Method and apparatus for employing deep learning neural network to predict management zones
A computer-implemented method for predicting a cropland data layer (CDL) for a current year includes: retrieving a first set of records from a historical CDL database, where the first set corresponds to sampled areas of a region taken over a period for a number of years; retrieving a second set of r...
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| creator | Brau, Ernesto Bussmann, R. Shane Sargent, Ethan |
| description | A computer-implemented method for predicting a cropland data layer (CDL) for a current year includes: retrieving a first set of records from a historical CDL database, where the first set corresponds to sampled areas of a region taken over a period for a number of years; retrieving a second set of records from a historical imagery database, where the second set corresponds to the sampled areas of the region, the period, and the number of years; employing the second set as inputs to train a deep learning network to generate the first set; retrieving a third set of records from a current imagery database, where the third set corresponds to a prescribed region, and where the third set corresponds to the time period and the current year; and using the third set as inputs and executing the trained deep learning network to generate a predicted CDL for the current year. |
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Shane</au><au>Sargent, Ethan</au><format>patent</format><genre>patent</genre><ristype>GEN</ristype><title>Method and apparatus for employing deep learning neural network to predict management zones</title><date>2024-01-23</date><risdate>2024</risdate><abstract>A computer-implemented method for predicting a cropland data layer (CDL) for a current year includes: retrieving a first set of records from a historical CDL database, where the first set corresponds to sampled areas of a region taken over a period for a number of years; retrieving a second set of records from a historical imagery database, where the second set corresponds to the sampled areas of the region, the period, and the number of years; employing the second set as inputs to train a deep learning network to generate the first set; retrieving a third set of records from a current imagery database, where the third set corresponds to a prescribed region, and where the third set corresponds to the time period and the current year; and using the third set as inputs and executing the trained deep learning network to generate a predicted CDL for the current year.</abstract><oa>free_for_read</oa></addata></record> |
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| subjects | AGRICULTURE ANIMAL HUSBANDRY CALCULATING COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS COMPUTING COUNTING CULTIVATION OF VEGETABLES, FLOWERS, RICE, FRUIT, VINES, HOPSOR SEAWEED ELECTRIC DIGITAL DATA PROCESSING FISHING FORESTRY HORTICULTURE HUMAN NECESSITIES HUNTING PHYSICS TRAPPING WATERING |
| title | Method and apparatus for employing deep learning neural network to predict management zones |
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