| Full text | |
| Author(s): |
da Silva, Allan Bastos
;
Lopes Siqueira, Thiago Luis
Total Authors: 2
|
| Document type: | Journal article |
| Source: | 2024 L LATIN AMERICAN COMPUTER CONFERENCE, CLEI 2024; v. N/A, p. 10-pg., 2024-01-01. |
| Abstract | |
Immediate attention and efforts are essential to mitigate hunger throughout the world. Although Latin America has diverse conditions to produce and offer a variety of foods, the improvement of production systems is continuous to enhance food security. Digital farming comprises cutting-edge technologies while smart farms are equipped with solutions that encompass internet of things (IoT), big data and artificial intelligence, for instance. The e-Science is supported by theory, experimentation, and computational methods to connect a multidisciplinary collaborative network to research, develop and innovate to create solutions. Both agro-industry and academia can benefit from high precision and quality data collected in smart farms and made available for different stakeholders to analyze them. In this paper, we present an information system that has been designed, implemented and evaluated to manage scientific data regarding digital farming, which: (i) can receive field data acquired and transferred by IoT devices placed in smart farms; (ii) stores data and enables data retrieval; (iii) allows the user to create, read, update and remove experiments and phases; (iv) allows the user to register IoT devices and attach files and URLs related to the experiments; (v) enables sharing experiment's data among users by means of requests and permissions; (vi) displays data in tables and charts and enables filtering. The evaluation of the system comprised a demo for stakeholders, execution while collecting field data, deployment and scalability simulations, and execution on smartphones. All of them succeeded. Our system can improve digital farming and promote e-Science as it provides means of receiving, storing, retrieving, visualizing, and sharing scientific data sourced from smart farms. Furthermore, it can be used to motivate collaborative research, development, and innovations to benefit food security. (AU) | |
| FAPESP's process: | 22/07442-8 - Intelligent systems: predictive modeling and internet of things for animal production in agriculture 4.0 |
| Grantee: | Iran José Oliveira da Silva |
| Support Opportunities: | Regular Research Grants |