发明申请
- 专利标题: MACHINE LEARNING IN AGRICULTURAL PLANTING, GROWING, AND HARVESTING CONTEXTS
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申请号: US16057387申请日: 2018-08-07
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公开(公告)号: US20190050948A1公开(公告)日: 2019-02-14
- 发明人: David Patrick Perry , Geoffrey Albert von Maltzahn , Robert Berendes , Eric Michael Jeck , Barry Loyd Knight , Rachel Ariel Raymond , Ponsi Trivisvavet , Justin Y H Wong , Neal Hitesh Rajdev , Marc-Cedric Joseph Meunier , Charles Vincent Michell, JR. , Casey James Leist , Pranav Ram Tadi , Andrea Lee Flaherty , Charles David Brummitt , Naveen Neil Sinha , Jordan Lambert , Jonathan Hennek , Carlos Becco , Mark Allen , Daniel Bachner , Fernando Derossi , Ewan Lamont , Rob Lowenthal , Dan Creagh , Steve Abramson , Ben Allen , Jyoti Shankar , Chris Moscardini , Jeremy Crane , David Weisman , Gerard Keating , Lauren Moores , William Pate
- 申请人: Indigo Ag, Inc.
- 主分类号: G06Q50/02
- IPC分类号: G06Q50/02 ; G06K9/00 ; G06K9/42 ; G06K9/62 ; G06Q10/04
摘要:
A crop prediction system performs various machine learning operations to predict crop production and to identify a set of farming operations that, if performed, optimize crop production. The crop prediction system uses crop prediction models trained using various machine learning operations based on geographic and agronomic information. Responsive to receiving a request from a grower, the crop prediction system can access information representation of a portion of land corresponding to the request, such as the location of the land and corresponding weather conditions and soil composition. The crop prediction system applies one or more crop prediction models to the access information to predict a crop production and identify an optimized set of farming operations for the grower to perform.
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