SMART: Sustainable Management of Agriculture with the Intelligent Computing Continuum

• Grant number: 24/15527-9
• Support Opportunities: Research Program on Global Climate Change - Thematic Grants
• Start date: January 01, 2025
• End date: December 31, 2027
• Field of knowledge: Physical Sciences and Mathematics - Computer Science - Computer Systems
• Principal Investigator: Carlos Alberto Kamienski
• Grantee: Carlos Alberto Kamienski
• Principal researcher abroad: Lei Xue
• Institution abroad: Sun Yat-Sen University, China
• Host Institution: Centro de Matemática, Computação e Cognição (CMCC). Universidade Federal do ABC (UFABC). Ministério da Educação (Brasil). Santo André , SP, Brazil
• Pesquisadores principais: Luís Henrique Bassoi ; Reinaldo Augusto da Costa Bianchi
• Associated researchers: André Torre Neto ; Carlos Manoel Pedro Vaz ; Dener Edson Ottolini Guedes da Silva ; Ednaldo José Ferreira ; Eduardo Antonio Speranza ; Fabíola Martins Campos de Oliveira Genari ; Gabriela Oliveira Biondi ; João Henrique Kleinschmidt ; Juliana Polizel ; Ladislau Marcelino Rabello ; Luiz Fernando Bittencourt ; Paulo Sergio de Paula Herrmann Junior ; Ronaldo Cristiano Prati

Abstract

Food security remains a significant global concern, particularly in large countries like Brazil and China. Modern agriculture faces the dual challenge of increasing crop productivity while ensuring environmental sustainability amid climate change. Precise and smart agriculture management is crucial, leveraging technologies like IoT, 5G/6G, and AI to enhance decision-making, reduce resource usage, and minimize environmental impact. Innovative agriculture applications generate vast amounts of data from IoT sensors and remote sensing. However, the distributed nature of real-world infrastructures complicates data management. This is where the IoT Computing Continuum, which includes sensors, edge computing nodes, and cloud datacenters, comes into play. It handles the end-to-end data flow, a complex task that requires innovative solutions. Yet, ensuring a reliable dataflow for sophisticated models that enhance precision, food security, and environmental protection remains challenging. The SMART project aims to develop and deploy trustworthy platforms across the IoT computing continuum, from field sensors to the cloud. It will utilize various sensors, data sources, networking technologies, data management, and machine learning to ensure reliable dataflow and trustworthiness. The project will be piloted in Brazil and China, focusing on coffee and wheat crops. It will emphasize smart irrigation and fertilization to reduce water and fertilizer usage. This approach aims to contribute to sustainability goals and address the challenges of climate change. (AU)