Methods and techniques to classify stages in the gonotrophic cycle of mosquitoes from images using computer vision techniques

A system comprising:

• at least one processor (e.g., cloud-based processing system);
• and
• a memory having instructions thereon, wherein the instructions when executed by the at least one processor, cause the at least one processor to:
• receive an image data set, wherein the image data set comprises a plurality of images that each depict a respective mosquito of a plurality of mosquitoes at a location;
• process at least a portion of the image data set using an image segmentation operation to determine a current gonotrophic phase for each of at least a portion of the plurality of mosquitoes;
• determine, using a trained machine learning model and based at least in part on the determined current gonotrophic phases, a predictive output indicative of an expected population of mosquitoes at the location during a future time period;
• and
• output an indication of the expected population of mosquitoes.

A method comprising:

• feeding a subset of a plurality of mosquitoes at a location to reach a gravid state;
• generating a first training image data set from a plurality of images, wherein each image depicts at least one of the plurality of mosquitoes;
• augmenting the first training image set using at least one image augmentation operation to generate a second training image data set;
• training a machine learning model using the second training image data set;
• and
• validating performance of the trained machine learning model using the first training image data set.