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Sensor for real-time detection of fruit flies (Anastrepha spp. and Ceratitis capitata) for use in automated traps

Grant number: 19/01034-2
Support Opportunities:Research Grants - Innovative Research in Small Business - PIPE
Duration: March 01, 2020 - February 28, 2021
Field of knowledge:Agronomical Sciences - Agronomy - Plant Health
Principal Investigator:Hugo Rafacho Fernandes
Grantee:Hugo Rafacho Fernandes
Host Company:AFH Soluções Tecnológicas Ltda
CNAE: Consultoria em tecnologia da informação
Tratamento de dados, provedores de serviços de aplicação e serviços de hospedagem na internet
Pesquisa e desenvolvimento experimental em ciências físicas e naturais
City: Campinas
Associated grant(s):21/06402-0 - Automatic trap for fruit fly real time detection (Anastrepha spp. and Ceratitis capitata), AP.PIPE
Associated scholarship(s):20/08237-3 - Sensor for real-time detection of fruit flies (Anastrepha spp. and Ceratitis capitata) for use in automated traps, BP.TT


Monitoring traps are important components of integrated pest management against important species of fruit flies, including representatives of the genus Anastrepha, Ceratitis capitata and Bactrocera carambolae that cause an annual damage, according to the Department of Plant Health of Mapa of R$ 180 million to Brazilian farmers between production losses and costs with the control. Control of these species is often done incorrectly by fruit growers using insecticides in the form of toxic-baits or by coverage without the knowledge of the infecting species, levels of infestation and distribution of hosts. This type of control has several undesirable consequences such as environmental impact, decrease in fruit quality, export restrictions due to the presence of chemical residues and an increase in the cost of production. With the objective of developing a tool for monitoring the species of these pests, and providing information with a greater precision for the decision making by the fruit growers, this proposal establishes the analysis and validation of a solution constituted by the adaptation of sensors in a traditional McPhail-type trap to carry out the identification through the local processing of variables of the species captured in the orchards. This is an interdisciplinary research and lies at the intersection of agriculture, computing and entomology by encompassing the design and implementation of a technology for automated pest monitoring with the aid of distributed computing and the internet of things. The identification of pest species in the production will allow the creation of maps of infestation density in real time, resulting in a support tool for decision making in chemical or biological control in local interventions. (AU)

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