USING MACHINE LEARNING-BASED SEED HARVEST MOISTURE PREDICTIONS TO IMPROVE A COMPUTER-ASSISTED AGRICULTURAL FARM OPERATION
Embodiments generate digital plans for agricultural fields. In an embodiment, a model receives digital inputs including stress risk data, product maturity data, field location data, planting date data, and/or harvest date data. The model mathematically correlates sets of digital inputs with threshol...
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| creator | SSEGANE, Herbert YANG, Xiao XIE, Yao SOOD, Shilpa BULL, Jason Kendrick EHLMANN, Tonya S REICH, Timothy MACISAAC, Susan Andrea SCHNICKER, Bruce J TRAPP, Allan SORGE, Matthew JACOBS, Morrison SHAH, Nikisha |
| description | Embodiments generate digital plans for agricultural fields. In an embodiment, a model receives digital inputs including stress risk data, product maturity data, field location data, planting date data, and/or harvest date data. The model mathematically correlates sets of digital inputs with threshold data associated with the stress risk data. The model is used to generate stress risk prediction data for a set of product maturity and field location combinations. In a digital plan, product maturity data or planting date data or harvest date data or field location data can be adjusted based on the stress risk prediction data. A digital plan can be transmitted to a field manager computing device. An agricultural apparatus can be moved in response to a digital plan. |
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In an embodiment, a model receives digital inputs including stress risk data, product maturity data, field location data, planting date data, and/or harvest date data. The model mathematically correlates sets of digital inputs with threshold data associated with the stress risk data. The model is used to generate stress risk prediction data for a set of product maturity and field location combinations. In a digital plan, product maturity data or planting date data or harvest date data or field location data can be adjusted based on the stress risk prediction data. A digital plan can be transmitted to a field manager computing device. An agricultural apparatus can be moved in response to a digital plan.</description><language>eng ; fre ; ger</language><subject>AGRICULTURE ; ANIMAL HUSBANDRY ; CALCULATING ; COMPUTING ; COUNTING ; DATA PROCESSING SYSTEMS OR METHODS, SPECIALLY ADAPTED FORADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORYOR FORECASTING PURPOSES ; FERTILISING ; FISHING ; FORESTRY ; HARVESTING ; HUMAN NECESSITIES ; HUNTING ; MOWING ; PARTS, DETAILS, OR ACCESSORIES OF AGRICULTURAL MACHINES ORIMPLEMENTS, IN GENERAL ; PHYSICS ; PLANTING ; SOIL WORKING IN AGRICULTURE OR FORESTRY ; SOWING ; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE,COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORY OR FORECASTINGPURPOSES, NOT OTHERWISE PROVIDED FOR ; TRAPPING</subject><creationdate>2022</creationdate><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://worldwide.espacenet.com/publicationDetails/biblio?FT=D&date=20220914&DB=EPODOC&CC=EP&NR=3869934A4$$EHTML$$P50$$Gepo$$Hfree_for_read</linktohtml><link.rule.ids>230,308,777,882,25545,76296</link.rule.ids><linktorsrc>$$Uhttps://worldwide.espacenet.com/publicationDetails/biblio?FT=D&date=20220914&DB=EPODOC&CC=EP&NR=3869934A4$$EView_record_in_European_Patent_Office$$FView_record_in_$$GEuropean_Patent_Office$$Hfree_for_read</linktorsrc></links><search><creatorcontrib>SSEGANE, Herbert</creatorcontrib><creatorcontrib>YANG, Xiao</creatorcontrib><creatorcontrib>XIE, Yao</creatorcontrib><creatorcontrib>SOOD, Shilpa</creatorcontrib><creatorcontrib>BULL, Jason Kendrick</creatorcontrib><creatorcontrib>EHLMANN, Tonya S</creatorcontrib><creatorcontrib>REICH, Timothy</creatorcontrib><creatorcontrib>MACISAAC, Susan Andrea</creatorcontrib><creatorcontrib>SCHNICKER, Bruce J</creatorcontrib><creatorcontrib>TRAPP, Allan</creatorcontrib><creatorcontrib>SORGE, Matthew</creatorcontrib><creatorcontrib>JACOBS, Morrison</creatorcontrib><creatorcontrib>SHAH, Nikisha</creatorcontrib><title>USING MACHINE LEARNING-BASED SEED HARVEST MOISTURE PREDICTIONS TO IMPROVE A COMPUTER-ASSISTED AGRICULTURAL FARM OPERATION</title><description>Embodiments generate digital plans for agricultural fields. In an embodiment, a model receives digital inputs including stress risk data, product maturity data, field location data, planting date data, and/or harvest date data. The model mathematically correlates sets of digital inputs with threshold data associated with the stress risk data. The model is used to generate stress risk prediction data for a set of product maturity and field location combinations. In a digital plan, product maturity data or planting date data or harvest date data or field location data can be adjusted based on the stress risk prediction data. A digital plan can be transmitted to a field manager computing device. An agricultural apparatus can be moved in response to a digital plan.</description><subject>AGRICULTURE</subject><subject>ANIMAL HUSBANDRY</subject><subject>CALCULATING</subject><subject>COMPUTING</subject><subject>COUNTING</subject><subject>DATA PROCESSING SYSTEMS OR METHODS, SPECIALLY ADAPTED FORADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORYOR FORECASTING PURPOSES</subject><subject>FERTILISING</subject><subject>FISHING</subject><subject>FORESTRY</subject><subject>HARVESTING</subject><subject>HUMAN NECESSITIES</subject><subject>HUNTING</subject><subject>MOWING</subject><subject>PARTS, DETAILS, OR ACCESSORIES OF AGRICULTURAL MACHINES ORIMPLEMENTS, IN GENERAL</subject><subject>PHYSICS</subject><subject>PLANTING</subject><subject>SOIL WORKING IN AGRICULTURE OR FORESTRY</subject><subject>SOWING</subject><subject>SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE,COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORY OR FORECASTINGPURPOSES, NOT OTHERWISE PROVIDED FOR</subject><subject>TRAPPING</subject><fulltext>true</fulltext><rsrctype>patent</rsrctype><creationdate>2022</creationdate><recordtype>patent</recordtype><sourceid>EVB</sourceid><recordid>eNqNiz0KwkAQRtNYiHqHuUCqBNFy3EyShewPM7tpQ5C1Eg3Extu7ggew-R58vLct3lG07cCg6rUlGAjZ5qO8oFADQnl65JEkgHFaQmQCz9RoFbSzAsGBNp7dSICgnPExEJcokt3cYsdaxSFnOECLbMB5Yvy2-2Jzm-9rOvy4K6CloPoyLc8prct8TY_0mshXp-P5XNVYV38oH5lrOns</recordid><startdate>20220914</startdate><enddate>20220914</enddate><creator>SSEGANE, Herbert</creator><creator>YANG, Xiao</creator><creator>XIE, Yao</creator><creator>SOOD, Shilpa</creator><creator>BULL, Jason Kendrick</creator><creator>EHLMANN, Tonya S</creator><creator>REICH, Timothy</creator><creator>MACISAAC, Susan Andrea</creator><creator>SCHNICKER, Bruce J</creator><creator>TRAPP, Allan</creator><creator>SORGE, Matthew</creator><creator>JACOBS, Morrison</creator><creator>SHAH, Nikisha</creator><scope>EVB</scope></search><sort><creationdate>20220914</creationdate><title>USING MACHINE LEARNING-BASED SEED HARVEST MOISTURE PREDICTIONS TO IMPROVE A COMPUTER-ASSISTED AGRICULTURAL FARM OPERATION</title><author>SSEGANE, Herbert ; YANG, Xiao ; XIE, Yao ; SOOD, Shilpa ; BULL, Jason Kendrick ; EHLMANN, Tonya S ; REICH, Timothy ; MACISAAC, Susan Andrea ; SCHNICKER, Bruce J ; TRAPP, Allan ; SORGE, Matthew ; JACOBS, Morrison ; SHAH, Nikisha</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-epo_espacenet_EP3869934A43</frbrgroupid><rsrctype>patents</rsrctype><prefilter>patents</prefilter><language>eng ; fre ; ger</language><creationdate>2022</creationdate><topic>AGRICULTURE</topic><topic>ANIMAL HUSBANDRY</topic><topic>CALCULATING</topic><topic>COMPUTING</topic><topic>COUNTING</topic><topic>DATA PROCESSING SYSTEMS OR METHODS, SPECIALLY ADAPTED FORADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORYOR FORECASTING PURPOSES</topic><topic>FERTILISING</topic><topic>FISHING</topic><topic>FORESTRY</topic><topic>HARVESTING</topic><topic>HUMAN NECESSITIES</topic><topic>HUNTING</topic><topic>MOWING</topic><topic>PARTS, DETAILS, OR ACCESSORIES OF AGRICULTURAL MACHINES ORIMPLEMENTS, IN GENERAL</topic><topic>PHYSICS</topic><topic>PLANTING</topic><topic>SOIL WORKING IN AGRICULTURE OR FORESTRY</topic><topic>SOWING</topic><topic>SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE,COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORY OR FORECASTINGPURPOSES, NOT OTHERWISE PROVIDED FOR</topic><topic>TRAPPING</topic><toplevel>online_resources</toplevel><creatorcontrib>SSEGANE, Herbert</creatorcontrib><creatorcontrib>YANG, Xiao</creatorcontrib><creatorcontrib>XIE, Yao</creatorcontrib><creatorcontrib>SOOD, Shilpa</creatorcontrib><creatorcontrib>BULL, Jason Kendrick</creatorcontrib><creatorcontrib>EHLMANN, Tonya S</creatorcontrib><creatorcontrib>REICH, Timothy</creatorcontrib><creatorcontrib>MACISAAC, Susan Andrea</creatorcontrib><creatorcontrib>SCHNICKER, Bruce J</creatorcontrib><creatorcontrib>TRAPP, Allan</creatorcontrib><creatorcontrib>SORGE, Matthew</creatorcontrib><creatorcontrib>JACOBS, Morrison</creatorcontrib><creatorcontrib>SHAH, Nikisha</creatorcontrib><collection>esp@cenet</collection></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext_linktorsrc</fulltext></delivery><addata><au>SSEGANE, Herbert</au><au>YANG, Xiao</au><au>XIE, Yao</au><au>SOOD, Shilpa</au><au>BULL, Jason Kendrick</au><au>EHLMANN, Tonya S</au><au>REICH, Timothy</au><au>MACISAAC, Susan Andrea</au><au>SCHNICKER, Bruce J</au><au>TRAPP, Allan</au><au>SORGE, Matthew</au><au>JACOBS, Morrison</au><au>SHAH, Nikisha</au><format>patent</format><genre>patent</genre><ristype>GEN</ristype><title>USING MACHINE LEARNING-BASED SEED HARVEST MOISTURE PREDICTIONS TO IMPROVE A COMPUTER-ASSISTED AGRICULTURAL FARM OPERATION</title><date>2022-09-14</date><risdate>2022</risdate><abstract>Embodiments generate digital plans for agricultural fields. In an embodiment, a model receives digital inputs including stress risk data, product maturity data, field location data, planting date data, and/or harvest date data. The model mathematically correlates sets of digital inputs with threshold data associated with the stress risk data. The model is used to generate stress risk prediction data for a set of product maturity and field location combinations. In a digital plan, product maturity data or planting date data or harvest date data or field location data can be adjusted based on the stress risk prediction data. A digital plan can be transmitted to a field manager computing device. An agricultural apparatus can be moved in response to a digital plan.</abstract><oa>free_for_read</oa></addata></record> |
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| subjects | AGRICULTURE ANIMAL HUSBANDRY CALCULATING COMPUTING COUNTING DATA PROCESSING SYSTEMS OR METHODS, SPECIALLY ADAPTED FORADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORYOR FORECASTING PURPOSES FERTILISING FISHING FORESTRY HARVESTING HUMAN NECESSITIES HUNTING MOWING PARTS, DETAILS, OR ACCESSORIES OF AGRICULTURAL MACHINES ORIMPLEMENTS, IN GENERAL PHYSICS PLANTING SOIL WORKING IN AGRICULTURE OR FORESTRY SOWING SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE,COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORY OR FORECASTINGPURPOSES, NOT OTHERWISE PROVIDED FOR TRAPPING |
| title | USING MACHINE LEARNING-BASED SEED HARVEST MOISTURE PREDICTIONS TO IMPROVE A COMPUTER-ASSISTED AGRICULTURAL FARM OPERATION |
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