APPLICATION OF DEEP LEARNING IN SUSTAINABLE AGRICULTURE: Agricultural Practices in the Brazilian Context - Pandolfi, José Guilherme; dos Reis Lucente , Adriano; Claudio Pandolfi, Marcos Alberto; de Oliveira, Marcus Rogério; Felix, Lucas Emanuel (Revista Interface Tecnológica, 2024)
This article addresses the application of Deep Learning, a branch of artificial intelligence that uses deep neural networks to analyze large datasets and identify complex patterns, in promoting sustainable agriculture. It analyzes its contributions and challenges in the Brazilian context. The main objective is to demonstrate how this technology can optimize agricultural practices, such as crop forecasting, pest and disease detection, and efficient resource management, aligning with the United Nations Sustainable Development Goals (SDGs) 2 and 13. The methodology used consists of qualitative research, analyzing data and information available in academic articles, books, news, and institutional web portals. The results indicate that Deep Learning can increase the accuracy of agricultural forecasts, reduce pesticide use, and optimize the use of water and fertilizers, promoting more efficient and sustainable practices. It is concluded that the adoption of Deep Learning in Brazilian agriculture can be an effective tool to achieve environmental and social goals, encouraging innovation and sustainability. However, challenges related to technological infrastructure and data quality must be addressed to maximize the benefits of this technology.
Keywords: artificial intelligence; input optimization; neural networks; SDGs; sustainability