. Overall, AI/ML are drivers of socio-technical transition consistent with Sustainability Transitions Theory, necessitating multidisciplinary strategies for sustainable, climate-resilient food systems.</p> |
| eu_rights_str_mv | openAccess |
| id | Manara_eae79a0a3905b1da69a67e306456d844 |
| identifier_str_mv | 10.6084/m9.figshare.30366451.v1 |
| network_acronym_str | Manara |
| network_name_str | ManaraRepo |
| oai_identifier_str | oai:figshare.com:article/30366451 |
| publishDate | 2025 |
| repository.mail.fl_str_mv | |
| repository.name.fl_str_mv | |
| repository_id_str | |
| rights_invalid_str_mv | CC BY 4.0 |
| spelling | Implementing artificial intelligence and machine learning algorithms for optimized crop management: a systematic review on data-driven approach to enhancing resource use and agricultural sustainabilityOkechukwu Paul-Chima Ugwu (21578037)Fabian C. Ogenyi (21578049)Esther Ugo Alum (20880703)Val Hyginus Udoka Eze (21578043)Mariam Basajja (22188196)Jovita Nnenna Ugwu (21578040)Chinyere N. Ugwu (21578046)Regina Idu Ejemot-Nwadiaro (15245416)Michael Ben Okon (21578052)Simeon Ikechukwu Egba (22438849)Uti Daniel Ejim (22438852)MedicineBiotechnologyEcologyScience PolicyPlant BiologyInformation Systems not elsewhere classifiedArtificial intelligence (AI)machine learning (ML)precision agricultureneural networkscrop yield predictionsmart irrigation and sustainable farmingArtificial IntelligenceComputer Science (General)Computer EngineeringDatabasesInformation & Communication Technology (ICT)Computing & IT SecuritySoftware Engineering & Systems DevelopmentSupercomputingNetworksPlant Engineering & Maintenance<p>Artificial intelligence (AI) and machine learning (ML) are transforming agriculture by enabling data-driven decisions that elevate productivity and sustainability. This review synthesises 95 studies published between 2013 and 2023 that evaluate applications across crop monitoring, yield prediction, and resource optimisation. Reported model accuracies for neural networks, decision trees, and deep learning reached up to 93 percent; deep learning was most accurate but least interpretable. Reported benefits include a 25 percent increase in yield, a 28 percent reduction in costs, 40 percent efficiency gains, 22 percent water savings, 28 percent fertilizer savings, and 35 percent lower nitrogen runoff. Adoption barriers persist, including poor data quality, expensive infrastructure, limited digital literacy, and ethical concerns around data ownership and bias. Integrated, enterprise-scale platforms favor large farms, while mobile AI applications yield 15-30 percent gains for smallholders. Converging technologies blockchain, IoT, and robotics enable integration, and automation can lower labor and input requirements by 35 percent. The review points to the importance of inclusion policies, transparent systems, and global governance. Overall, AI/ML are drivers of socio-technical transition consistent with Sustainability Transitions Theory, necessitating multidisciplinary strategies for sustainable, climate-resilient food systems.</p>2025-10-15T16:20:10ZDatasetinfo:eu-repo/semantics/publishedVersiondataset10.6084/m9.figshare.30366451.v1https://figshare.com/articles/dataset/Implementing_artificial_intelligence_and_machine_learning_algorithms_for_optimized_crop_management_a_systematic_review_on_data-driven_approach_to_enhancing_resource_use_and_agricultural_sustainability/30366451CC BY 4.0info:eu-repo/semantics/openAccessoai:figshare.com:article/303664512025-10-15T16:20:10Z |
| spellingShingle | Implementing artificial intelligence and machine learning algorithms for optimized crop management: a systematic review on data-driven approach to enhancing resource use and agricultural sustainability Okechukwu Paul-Chima Ugwu (21578037) Medicine Biotechnology Ecology Science Policy Plant Biology Information Systems not elsewhere classified Artificial intelligence (AI) machine learning (ML) precision agriculture neural networks crop yield prediction smart irrigation and sustainable farming Artificial Intelligence Computer Science (General) Computer Engineering Databases Information & Communication Technology (ICT) Computing & IT Security Software Engineering & Systems Development Supercomputing Networks Plant Engineering & Maintenance |
| status_str | publishedVersion |
| title | Implementing artificial intelligence and machine learning algorithms for optimized crop management: a systematic review on data-driven approach to enhancing resource use and agricultural sustainability |
| title_full | Implementing artificial intelligence and machine learning algorithms for optimized crop management: a systematic review on data-driven approach to enhancing resource use and agricultural sustainability |
| title_fullStr | Implementing artificial intelligence and machine learning algorithms for optimized crop management: a systematic review on data-driven approach to enhancing resource use and agricultural sustainability |
| title_full_unstemmed | Implementing artificial intelligence and machine learning algorithms for optimized crop management: a systematic review on data-driven approach to enhancing resource use and agricultural sustainability |
| title_short | Implementing artificial intelligence and machine learning algorithms for optimized crop management: a systematic review on data-driven approach to enhancing resource use and agricultural sustainability |
| title_sort | Implementing artificial intelligence and machine learning algorithms for optimized crop management: a systematic review on data-driven approach to enhancing resource use and agricultural sustainability |
| topic | Medicine Biotechnology Ecology Science Policy Plant Biology Information Systems not elsewhere classified Artificial intelligence (AI) machine learning (ML) precision agriculture neural networks crop yield prediction smart irrigation and sustainable farming Artificial Intelligence Computer Science (General) Computer Engineering Databases Information & Communication Technology (ICT) Computing & IT Security Software Engineering & Systems Development Supercomputing Networks Plant Engineering & Maintenance |