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Machine learning in sustainable agriculture: systematic review and research perspectives
| dc.contributor.author | Botero-Valencia, Juan | |
| dc.contributor.author | García-Pineda, Vanessa | |
| dc.contributor.author | Valencia-Arias, Alejandro | |
| dc.contributor.author | Valencia, Jackeline | |
| dc.contributor.author | Reyes-Vera, Erick | |
| dc.contributor.author | Mejia-Herrera, Mateo | |
| dc.contributor.author | Hernández-García, Ruber | |
| dc.date.accessioned | 2025-06-05T14:54:11Z | |
| dc.date.available | 2025-06-05T14:54:11Z | |
| dc.date.issued | 2025 | |
| dc.identifier.uri | http://repositorio.ucm.cl/handle/ucm/6074 | |
| dc.description.abstract | Machine learning (ML) has revolutionized resource management in agriculture by analyzing vast amounts of data and creating precise predictive models. Precision agriculture improves agricultural productivity and profitability while reducing costs and environmental impact. However, ML implementation faces challenges such as managing large volumes of data and adequate infrastructure. Despite significant advances in ML applications in sustainable agriculture, there is still a lack of deep and systematic understanding in several areas. Challenges include integrating data sources and adapting models to local conditions. This research aims to identify research trends and key players associated with ML use in sustainable agriculture. A systematic review was conducted using the PRISMA methodology by a bibliometric analysis to capture relevant studies from the Scopus and Web of Science databases. The study analyzed the ML literature in sustainable agriculture between 2007 and 2025, identifying 124 articles that meet the criteria for certainty assessment. The findings show a quadratic polynomial growth in the publication of articles on ML in sustainable agriculture, with a notable increase of up to 91% per year. The most productive years were 2024, 2022, and 2023, demonstrating a growing interest in the field. The study highlights the importance of integrating data from multiple sources for improved decision making, soil health monitoring, and understanding the interaction between climate, topography, and soil properties with agricultural land use and crop patterns. Furthermore, ML in sustainable agriculture has evolved from understanding weather data to integrating advanced technologies like the Internet of Things, remote sensing, and smart farming. Finally, the research agenda highlights the need for the deepening and expansion of predominant concepts, such as deep learning and smart farming, to develop more detailed and specialized studies and explore new applications to maximize the benefits of ML in agricultural sustainability. | es_CL |
| dc.language.iso | en | es_CL |
| dc.rights | Atribución-NoComercial-SinDerivadas 3.0 Chile | * |
| dc.rights.uri | http://creativecommons.org/licenses/by-nc-nd/3.0/cl/ | * |
| dc.source | Agriculture, 15(4), 377 | es_CL |
| dc.subject | Deep learning | es_CL |
| dc.subject | Neural networks | es_CL |
| dc.subject | Precision farming | es_CL |
| dc.subject | Internet of Things | es_CL |
| dc.subject | PRISMA 2020 | es_CL |
| dc.title | Machine learning in sustainable agriculture: systematic review and research perspectives | es_CL |
| dc.type | Article | es_CL |
| dc.ucm.facultad | Facultad de Ciencias de la Ingeniería | es_CL |
| dc.ucm.indexacion | Scopus | es_CL |
| dc.ucm.indexacion | Isi | es_CL |
| dc.ucm.uri | mdpi.com/2077-0472/15/4/377 | es_CL |
| dc.ucm.doi | doi.org/10.3390/agriculture15040377 | es_CL |
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