Systems and methods for classifying mosquito larvae based on extracted masks of anatomical components from images

A system for identifying a genus and species of an insect larva, the system comprising:

• an imaging device configured to generate images of the insect larva;
• a computer processor connected to memory storing computer implemented commands in software, the memory receiving the images, wherein the software implements the following computerized method with respective images:
• applying a first convolutional neural network to the respective images to develop at least a first feature map directed to anatomical pixels at corresponding image locations in the respective images, said anatomical pixels corresponding to a body part of the insect larva;
• applying a second convolutional neural network to the respective images to develop at least a second feature map directed to anatomical pixels at corresponding image locations in the respective images, said anatomical pixels corresponding to a body part of the insect larva;
• forming an integrated feature map from the first feature map and the second feature map;
• applying bounding boxes to portions of the images;
• identifying a region of interest from the bounding boxes;
• extracting fully connected layers from respective sets of integrated feature maps that have had the first convolutional neural network and the second convolutional neural network applied thereto;
• applying the fully connected layers to a classification network for identifying the genus and the species of the insect larva.

A computerized method of identifying at least one of a genus of an insect larva, a species of an insect larva, a sex of an insect larva, or a life stage of an insect larva, the method comprising:

• acquiring images of an insect larva and storing pixel data from the images in a computer memory in data communication with a computer processor;
• using the computer processor to perform the following steps of the method:
• applying at least one convolutional neural network to the respective images to develop a plurality of feature maps of hyperparameters based on anatomical pixels at corresponding image locations in respective feature maps, said anatomical pixels corresponding to at least one landmark on the insect larva that is identifiable from the images;
• forming an integrated feature map from the plurality of feature maps;
• extracting fully connected layers from respective sets of integrated feature maps that have had at least a first convolutional neural network applied thereto;
• and
• applying the fully connected layers to a classification network for classifying the insect larva from anatomical pixels that correspond to at least one of a head of the insect larva, a thorax of the insect larva, an eye of the insect larva, an antenna of the insect larva, an anal brush of the insect larva, anal gills of the insect larva, comb scales of an insect larva, a ventral brush of the insect larva, anal papillae of the insect larva, pectin of the insect larva, a saddle of the insect larva, and a siphon of the insect larva.