Digital modeling of probabilistic crop yields for implementing agricultural field trials
US12052943B1 · kind B1 · utility
Assignee
Inventors
Key dates
| Filing date | Feb 22, 2021 |
| Grant date | Aug 6, 2024 |
| Priority date | — |
| Expiry date | Oct 16, 2042 |
Classification
- Technology area (CPC G)Physics
- CPC primaryG06N20/00
- WIPO fieldComputer technology
- WIPO sectorElectrical engineering
Abstract
Systems and methods for improving the training of machine learning models to generate probability distributions of yield values are presented. In an embodiment, a system stores a machine learning system trained to compute parameters for a probability distribution of yield values based on seeding density, seed type, and information specific to a field. The system receives inputs for a particular field and computes parameters for a probability distribution of yield. The system generates a probability distribution of yield using the parameters and uses the probability distribution to generate a yield guarantee value. The system supplies the yield guarantee value to a field manager computing device with a seed type and/or seed density recommendation. When the system receives input accepting the recommendation, the system generates one or more scripts which, when executed by an application controller, causes the application controller to control an agricultural implement to cause the agricultural implement to plant a seed on the field according to the recommendation.
Source: USPTO / EPO open patent data. Objective bibliographic and citation counts.