Abstract: Techniques for analyzing images of pests using a mobile device application are described. A mobile computing device may receive, via a graphical user interface (GUI), location information input corresponding to a location of a glueboard, an identification input of a pest type caught on the glueboard, and an image of the glueboard. The device may analyze white and dark areas of the image to determine at least one of: 1) total dark surface area of the glueboard and 2) number and size of multiple contiguous dark surface areas of the glueboard. The device may calculate a quantity of pests based on dark surface area and an average size of the pest. The device may output the quantity of pests to the GUI.

Abstract: A system includes a server connected to a network. The server includes a data collection module, a database interaction module, and a predictive analysis module. The data collection module is configured to collect data, via the network, from one or more data sources, the data being related to food safety risk and sanitation compliance tracking of a food establishment. The database interaction module is configured to store, into a database, data collected by the data collection module and to retrieve data from the database. The predictive analysis module is configured to analyze data in the database and calculate, based on the analyzed data, a probability of the food establishment violating a health code.

Abstract: Techniques for analyzing images of pests using a mobile device application are described. A mobile computing device may receive, via a graphical user interface (GUI), location information input corresponding to a location of a glueboard, an identification input of a pest type caught on the glueboard, and an image of the glueboard. The device may analyze white and dark areas of the image to determine at least one of: 1) total dark surface area of the glueboard and 2) number and size of multiple contiguous dark surface areas of the glueboard. The device may calculate a quantity of pests based on dark surface area and an average size of the pest. The device may output the quantity of pests to the GUI.

Abstract: Techniques for analyzing images of pests using a mobile device application are described. A mobile computing device may receive, via a graphical user interface (GUI), location information input corresponding to a location of a glueboard, an identification input of a pest type caught on the glueboard, and an image of the glueboard. The device may analyze white and dark areas of the image to determine at least one of: 1) total dark surface area of the glueboard and 2) number and size of multiple contiguous dark surface areas of the glueboard. The device may calculate a quantity of pests based on dark surface area and an average size of the pest. The device may output the quantity of pests to the GUI.

Abstract: Techniques for analyzing images of pests using a mobile device application are described. A mobile computing device may receive, via a graphical user interface (GUI), location information input corresponding to a location of a glueboard, an identification input of a pest type caught on the glueboard, and an image of the glueboard. The device may analyze white and dark areas of the image to determine at least one of: 1) total dark surface area of the glueboard and 2) number and size of multiple contiguous dark surface areas of the glueboard. The device may calculate a quantity of pests based on dark surface area and an average size of the pest. The device may output the quantity of pests to the GUI.