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Artificial neural networks applied for the study of Asian soybean rust

Grant number: 15/19698-3
Support Opportunities:Scholarships in Brazil - Scientific Initiation
Effective date (Start): March 01, 2016
Effective date (End): December 31, 2018
Field of knowledge:Engineering - Electrical Engineering - Industrial Electronics, Electronic Systems and Controls
Principal Investigator:WALDIR CINTRA DE JESUS JUNIOR
Grantee:Nathalia Hidalgo Leite
Host Institution: Centro de Ciências da Natureza (CCN). Universidade Federal de São Carlos (UFSCAR). Campus de Lagoa do Sino. Buri , SP, Brazil

Abstract

Agriculture is historically one of the main bases of Brazil's economy. According to Brazilian Institute of Geography and Statistics (IBGE, 2014), the production of grains in the country should achieve a new record in 2015, growing 2.5% compared to 2014 and reaching 198.3 million tons. The mission of Ministry of Agriculture, Livestock and Supply (MALS) is to promote sustainable development and agribusiness competitiveness for the benefit of Brazilian society. According to Master Plan of Information Technology of MALS, triennium 2013-2015, Information Technology (IT) is essential in order to achieve the objectives of this ministry (MAPA, 2013).In this context, Precision Agriculture (PA) is an overall, systemic and multidisciplinary theme. It aims the detailed management of all processes involved in the agricultural production and is represented by three points that converge in excellent results: managerial revolution, IT and value added to production (EMBRAPA, 2015).In Brazil, the first research in PA were carried out in the 90s. In 2007, the installation of Brazilian AP Committee on International Symposium on Precision Agriculture, coordinated by MALS, represented a major breakthrough for the agricultural sector. Furthermore, the development of sensors and equipment at reduced costs makes the practice of AP increasingly accessible. In recent years, researchers in Computational Intelligence have developed several models to improve processes in agriculture. Integrating this area, the focus of the project presented here is the application of Artificial Neural Networks (ANN) for the study of Asian soybean rust.

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