ADOPTION OF PRECISION AGRICULTURE AND ARTIFICIAL INTELLIGENCE TECHNOLOGIES IN THE TRIÂNGULO MINEIRO: A STUDY WITH RURAL PRODUCERS
Abstract
This study evaluates the level of adoption and perception of rural producers regarding Precision Agriculture (PA) and Artificial Intelligence (AI) technologies in the Triângulo Mineiro region of Brazil. The theoretical framework is grounded in Rogers' (2003) Diffusion of Innovations Theory and the Agriculture 4.0 paradigm as a model for data-driven management. The methodology consisted of a quantitative-descriptive and exploratory survey conducted with a sample of 20 rural producers linked to the area of influence of the Federal Institute of Triângulo Mineiro (IFTM), selected by convenience, using a structured 10-question questionnaire applied in 2025. The results indicate that while knowledge of PA is high (90%), effective utilization is only 40% with significant generational variation: 75% among producers up to 30 years old and 0% over 60 years old. Consolidated technologies such as GPS and automatic guidance systems predominate (70%), while advanced AI tools like drones and farm management and analytics software face adoption barriers. The main challenges identified were high implementation costs (80%) and lack of field connectivity (60%). Age analysis revealed that the lack of technical training disproportionately affects producers over 50 years of age (80% vs. 25% among young people). It is concluded that the sector is experiencing a hybrid transition, in which future optimism (85% investment intention over the next two years) contrasts with the urgent need for infrastructure and technical training to consolidate data-driven management.
Author Biographies
Undergraduate student in Agronomic Engineering.
Doctor of Science in Phytotechnics/Phytopathology.
References
AUBERT, B. A. et al. IT as enabler of sustainable farming: an empirical analysis of farmers' adoption decision of precision agriculture technology. Decision Support Systems, Amsterdam, v. 54, p. 510–520, 2013. DOI: https://doi.org/10.1016/j.dss.2012.07.002
BERNARDES, M. S.; HINOJOSA, L. L. R. Princípios de agricultura de precisão. Piracicaba: ESALQ/USP, 2021.
CARRER, M. J.; SOUZA FILHO, H. M.; VINHOLIS, M. M. B. Determinants of precision agriculture adoption in
Brazil. Precision Agriculture, Dordrecht, v. 23, n. 1, p. 1–21, 2022.
COBLE, K. H. et al. Advancing digital agriculture: new tools for a new era. Applied Economic Perspectives and Policy, Oxford, v. 40, n. 1, p. 12–38, 2018. DOI: https://doi.org/10.1093/aepp/ppx056
FERNANDES, J. L. Agricultura de precisão. Curitiba: Contentus, 2018.
FOOD AND AGRICULTURE ORGANIZATION OF THE UNITED NATIONS (FAO). The future of food and agriculture: trends and challenges. Roma: FAO, 2017. Disponível em: https://www.fao.org/3/i6583e/i6583e.pdf Acesso em: 22 abr. 2026.
GEBBERS, R.; ADAMCHUK, V. I. Precision agriculture and food security. Science, Washington, v. 327, n. 5967, p. 828–831, 2010. DOI: https://doi.org/10.1126/science.1183899
KAMILARIS, A.; PRENAFETA-BOLDÚ, F. X. Deep learning in agriculture: a survey. Computers and Electronics in Agriculture, Amsterdam, v. 147, p. 70–90, 2018. DOI: https://doi.org/10.1016/j.compag.2018.02.016
LIAKOS, K. G. et al. Machine learning in agriculture: a review. Sensors, Basel, v. 18, n. 8, e2674, 2018. DOI: https://doi.org/10.3390/s18082674
PIVOTO, D. et al. Factors influencing the adoption of smart farming in Brazil. Computers and Electronics in Agriculture, Amsterdam, v. 156, p. 476–486, 2019.
ROGERS, Everett M. Diffusion of innovations. 5. ed. New York: Free Press, 2003.
RUSSELL, Stuart; NORVIG, Peter. Inteligência Artificial: uma abordagem moderna. 4. ed. Rio de Janeiro: Elsevier, 2020.
SCHWAB, Klaus. A Quarta Revolução Industrial. Tradução de Daniel Moreira Miranda. São Paulo: Edipro, 2016.
SILVA, C. B. et al. Digital transformation in agriculture: a review. Ciência Rural, Santa Maria, v. 50, n. 11, e20190557, 2020.
SOTO, I. et al. Challenges and opportunities of artificial intelligence applications in agriculture. Frontiers in Plant Science, Lausanne, v. 12, e756587, 2021.
TALAVIYA, T. et al. Implementation of artificial intelligence in agriculture for optimisation of irrigation and application of pesticides and herbicides. Artificial Intelligence in Agriculture, Amsterdam, v. 4, p. 58–73, 2020. DOI: https://doi.org/10.1016/j.aiia.2020.04.002
WOLFERT, S. et al. Big data in smart farming: a review. Agricultural Systems, Amsterdam, v. 153, p. 69–80, 2017. DOI: https://doi.org/10.1016/j.agsy.2017.01.023
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