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(Referência obtida automaticamente do Web of Science, por meio da informação sobre o financiamento pela FAPESP e o número do processo correspondente, incluída na publicação pelos autores.)

Exploring Low Cost Laser Sensors to Identify Flying Insect Species Evaluation of Machine Learning and Signal Processing Methods

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Silva, Diego F. [1] ; Souza, Vinicius M. A. [1] ; Ellis, Daniel P. W. [2] ; Keogh, Eamonn J. [3] ; Batista, Gustavo E. A. P. A. [1]
Número total de Autores: 5
Afiliação do(s) autor(es):
[1] Univ Sao Paulo, Inst Ciencias Matemat & Computacao, BR-13560970 Sao Carlos, SP - Brazil
[2] Columbia Univ, Elect Engn, New York, NY 10027 - USA
[3] Univ Calif Riverside, Dept Comp Sci & Engn, Riverside, CA 92521 - USA
Número total de Afiliações: 3
Tipo de documento: Artigo Científico
Fonte: JOURNAL OF INTELLIGENT & ROBOTIC SYSTEMS; v. 80, n. 1, SI, p. S313-S330, DEC 2015.
Citações Web of Science: 16

Insects have a close relationship with the humanity, in both positive and negative ways. Mosquito borne diseases kill millions of people and insect pests consume and destroy around US \$40 billion worth of food each year. In contrast, insects pollinate at least two-thirds of all the food consumed in the world. In order to control populations of disease vectors and agricultural pests, researchers in entomology have developed numerous methods including chemical, biological and mechanical approaches. However, without the knowledge of the exact location of the insects, the use of these techniques becomes costly and inefficient. We are developing a novel sensor as a tool to control disease vectors and agricultural pests. This sensor, which is built from inexpensive commodity electronics, captures insect flight information using laser light and classifies the insects according to their species. The use of machine learning techniques allows the sensor to automatically identify the species without human intervention. Finally, the sensor can provide real-time estimates of insect species with virtually no time gap between the insect identification and the delivery of population estimates. In this paper, we present our solution to the most important challenge to make this sensor practical: the creation of an accurate classification system. We show that, with the correct combination of feature extraction and machine learning techniques, we can achieve an accuracy of almost 90 % in the task of identifying the correct insect species among nine species. Specifically, we show that we can achieve an accuracy of 95 % in the task of correctly recognizing if a given event was generated by a disease vector mosquito. (AU)

Processo FAPESP: 13/26151-5 - Análise de séries temporais por similaridade em larga escala
Beneficiário:Diego Furtado Silva
Linha de fomento: Bolsas no Brasil - Doutorado
Processo FAPESP: 12/50714-7 - Sensores inteligentes para controle de pragas agrícolas e insetos vetores de doenças
Beneficiário:Gustavo Enrique de Almeida Prado Alves Batista
Linha de fomento: Auxílio à Pesquisa - Regular
Processo FAPESP: 11/17698-5 - Classificação de fluxo de dados não estacionários com aplicação em sensores identificadores de insetos
Beneficiário:Vinícius Mourão Alves de Souza
Linha de fomento: Bolsas no Brasil - Doutorado