PROTOTYPE OF AN AGRICULTURAL DATA RECOMMENDATION SYSTEM BASED ON A SCALABLE DATA PLATFORM
DOI:
https://doi.org/10.56238/bocav25n74-020Keywords:
Agricultural Production, Technology, Data AnalysisAbstract
This project aims to develop a robust data platform for exploratory analyses in the technical field of agronomy. Concepts of application architecture and cloud computing focused on big data processing were applied. Different types of data were centralized in a scalable and secure platform using AWS services, known for their reliability and high availability. The solution incorporated open-source tools such as Apache Spark and Apache Airflow, responsible for distributed processing and orchestration of data pipelines. The main data sources included meteorological information from the National Institute of Meteorology (INMET) and agricultural data from the Ministry of Agriculture and Livestock (MAPA). The proposed structure enabled detailed processing of this information, allowing for the generation of relevant insights. Analyses were conducted to investigate the influence of thermal sum and rainfall accumulation on the productivity of different corn hybrids, segmented by location and time period. As a final product, an interactive and intuitive dashboard was developed, allowing agronomists to visualize historical data and make future projections based on the processed information. It is concluded that the platform offers a resilient, scalable, and efficient foundation for handling large volumes of data, with great potential to support more precise and well-informed technical decisions in the agricultural sector.
References
AIRFLOW. Airflow Documentation. 2023. Disponível em: <https://airflow.apache.org/docs/>.
AKHTER, R.; SOFI, S. A. Precision agriculture using iot data analytics and machine learning. Journal of King Saud University-Computer and Information Sciences, Elsevier, v. 34, n. 8, p. 5602–5618, 2022. DOI: https://doi.org/10.1016/j.jksuci.2021.05.013
AMAZON. AWS Documentation. 2023. Disponível em: <https://docs.aws.amazon.com/>.
BHATTARAI, B. P. et al. Big data analytics in smart grids: State-of-theart, challenges, opportunities, and future directions. IET Smart Grid, Institution of Engineering and Technology, v. 2, p. 141–154, 6 2019. ISSN 25152947. DOI: https://doi.org/10.1049/iet-stg.2018.0261
BRONSON, K.; KNEZEVIC, I. Big data in food and agriculture. Big Data & Society, Sage Publications Sage UK: London, England, v. 3, n. 1, p. 2053951716648174, 2016. DOI: https://doi.org/10.1177/2053951716648174
DELGADO, J. A. et al. Big data analysis for sustainable agriculture on a geospatial cloud framework. Frontiers in Sustainable Food Systems, Frontiers Media SA, v. 3, p. 54, 2019. DOI: https://doi.org/10.3389/fsufs.2019.00054
DOCKER. Docker Documentation. 2023. Disponível em: <https://docs.docker.com/>.
EDWARDS,C. A. Sustainable agricultural systems. [S.l.]: CRC Press, 2020. DOI: https://doi.org/10.1201/9781003070474
EMBRAPA. Milho, Relações com o clima. 2021. <https://www.embrapa.br/agencia-de-informacao-tecnologica/cultivos/milho/pre-producao/caracteristicas-da-especie-e-relacoes- com-o-ambiente/relacoes-com-o-clima>. Acessado em 14 de novembro de 2023.
HARENSLAK,B. P.; RUITER, J. de. Data Pipelines with Apache Airflow. [S.l.]: Simon and Schuster, 2021.
KAMBLE, S. S.; GUNASEKARAN, A.; GAWANKAR, S. A. Achieving sustainable performance in a data-driven agriculture supply chain: A review for research and applications. International Journal of Production Economics, Elsevier, v. 219, p. 179–194, 1 2020. ISSN0925-5273. DOI: https://doi.org/10.1016/j.ijpe.2019.05.022
KAMILARIS, A.; KARTAKOULLIS, A.; PRENAFETA-BOLDú, F. X. A review on the practice of big data analysis in agriculture. Computers and Electronics in Agriculture, v. 143, p. 23–37, 2017. ISSN 0168-1699. Disponível em: <https://www.sciencedirect.com/science/article/pii/ S0168169917301230>. DOI: https://doi.org/10.1016/j.compag.2017.09.037
KLEPPMANN, M. Designing Data-Intensive Applications. Beijing: O’Reilly, 2017. ISBN 978-1-4493-7332-0. Disponível em: <https://www.safaribooksonline.com/library/view/ designing-data-intensive-applications/9781491903063/>.105
MICROSOFT. Microsoft Documentation. 2023. Disponível em: <https://learn.microsoft.com/pt-br/docs/>. PYTHON. Python Documentation. 2023. Disponível em: <https://docs.python.org/3/>.
PYTHON. Python Documentation. 2023. Disponível em: https://docs.python.org/3/
SALLOUM,S. et al. Big data analytics on apache spark. International Journal of Data Science and Analytics, Springer, v. 1, p. 145–164, 2016. DOI: https://doi.org/10.1007/s41060-016-0027-9
WAGA,D.; RABAH, K. Environmental conditions’ big data management and cloud computing analytics for sustainable agriculture. World Journal of Computer Application and Technology, Horizon Research Publishing Co., Ltd., v. 2, p. 73–81, 3 2014. ISSN 2331-4982. DOI: https://doi.org/10.13189/wjcat.2014.020303
WAGNER, M. V. et al. Estimativa da produtividade do milho em função da disponibilidade hídrica em guarapuava, pr, brasil. Revista Brasileira de Engenharia Agrícola e Ambiental, Departamento de Engenharia Agrícola- UFCG, v. 17, n. 2, p. 170–179, Feb 2013. ISSN 1415-4366. Disponível em: <https://doi.org/10.1590/S1415-43662013000200008>. DOI: https://doi.org/10.1590/S1415-43662013000200008
ZAHARIA, M. et al. Apache spark: a unified engine for big data processing. Communications of the ACM, ACMNewYork, NY, USA, v. 59, n. 11, p. 56–65, 2016. DOI: https://doi.org/10.1145/2934664
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Copyright (c) 2026 Rafael Ignaulin, Sandro Silva de Oliveira, Cristiano Reschke Lajús, Ariel Gustavo Zuquello, Éttore Guilherme Poletto Diel, Fábio José Busnello, Magdalena Reschke Lajús Travi, Mauricio Bedin
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Copyright (c). Conjuncture Bulletin (BOCA)
This work is licensed under a Creative Commons Attribution 4.0 International License.
