Abstract: Asset tracking systems and methods include one or more tracking devices that pair with one or more hub devices to transmit sensor data from a tracking device only to a paired hub device with a hub device state quality rating, and from the paired hub device to a network server, thereby reducing redundant communications and communication network usage.

Abstract: Embodiments of the present disclosure automate the identification of gonotrophic stages in mosquitoes. An example computer-implemented method can include: receiving an image data set; processing 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; determining, 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 outputting an indication of the expected population of mosquitoes.

Abstract: Images of an insect are subjected to at least a first convolutional neural network to develop feature maps based on anatomical pixels at corresponding image locations in the respective feature maps. The anatomical pixels correspond to a body part of the insect. A computer calculates an outer product of the first feature map and the second feature map to form an integrated feature map. Extracting fully connected layers from respective sets of integrated feature maps and applying the fully connected layers to a classification network for identifying the genus and the species of the insect.

Abstract: Images of an insect larva are subjected to at least a first convolutional neural network to develop feature maps based on anatomical pixels at corresponding image locations in the respective feature maps. The anatomical pixels correspond to a body part of the insect larva. A computer uses a plurality of feature maps form an integrated feature map and bounding boxes on anatomical pixels corresponding to a head, thorax, abdomen, or terminal abdomen area of the insect larva. A classification network identifies the genus, the species, the sex, or the life stage of the insect larva.

Abstract: Images of an insect are subjected to at least a first convolutional neural network to develop feature maps based on anatomical pixels at corresponding image locations in the respective feature maps. The anatomical pixels correspond to a body part of the insect. A computer calculates an outer product of the first feature map and the second feature map to form an integrated feature map. Extracting fully connected layers from respective sets of integrated feature maps and applying the fully connected layers to a classification network for identifying the genus and the species of the insect.