Intelligent sensor for controlling agricultural pests and disease-vector insects

• Grant number: 12/50714-7
• Support Opportunities: Regular Research Grants
• Start date: June 01, 2013
• End date: May 31, 2015
• Field of knowledge: Physical Sciences and Mathematics - Computer Science - Computing Methodologies and Techniques
• Agreement: CNPq - First Projects Program
• Principal Investigator: Gustavo Enrique de Almeida Prado Alves Batista
• Grantee: Gustavo Enrique de Almeida Prado Alves Batista
• Host Institution: Instituto de Ciências Matemáticas e de Computação (ICMC). Universidade de São Paulo (USP). São Carlos , SP, Brazil

Abstract

Applications such as intelligent sensors should be able to collect environment information and to make decisions based on input data. An example is an under-development low-cost sensor to detect and classify insects in their species using laser light and machine learning techniques. This sensor is an important step towards the development of intelligent traps able to attract and selectively capture insect species of interest such as disease vectors or agricultural pests, without affecting the beneficial species. The data gathered by the sensor constitutes a data stream with non-stationary characteristics, since the insects metabolism is influenced by environmental conditions such as temperature, humidity and atmospheric pressure. This research grant proposal has two main objectives: the first one is to develop new algorithms to classify in real- time signals from the sensor obtained from the data stream; the second one is to technologically develop the sensor in order to allow the developed machine learning techniques to be embedded in the sensor. (AU)

Scientific publications

(References retrieved automatically from Web of Science and SciELO through information on FAPESP grants and their corresponding numbers as mentioned in the publications by the authors)

SILVA, DIEGO F.; SOUZA, VINICIUS M. A.; ELLIS, DANIEL P. W.; KEOGH, EAMONN J.; BATISTA, GUSTAVO E. A. P. A.. Exploring Low Cost Laser Sensors to Identify Flying Insect Species Evaluation of Machine Learning and Signal Processing Methods. JOURNAL OF INTELLIGENT & ROBOTIC SYSTEMS, v. 80, n. 1, SI, p. S313-S330, DEC 2015. (11/17698-5, 12/50714-7, 13/26151-5)

SILVA, DIEGO FURTADO; ALVES DE SOUZA, VINICIUS MOURAO; PRADO ALVES BATISTA, GUSTAVO ENRIQUE DE ALMEIDA. A comparative study between MFCC and LSF coefficients in automatic recognition of isolated digits pronounced in Portuguese and English. ACTA SCIENTIARUM-TECHNOLOGY, v. 35, n. 4, p. 621-628, 2013. (11/04054-2, 12/07295-3, 11/17698-5, 12/50714-7)

SILVA, DIEGO F.; SOUZA, VINICIUS M. A.; ELLIS, DANIEL P. W.; KEOGH, EAMONN J.; BATISTA, GUSTAVO E. A. P. A.. Exploring Low Cost Laser Sensors to Identify Flying Insect Species Evaluation of Machine Learning and Signal Processing Methods. JOURNAL OF INTELLIGENT & ROBOTIC SYSTEMS, v. 80, p. 18-pg., 2015-12-01. (13/26151-5, 12/50714-7, 11/17698-5)