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

Author(s)

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]

Affiliation

^[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

• Document type: Journal article
• Source: JOURNAL OF INTELLIGENT & ROBOTIC SYSTEMS; v. 80, n. 1, SI, p. S313-S330, DEC 2015.
• Web of Science Citations: 16

Abstract

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)

• FAPESP's process: 11/17698-5 - Classification of non-stationary data stream with application in sensors for insect identification
• Grantee: Vinícius Mourão Alves de Souza
• Support type: Scholarships in Brazil - Doctorate
• FAPESP's process: 13/26151-5 - Time series analysis by similarity in large scale
• Grantee: Diego Furtado Silva
• Support type: Scholarships in Brazil - Doctorate
• FAPESP's process: 12/50714-7 - Intelligent sensor for controlling agricultural pests and disease-vector insects
• Grantee: Gustavo Enrique de Almeida Prado Alves Batista
• Support type: Regular Research Grants