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The Automatic Classification of Pyriproxyfen-Affected Mosquito Ovaries
![Fowler, M.T., Lees, R.S., Fagbohoun, J., Matowo, N.S., Ngufor, C., Protopopoff, N. and Spiers, A., 2021. The Automatic Classification of Pyriproxyfen-Affected Mosquito Ovaries. Insects, 12(12), p.1134. An image of 6 circles increasing in size, length and proportion of dark to white colouration from left to right, categorised into five numerical categories with the first five circles labelled infertile. The image represents Christopher stages of egg development. Mosquitos whose eggs have fully developed to stage V (normal elongated, boat/sausage-shaped eggs with lateral floats) are classified as ‘fecund’ or ‘fertile’. If eggs have not fully developed and remain in stages I–IV (less elongated, round shape, lacking floats), the mosquito is classified as ‘non-fecund’ or ‘infertile’.](https://innovationtoimpact.org/wp-content/uploads/2022/01/Machine-learning-paper-image_Fig-1_V2.jpg)
A new method for measuring the effect of pyriproxyfen on mosquito fertility using a machine learning approach has been published.
Using a Convolutional Neural Network (CNN) model, researchers were able to automatically classify the fertility status of ovaries from PPF-exposed female mosquitoes. This recent work by members of I2I was published as part of the special issue on mosquito control in the journal Insects.
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Comparative analysis of the Potter Tower and a new Track Sprayer for the application of residual sprays in the laboratory
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“A bioassay method validation framework for laboratory and semi-field tests used to evaluate vector control tools”. Matope et al. Malaria Journal
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Insecticides for Mosquito Control: Improving and Validating Methods to Strengthen the Evidence Base
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Unmanned aerial vehicles for surveillance and control of vectors of malaria and other vector-borne diseases
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