Abstract: System and methods for predicting future pest pressures are provided. A pest pressure prediction computing device includes a memory and a processor communicatively coupled to the memory. The processor is programmed to receive trap data for a plurality of pest traps in a geographic location, receive weather data for the geographic location, receive image data for the geographic location, identify at least one geospatial feature within or proximate to the geographic location, apply a machine learning algorithm to the trap data, the weather data, the image data, and the at least one identified geospatial feature to identify a correlation between pest pressure and the at least one geospatial feature, and generate predicted future pest pressures for the geographic location based at least on the identified correlation between pest pressure and the at least one geospatial feature.

Abstract: Systems and methods for generating and displaying heat maps are provided. A heat map generation computing device includes a memory and a processor. The processor is programmed to receive trap data for a plurality of pest traps in a geographic location, the trap data including current and historical pest pressure values at each of the plurality of pest traps, receive weather data for the geographic location, receive image data for the geographic location, apply a machine learning algorithm to generate predicted future pest pressure values at each of the plurality of pest traps, generate a first heat map for a first point in time and a second heat map for a second point in time, and transmit the first and second heat maps to a mobile computing device to cause a user interface on the mobile computing device to display a time lapse heat map.

Abstract: Systems and methods for generating and displaying heat maps are provided. A heat map generation computing device includes a memory and a processor. The processor is programmed to receive trap data for a plurality of pest traps in a geographic location, the trap data including current and historical pest pressure values at each of the plurality of pest traps, receive weather data for the geographic location, receive image data for the geographic location, apply a machine learning algorithm to generate predicted future pest pressure values at each of the plurality of pest traps, generate a first heat map for a first point in time and a second heat map for a second point in time, and transmit the first and second heat maps to a mobile computing device to cause a user interface on the mobile computing device to display a time lapse heat map.

Abstract: Systems and methods for generating and displaying heat maps are provided. A heat map generation computing device includes a memory and a processor. The processor is programmed to receive trap data for a plurality of pest traps in a geographic location, the trap data including current and historical pest pressure values at each of the plurality of pest traps, receive weather data for the geographic location, receive image data for the geographic location, apply a machine learning algorithm to generate predicted future pest pressure values at each of the plurality of pest traps, generate a first heat map for a first point in time and a second heat map for a second point in time, and transmit the first and second heat maps to a mobile computing device to cause a user interface on the mobile computing device to display a time lapse heat map.

Abstract: Systems and methods for generating and displaying heat maps are provided. A heat map generation computing device includes a memory and a processor. The processor is programmed to receive trap data for a plurality of pest traps in a geographic location, the trap data including current and historical pest pressure values at each of the plurality of pest traps, receive weather data for the geographic location, receive image data for the geographic location, apply a machine learning algorithm to generate predicted future pest pressure values at each of the plurality of pest traps, generate a first heat map for a first point in time and a second heat map for a second point in time, and transmit the first and second heat maps to a mobile computing device to cause a user interface on the mobile computing device to display a time lapse heat map.

Abstract: Systems and methods for generating and displaying heat maps are provided. A heat map generation computing device includes a memory and a processor. The processor is programmed to receive trap data for a plurality of pest traps in a geographic location, the trap data including current and historical pest pressure values at each of the plurality of pest traps, receive weather data for the geographic location, receive image data for the geographic location, apply a machine learning algorithm to generate predicted future pest pressure values at each of the plurality of pest traps, generate a first heat map for a first point in time and a second heat map for a second point in time, and transmit the first and second heat maps to a mobile computing device to cause a user interface on the mobile computing device to display a time lapse heat map.

Abstract: Systems and methods for generating and displaying heat maps are provided. A heat map generation computing device includes a memory and a processor. The processor is programmed to receive trap data for a plurality of pest traps in a geographic location, the trap data including current and historical pest pressure values at each of the plurality of pest traps, receive weather data for the geographic location, receive image data for the geographic location, apply a machine learning algorithm to generate predicted future pest pressure values at each of the plurality of pest traps, generate a first heat map for a first point in time and a second heat map for a second point in time, and transmit the first and second heat maps to a mobile computing device to cause a user interface on the mobile computing device to display a time lapse heat map.

Abstract: System and methods for predicting future pest pressures are provided. A pest pressure prediction computing device includes a memory and a processor communicatively coupled to the memory. The processor is programmed to receive trap data for a plurality of pest traps in a geographic location, receive weather data for the geographic location, receive image data for the geographic location, identify at least one geospatial feature within or proximate to the geographic location, apply a machine learning algorithm to the trap data, the weather data, the image data, and the at least one identified geospatial feature to identify a correlation between pest pressure and the at least one geospatial feature, and generate predicted future pest pressures for the geographic location based at least on the identified correlation between pest pressure and the at least one geospatial feature.