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


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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Scientific publications (6)
(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)
MOURA, LIDIA; DE NADAI, BARBARA L.; BERNEGOSSI, ALINE C.; FELIPE, MAYARA C.; CASTRO, GLEYSON B.; CORBI, JULIANO J. Be quick or be dead: high temperatures reduce Aedes aegypti (Diptera: Culicidae) larval development time and pyriproxyfen larvicide efficiency in laboratory conditions. INTERNATIONAL JOURNAL OF TROPICAL INSECT SCIENCE, JAN 2021. Web of Science Citations: 0.
SOUZA, VINICIUS M. A.; DOS REIS, DENIS M.; MALETZKE, ANDRE G.; BATISTA, GUSTAVO E. A. P. A. Challenges in benchmarking stream learning algorithms with real-world data. DATA MINING AND KNOWLEDGE DISCOVERY, JUL 2020. Web of Science Citations: 0.
MOURA, LIDIA; DE NADAI, BARBARA LEPRETTI; CORBI, JULIANO J. What does not kill it does not always make it stronger: High temperatures in pyriproxyfen treatments produce Aedes aegypti adults with reduced longevity and smaller females. JOURNAL OF ASIA-PACIFIC ENTOMOLOGY, v. 23, n. 2, p. 529-535, JUN 2020. Web of Science Citations: 0.
SABINO PARMEZAN, ANTONIO RAFAEL; SOUZA, VINICIUS M. A.; BATISTA, GUSTAVO E. A. P. A. Evaluation of statistical and machine learning models for time series prediction: Identifying the state-of-the-art and the best conditions for the use of each model. INFORMATION SCIENCES, v. 484, p. 302-337, MAY 2019. Web of Science Citations: 2.
SILVA, DIEGO F.; YEH, CHIN-CHIA M.; ZHU, YAN; BATISTA, GUSTAVO E. A. P. A.; KEOGH, EAMONN. Fast Similarity Matrix Profile for Music Analysis and Exploration. IEEE TRANSACTIONS ON MULTIMEDIA, v. 21, n. 1, p. 29-38, JAN 2019. Web of Science Citations: 1.
SILVA, DIEGO F.; GIUSTI, RAFAEL; KEOGH, EAMONN; BATISTA, GUSTAVO E. A. P. A. Speeding up similarity search under dynamic time warping by pruning unpromising alignments. DATA MINING AND KNOWLEDGE DISCOVERY, v. 32, n. 4, p. 988-1016, JUL 2018. Web of Science Citations: 9.

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