The patent badge is an abbreviated version of the USPTO patent document. The patent badge does contain a link to the full patent document.

The patent badge is an abbreviated version of the USPTO patent document. The patent badge covers the following: Patent number, Date patent was issued, Date patent was filed, Title of the patent, Applicant, Inventor, Assignee, Attorney firm, Primary examiner, Assistant examiner, CPCs, and Abstract. The patent badge does contain a link to the full patent document (in Adobe Acrobat format, aka pdf). To download or print any patent click here.

Date of Patent:
Dec. 10, 2024

Filed:

Oct. 27, 2020
Applicant:

Fmc Corporation, Philadelphia, PA (US);

Inventors:

Sukhvinder Singh, Johnston, IA (US);

Sara Catherine Sterling, Thorndale, PA (US);

Simon Bridge Barratt, Ardmore, PA (US);

Ruixue Gong, Chicago, IL (US);

Wandi Lin, Chicago, IL (US);

Sai Anirudh Mandagondi, Chicago, IL (US);

Cassandra Pallai, Beverly, MA (US);

Ross Joseph Putterman, Chicago, IL (US);

Assignee:

FMC Corporation, Philadelphia, PA (US);

Attorney:
Primary Examiner:
Int. Cl.
CPC ...
A01M 1/02 (2006.01); G01W 1/10 (2006.01); G06N 20/00 (2019.01); G06V 10/764 (2022.01); G06V 20/10 (2022.01); G06V 20/13 (2022.01); H04M 1/72403 (2021.01);
U.S. Cl.
CPC ...
A01M 1/026 (2013.01); G01W 1/10 (2013.01); G06N 20/00 (2019.01); G06V 10/764 (2022.01); G06V 20/13 (2022.01); G06V 20/182 (2022.01); H04M 1/72403 (2021.01);
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.


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