METADATA: THE KEY TO SUSTAINABLE PRECISION FARMING AND HIGHPRODUCTIVITY IN SESAME CULTIVATION

Authors

DOI:

https://doi.org/10.31413/nat.v14i1.20756


Keywords:

Sesamum indicum L scientometric analysis, agricultural practices, productivity

Abstract

Precision agriculture, based on advanced technologies such as remote sensing and big data
analysis, has revolutionized agricultural management, allowing data-based decisions. In the case of sesame
(Sesamum indicum L.), known for its adaptability to climate change and high economic value, metadata
stands out as strategic. They organized and described information obtained by sensors, drones and
geoprocessing systems, facilitating the analysis of essential variables such as supervision, fertilization and
supervision. This study seeks to explore the role of metadata in optimizing sustainable sesame
management, focusing on the efficient use of resources and the reduction of environmental impacts. The
methodology included scientometric analyzes of 66 articles published between 2019 and 2024, extracted
from the Web of Science and Scopus databases and processed in the RStudio software with the Bibliometrix
package. The results highlight significant advances in the use of emerging technologies to increase the
resilience and productivity of this crop, responding to growing demands for traceable and sustainable
products. It is concluded that sesame is strategic for food security and agricultural sustainability, especially
in semi-arid regions. The integration of metadata with innovative technologies, improvements in
management, promoting approved agricultural practices to global demands.

References

ALVARES, C. A.; STAPE, J. L.; SENTELHAS, P. C.; GONÇALVES, J. L. de M.; SPAROVEK, G. Köppen’s climate classification map for Brazil. Meteorologische Zeitschrift, v. 22, n. 6, p. 711-728, 2013. https://doi.org/10.1127/0941-2948/2013/0507

CAMPOS, J. C. C.; LEITE, H. G. Mensuração Florestal: perguntas e respostas. 5. ed. Editora UFV, 2017. 636p.

CARMONA, I. N.; AQUINO, M. G. C. de; ROCHA, D. Í. S. da; SILVA, J. J. das N.; FICAGNA, A. G.; BALONEQUE, D. D.; OTAKE, M. Y. F.; PAULETTO, D. Variáveis morfométricas de três espécies florestais em sistema agroflorestal. Revista Agroecossistemas, v. 10, n. 1, e131, 2018. https://doi.org/10.18542/ragros.v10i1.5158

CASAS, G. G.; FARDIN, L. P.; SILVA, S.; OLIVEIRA NETO, R. R. de; BINOTI, D. H. B.; LEITE, R. V.; DOMICIANO, C. A. R.; LOPES, L. S. de S.; CRUZ, J. P. da; REIS, T. L. dos; LEITE, H. G. Improving yield projections from early ages in eucalypt plantations with the clutter model and artificial neural networks. Pertanika Journal of Science and Technology, v. 30, n. 2, p. 1257-1272, 2022. https://doi.org/10.47836/pjst.30.2.22

CHEN, Z.; XIAO, F.; GUO, F.; YAN, J. Interpretable machine learning for building energy management: A state-of-the-art review. Advances in Applied Energy, v. 9, e100123, 2023. https://doi.org/10.1016/j.adapen.2023.100123

DOS SANTOS, M. L.; RODRIGUES, R. P.; LIMA, M. D. R.; MARTINS, W. B. R.; COSTA, B. C.; SUZUKI, P. M. Hypsometric models for a clonal plantation of Tectona grandis Linn F. subjected to selective thinning. Revista Agro@Mbiente On-Line, v. 13, e35, 2019. https://doi.org/10.18227/1982-8470ragro.v13i0.5292

GÜNER, Ş. T.; DIAMANTOPOULOU, M. J.; ÖZÇELIK, R. Diameter distributions in Pinus sylvestris L. stands: evaluating modelling approaches including a machine learning technique. Journal of Forestry Research, v. 34, n. 6, p. 1829-1842, 2023. https://doi.org/10.1007/s11676-023-01625-2

LOPES, L. S. D. S.; RODE, R.; PAULETTO, D.; BALONEQUE, D. D.; SILVA, A. R.; SANTOS, K. N. F. dos. Ajuste dos modelos de taper e do sortimento de toras de mogno africano em sistemas agroflorestais em Belterra, Pará. Revista Agroecossistemas, v. 10, n. 1, e5213, 2018. https://doi.org/10.18542/ragros.v10i1.5213

MARTORANO, L. G.; SOARES, W. B.; MORAES, J. R. da S. C. de; NASCIMENTO, W.; APARECIDO, L. E. de O.; VILLA, P. M. Climatology of air temperature in Belterra: thermal regulation ecosystem services provided by the Tapajós National Forest in the Amazon. Revista Brasileira de Meteorologia, v. 36, n. 2, p. 327-337, 2021. https://doi.org/10.1590/0102-77863620015

NUNES, S.; GASTAUER, M.; CAVALCANTE, R. B. L.; RAMOS, S. J.; CALDEIRA, C. F.; SILVA, D.; RODRIGUES, R. R.; SALOMÃO, R.; OLIVEIRA, M.; SOUZA-FILHO, P. W. M.; SIQUEIRA, J. O. Challenges and opportunities for large-scale reforestation in the Eastern Amazon using native species. Forest Ecology and Management, v. 466, e118120, 2020. https://doi.org/10.1016/j.foreco.2020.118120

RYO, M. Explainable artificial intelligence and interpretable machine learning for agricultural data analysis. Artificial Intelligence in Agriculture, v. 6, p. 257-265, 2022. https://doi.org/10.1016/j.aiia.2022.11.003

SALEKIN, S.; CATALÁN, C. H.; BOCZNIEWICZ, D.; PHIRI, D.; MORGENROTH, J.; MEASON, D. F.; MASON, E. G. Global tree taper modelling: a review of applications, methods, functions, and their parameters. Forests, v. 12, n. 7, e913, 2021. https://doi.org/10.3390/f12070913

SANTOS, M. L. dos; MIGUEL, E. P.; NAPPO, M. E.; SOUZA, H. J. de; SANTOS, C. R. C. dos; SILVA, J. N. M.; MATRICARDI, E. A. T. Approaches to Forest Site Classification as an Indicator of Teak Volume Production. Forests, v. 14, n. 8, e1613, 2023. https://doi.org/10.3390/f14081613

SILVA, G. C. C.; NEVES, J. C. L.; MARCATTI, G. E.; SOARES, C. P. B.; CALEGARIO, N.; JÚNIOR, C. A. A.; GONZÁLES, D. G. E.; GLERIANI, J. M.; BINOTI, D. H. B.; PAIVA, H. N. de; LEITE, H. G. Improving 3-PG calibration and parameterization using artificial neural networks. Ecological Modelling, v. 479, e110301, 2023. https://doi.org/10.1016/j.ecolmodel.2023.110301

SMITH, J. E.; DOMKE, G. M.; WOODALL, C. W. Predicting downed woody material carbon stocks in forests of the conterminous United States. Science of the Total Environment, v. 803, e150061, 2022. https://doi.org/10.1016/j.scitotenv.2021.150061

SOUZA, G. S. A. de; COSENZA, D. N.; ARAÚJO, A. C. da S. C.; PIMENTA, L. V. A.; SOUZA, R. B.; ALMEIDA, F. M.; LEITE, H. G. Evaluation of non-linear taper equations for predicting the diameter of eucalyptus trees. Revista Árvore, v. 42, n. 1, e420102 2018. https://doi.org/10.1590/1806-90882018000100002

TAVARES JÚNIOR, I. da S.; SOUZA, J. R. M. de; LOPES, L. S. de S.; FARDIN, L. P.; CASAS, G. G.; OLIVEIRA NETO, R. R. de; LEITE, R. V.; LEITE, H. G. Machine learning and regression models to predict multiple tree stem volumes for teak. Southern Forests: a Journal of Forest Science, v. 83, n. 4, p. 294-302, 2021. https://doi.org/10.2989/20702620.2021.1994345

VAPNIK, V. N. The Nature of Statistical Learning Theory. New York, NY: Springer, 2000. 334p.

VIEIRA, I. C. G.; SILVA, J. M. C. da. Zero deforestation and degradation in the Brazilian Amazon. Trends in Ecology & Evolution, v. 39, n. 5, p. 413-416, 2024. https://doi.org/10.1016/j.tree.2024.03.004

YANG, S.-I.; BRANDEIS, T. J.; HELMER, E. H.; MARCANO-VEGA, H. Predicting species-specific diameter growth rate for Caribbean trees using mixed-effects extreme gradient boosting. Forest Ecology and Management, v. 580, e122520, 2025. https://doi.org/10.1016/j.foreco.2025.122520

ZHOU, Z.-H. Machine Learning. Singapore: Springer Singapore, 2021. 460p.

Published

2026-02-24

Issue

Section

Engenharia Florestal / Forest Engineering

How to Cite

METADATA: THE KEY TO SUSTAINABLE PRECISION FARMING AND HIGHPRODUCTIVITY IN SESAME CULTIVATION. (2026). Nativa, 14(1), e20756. https://doi.org/10.31413/nat.v14i1.20756

Most read articles by the same author(s)