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The efficiency of a new automated mosquito larval counter and its impact on larval survival.

W MamaiH MaigaM GárdosP BánN S Bimbilé SomdaA KonczalT WallnerAndrew Gordon ParkerF BalestrinoH YamadaJ R L GillesJ Bouyer
Published in: Scientific reports (2019)
To achieve consistent and standardized rearing for mosquito immature stages, it is crucial to control the initial number of larvae present in each larval tray. In addition, maintaining an optimal and synchronized development rate of larvae is essential to maximize the pupal production and optimize male sorting in a mass-rearing setting. Manual counting is labor intensive, time consuming and error prone. Therefore, this study aimed to investigate the use of a customized automated counter for the quantification of mosquito larvae. The present prototype of the mosquito larval counter uses a single counting channel consisting of three parts: a larvae dispenser, an electronic counting unit and computer control software. After the separation of the larvae from eggs and debris, batches of different numbers of Aedes aegypti first instar larvae were manually counted and introduced into the counter through the upper loading funnel and channeled out from the bottom of the counter by gravitational flow. The accuracy and repeatability of the mosquito larval counter were determined in relation to larval density and water quality. We also investigated its impact on larval survival. Results showed an impact of larval density and water quality on the accuracy of the device. A -6% error and a repeatability of +/- 2.56% average value were achieved with larval densities up to 10 larvae/mL of clean water. Moreover, the use of the mosquito larval counter did not have any effect on larval survival or development. Under recommended conditions, the mosquito larval counter can be used to enumerate the number of mosquito larvae at a given density. However, future developments involving the use of multiple channels or larger input larvae container would help to expand its use in large-scale facilities.
Keyphrases
  • aedes aegypti
  • zika virus
  • dengue virus
  • water quality
  • machine learning
  • deep learning
  • high throughput
  • single cell
  • drosophila melanogaster