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Intelligent traps and sensors: an innovative approach to control insect pests and disease vectors

Grant number: 16/04986-6
Support type:Research Grants - eScience and Data Science Program - Regular Program Grants
Duration: June 01, 2017 - May 31, 2019
Field of knowledge:Physical Sciences and Mathematics - Computer Science
Principal Investigator:Gustavo Enrique de Almeida Prado Alves Batista
Grantee:Gustavo Enrique de Almeida Prado Alves Batista
Home Institution: Instituto de Ciências Matemáticas e de Computação (ICMC). Universidade de São Paulo (USP). São Carlos, SP, Brazil
Assoc. researchers:Agenor Mafra Neto ; Claudia Regina Milaré ; Eamonn John Keogh ; Juliano José Corbi ; Pedro Takao Yamamoto ; Ronaldo Cristiano Prati ; Vinícius Mourão Alves de Souza


Insects are undoubtedly important to agriculture, the environment and human health. Many insect species are beneficial to the environment and humans. For example, insects are responsible for pollinating at least two-thirds of all food consumed in the world. Due to its importance to humans, the recent decline in populations of pollinator insects, especially bees, is considered a serious environmental problem; frequently associated with pesticide exposure. In contrast, insect pests destroy over 40 billion U.S. dollars worth of food each year and vectors are responsible for spreading diseases that kill over one million people annually, such as malaria, dengue and chikungunya fevers and zika virus. In this project, we propose an intelligent trap that captures harmful insect species. Such a trap uses a sensor that we have developed over the last years to automatically recognize insect species using wingbeat data. The insect recognition will allow the creation of real-time insect density maps that can be used to support local interventions. For instance, in the case of insect pests, these maps will allow more local use of insecticides and, therefore, a reduced impact over the environment. In the case of disease vectors, this trap will make some sophisticated but highly costly interventions, such as SIT (Sterile Insect Technique), more cost-effective. In this project, we show how this real application can expand the limits of the state-of-the-art research in Computer Science, particularly in Machine Learning and Data Stream Mining areas. In order to demonstrate the practical aspects of our proposal, we will concentrate in the identification of two species: the Asian citrus psyllid, vector of greening, a terrible citrus disease and the Aedes aegypti vector of dengue, chikungunya and yellow fevers, as well as, the zika virus, recently associated with cases of microcephaly in newborns. (AU)

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