Comparative biology between a tropical-equatorial and temperate population of Rhipicephalus microplus from Brazil

Tavares, Caio P.; Sousa, Isabella Chaves; Gomes, Matheus N; Mesquita-sousa, Dauana; Faccini, João Luíz Horácio; Reck, José; Costa-junior, Livio M.; Luz, Hermes Ribeiro

doi:10.11158/saa.28.2.7

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“…For manual evaluation, it is necessary to wait for the oviposition time, which varies between 2 and 3 days, as well as the egg incubation time, which can vary between 27 and 29 days under controlled conditions (27 ± 1 C, 80% RH) (Tavares et al, 2023). Davey, George, and Miller (2006) suggests an egg incubation period between 19 and 26 days under controlled conditions (27 ± 2 C, 92% RH and 12:12 L photoperiod: D), according to the resistance and susceptibility profile of R. microplus populations to acaricides.…”

Section: Discussionmentioning

confidence: 99%

Automatic method based on deep learning to identify and account Rhipicephalus microplus larval hatching

Santos

Tavares

Klafke

et al. 2023

Medical Vet Entomology

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Reports of Rhipicephalus microplus resistant populations worldwide have increased extensively, making it difficult to control this ectoparasite. The adult immersion test, commonly used to screen for acaricide resistance, produces the results only after 40 days of the tick collection because it needs the eggs to be laid and larvae to hatch. The present study aims to develop an automatic method, based on deep learning, to predict the hatching of R. microplus larva based on egg morphology. Initially, the time course of embryonic development of tick eggs was performed to discriminate between viable and non‐viable eggs. Secondly, using artificial intelligence deep learning techniques, a method was developed to classify and count the eggs. The larval hatching rate of three populations of R. microplus was evaluated for the software validation process. Groups of three and six images of eggs with 12 days of embryonic development were submitted to the software to predict the larval hatching percent automatically. The results obtained by the software were compared with the prediction results of the hatching percentage performed manually by the specialist and with the results of the hatching percentage of larvae obtained in the biological assay. The group with three images of each population submitted to the software for automatic prediction of the larval hatching percent presented mean values of 96.35% ± 3.33 (Piracanjuba population), 95.98% ± 3.5 (Desterro population) and 0.0% ± 0.0 (Barbalha population). For groups with six images, the values were 94.41% ± 3.84 (Piracanjuba population), 95.93% ± 2.36 (Desterro population) and 0.0% ± 0.0 (Barbalha population). Biological assays showed the following hatching percentage values: 98% ± 1.73 (Piracanjuba population); 96% ± 2.1 (Desterro population); and 0.14% ± 0.25 (Barbalha population). There was no statistical difference between the evaluated methods. The automatic method for predicting the hatching percentage of R. microplus larvae was validated and proved to be effective, with considerable reduction in time to obtain results.

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Section: Discussionmentioning

confidence: 99%