Systems and methods for predicting pest pressure using geospatial features and machine learning

A method for generating pest pressure predication data, the method implemented using a pest pressure prediction computing device including a memory communicatively coupled to a processor, the method comprising:

• receiving trap data for a plurality of pest traps in a geographic location, the trap data including at least current and historical pest pressure at each of the plurality of pest traps;
• receiving weather data for the geographic location, the weather data including at least current and historical weather conditions for the geographic location;
• receiving image data for the geographic location;
• identifying at least one geospatial feature within or proximate to the geographic location;
• applying 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
• generating 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.

A computer-readable storage medium having computer-executable instructions embodied thereon, wherein when executed by a pest pressure prediction computing device including at least one processor in communication with a memory, the computer-readable instructions cause the pest pressure prediction computing device to:

• receive trap data for a plurality of pest traps in a geographic location, the trap data including at least current and historical pest pressure at each of the plurality of pest traps;
• receive weather data for the geographic location, the weather data including at least current and historical weather conditions 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.