Cultivating agricultural evolution: revolutionizing farming through the power of AI and technology
DOI:
https://doi.org/10.37497/rev.artif.intell.educ.v4i00.10Palavras-chave:
AI in Agriculture, Technology Adoption, Predictive Solutions, Environmental Sustainability, Global Food DemandsResumo
Objective: The objective of this study is to explore the current and potential role of Artificial Intelligence (AI) in the agricultural sector. We aim to analyze the adoption and impact of AI solutions in farming, identify challenges, and discuss the prospects for its future integration.
Method: We conducted a comprehensive review of existing literature and ongoing research projects related to AI applications in agriculture. We also examined case studies, technological developments, and AI pioneers in the field.
Results: Our analysis reveals that while AI solutions are being researched and applied in agriculture, there is a gap in widespread industry adoption. Large-scale research projects are underway, and some AI applications are available in the market. However, the development of predictive solutions to address real farming challenges is in the early stages. AI's influence extends across various sectors, contributing to the advancement of technologies such as big data, robotics, and the Internet of Things.
An illustrative example is the styrofoam container device, which utilizes machine learning and computer vision to detect and categorize "safety occurrences." Although not all-encompassing, this technology gathers significant data, such as driver behavior, speed, and surroundings. IFM's system promptly alerts supervisors to safety breaches, enhancing both safety and productivity.
Conclusion: The future of AI in agriculture hinges on the widespread adoption of AI solutions. The agricultural industry remains underserved in terms of AI integration, and the development of predictive solutions is in its early stages. However, AI's impact across sectors underscores its importance. Pioneers like IFM and IBM's patent statistics demonstrate the expanding scope of AI innovation.
Referências
Kaur, G., Gujrati, R., & Uygun, H. (2023). How does AI fit into the Management of Human Resources? Review of Artificial Intelligence in Education, 4(00), e04. https://doi.org/10.37497/rev.artif.intell.education.v4i00.4
McGrath, C., Pargman, T. C., Juth, N., & Palmgren, P. J. (2023). University teachers' perceptions of responsibility and artificial intelligence in higher education: An experimental philosophical study. Computers and Education: Artificial Intelligence, 100139.
Prinsloo, P. (2020). Of ‘black boxes’ and algorithmic decision-making in (higher) education–A commentary. Big Data & Society, 7(1), 2053951720933994.
Slade, S., & Prinsloo, P. (2013). Learning analytics: Ethical issues and dilemmas. American Behavioral Scientist, 57(10), 1510-1529.
Silva, A. de O., & Janes, D. dos S. (2023). Challenges And Opportunities of Artificial Intelligence in Education in A Global Context. Review of Artificial Intelligence in Education, 4(00), e01. https://doi.org/10.37497/rev.artif.intell.education.v4i00.1
Silva, A. de O., & Janes, D. dos S. (2020). Exploring the Role of Artificial Intelligence in Education: A Comprehensive Perspective. Review of Artificial Intelligence in Education, 1(00), e05. https://doi.org/10.37497/rev.artif.intell.education.v1i00.5
Silva, A. de O., & Janes, D. dos S. (2021). The Emergence of ChatGPT and its Implications for Education and Academic Research in the 21st Century. Review of Artificial Intelligence in Education, 2(00), e06. https://doi.org/10.37497/rev.artif.intell.education.v2i00.6
Tambuskar, S. (2022). Challenges and Benefits of 7 ways Artificial Intelligence in Education Sector. Review of Artificial Intelligence in Education, 3(00), e03. https://doi.org/10.37497/rev.artif.intell.education.v3i00.3
Velander, J., Otero, N., Pargman, T. C., & Milrad, M. (2021). “We Know What You Were Doing”. In: Sahin, M., Ifenthaler, D. (Eds.), Visualizations and Dashboards for Learning Analytics. Advances in Analytics for Learning and Teaching. Springer, Cham. https://doi.org/10.1007/978-3-030-81222-5_15
Yang, S. J., Ogata, H., Matsui, T., & Chen, N. S. (2021). Human-centered artificial intelligence in education: Seeing the invisible through the visible. Computers and Education: Artificial Intelligence, 2, 100008.
Zhang, C. (2022). Current Status and Outlook of Higher Education Digital Transformation in China. Review of Artificial Intelligence in Education, 3(00), e02. https://doi.org/10.37497/rev.artif.intell.education.v3i00.2
Downloads
Publicado
Como Citar
Edição
Seção
Licença
Este trabalho está licenciado sob uma licença Creative Commons Attribution 4.0 International License.
Authors who publish with this journal agree to the following terms:
1. Authors who publish in this journal agree to the following terms: the author(s) authorize(s) the publication of the text in the journal;
2. The author(s) ensure(s) that the contribution is original and unpublished and that it is not in the process of evaluation by another journal;
3. The journal is not responsible for the views, ideas and concepts presented in articles, and these are the sole responsibility of the author(s);
4. The publishers reserve the right to make textual adjustments and adapt texts to meet with publication standards.
5. Authors retain copyright and grant the journal the right to first publication, with the work simultaneously licensed under the Creative Commons Atribuição NãoComercial 4.0 internacional, which allows the work to be shared with recognized authorship and initial publication in this journal.
6. Authors are allowed to assume additional contracts separately, for non-exclusive distribution of the version of the work published in this journal (e.g. publish in institutional repository or as a book chapter), with recognition of authorship and initial publication in this journal.
7. Authors are allowed and are encouraged to publish and distribute their work online (e.g. in institutional repositories or on a personal web page) at any point before or during the editorial process, as this can generate positive effects, as well as increase the impact and citations of the published work (see the effect of Free Access) at http://opcit.eprints.org/oacitation-biblio.html
• 8. Authors are able to use ORCID is a system of identification for authors. An ORCID identifier is unique to an individual and acts as a persistent digital identifier to ensure that authors (particularly those with relatively common names) can be distinguished and their work properly attributed.
