The Climate Corporation

Tonya S Ehlmann

Jason Kendrick Bull

Xiao Yang

Timothy Reich

Yao Xie

Allan Trapp

Morrison Jacobs

Dongming Jiang

Herbert Ssegane

Bruce J Schnicker

Matthew Sorge

Doug Sauder

Thomas Gene Ruff

Nicholas Charles Cizek

Moslem Ladoni

Jyoti Dharna

Daniel Williams

Samuel Alexander Wimbush

Brandon Rinkenberger

Brian Lutz

Aaron E Robinson

Christina Bogdan

Shilpa Sood

Xuefei Wang

Susan Andrea Macisaac

Guomei Wang

Liwei Wen

James C Moore, Iii

Nikisha Shah

David Rock

Hunter Merrill

Joo Yoon Han

Systems and methods are provided for managing hybrid seeds for planting. One example computer-implemented method includes receiving a first dataset of hybrid seeds for planting on a target field, where the first dataset includes probability of success values and historical agricultural data for the hybrid seeds, and selecting a subset of hybrid seeds of the first dataset based on the probability of success values. The method also includes generating representative yield values for the subset of hybrid seeds based on the historical agricultural data, generating risk values for the subset of hybrid seeds based on the historical agricultural data, and generating a second dataset of hybrid seeds for planting based on the risk values, the representative yield values, and properties for the target field. The method further includes causing displaying the representative yield values and the risk values related to the second dataset of hybrid seeds for planting.

Hybrid seed selection and seed portfolio optimization by field

A system for implementing a trial a field is provided. In an embodiment, the system is configured to generate a trial recommendation for a field and, based on field data for the field, compute a yield probabilities for the field. The system is also configured to generate a plurality of outcome-based values for the field based on the yield probabilities, compute crop values for each of the outcome-based values and a bushel per acre value, and cause display of an interface that dynamically displays each of the plurality of outcome-based values for the field based on a selected bushel per acre value. The system is further configured to receive user input changing a position of an interactive sliding widget in the interface, to change the bushel per acre value, and in response, compute a crop value for each of the outcome-based values based on the changed bushel per acre value.

Digital modeling and tracking of agricultural fields for implementing agricultural field trials

An example computer-implemented method includes receiving agricultural data records comprising a first set of yield properties for a first set of seeds grown in a first set of environments, and receiving genetic feature data related to a second set of seeds. The method further includes generating a second set of yield properties for the second set of seeds associated with a second set of environments by applying a model using the genetic feature data and the agricultural data records. In addition, the method includes determining predicted yield performance for a third set of seeds associated with one or more target environments by applying the second set of yield properties, and generating seed recommendations for the one or more target environments based on the predicted yield performance for the third set of seeds. In the present example, the method also includes causing display of the seed recommendations.

Leveraging genetics and feature engineering to boost placement predictability for seed product selection and recommendation by field

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.

Using machine learning-based seed harvest moisture predictions to improve a computer-assisted agricultural farm operation

Techniques for recommending side-by-side plantings of pairs of seeds include a server computer receiving agricultural data records that represent crop seed data describing seed and yield properties of seeds and first data for agricultural fields where the seeds were planted. The server receives second data for available seeds and automatically calculates a dataset of success probability scores that describe the probability of a successful yield on the target fields. Data is organized as pairs to facilitate comparison of actual plantings to optimized plantings that have a probability of success (POS), in terms of yield lift or increased yield season-over-season, for different yield values. Confidence values are generated and stored in association with the POS values and can be used as a basis of visual output to support planting and/or field management decisions as part of an automated intelligent agricultural decision support system.

Computer-implemented recommendation of side-by-side planting in agricultural fields

A server computer system receives a dataset of candidate hybrid seeds for planting on one or more target fields, which comprises data regarding one or more hybrid seeds, probability of success values associated with each of the one or more hybrid seeds that describe a probability of a successful yield, and historical agricultural data associated with each of the one or more hybrid seeds further receives one or more properties for one or more target fields, and selects a subset of the one or more hybrid seeds based on the probabilities of success values. The server computer system generates representative yield values for hybrid seeds in the subset based on the historical agricultural data, generates a dataset of risk values for the subset, which describes an amount of risk associated with the representative yield values for each hybrid seed in the subset based on the historical agricultural data associated with each hybrid seed in the subset, and selects a list of target hybrid seeds from the subset for planting on the one or more target fields based on the dataset of risk values, the representative yield values for the subset, and the one or more properties for the one or more target fields. In addition, the server computer system generates a prescription related to planting the list of target hybrid seeds in the one or more target fields for a next period determines a safety stock for the list for the next period with respect to a supply chain based on the prescription, and causes displaying values of the safety stock, thereby causing production of the safety stock.

Hybrid seed supply management based on prescription of hybrid seed placement

A system for implementing a trial in one or more fields is provided. In an embodiment, an agricultural intelligence computing system receives field data for a plurality of agricultural fields. Based, at least in part, on the field data for the plurality of agricultural fields, the agricultural intelligence computing system identifies one or more target agricultural fields. The agricultural intelligence computing system sends, to a field manager computing device associated with the one or more target agricultural fields, a trial participation request. The server receives data indicating acceptance of the trial participation request from the field manager computing device. The server determines one or more locations on the one or more target agricultural fields for implementing a trial and sends data identifying the one or more locations to the field manager computing device. When the agricultural intelligence computing system receives application data for the one or more target agricultural fields, the agricultural intelligence computing system determines whether the one or more target agricultural fields are in compliance with the trial. The agricultural intelligence computing system then receives result data for the trial and, based on the result data, computes a benefit value for the trial.

An example computer-implemented method includes receiving agricultural data records comprising a first set of yield properties for a first set of seeds grown in a first set of environments, and receiving genetic feature data related to a second set of seeds. The method further includes generating a second set of yield properties for the second set of seeds associated with a second set of environments by applying a model using the genetic feature data and the agricultural data records. In addition, the method includes determining predicted yield performance for a third set of seeds associated with one or more target environments by applying the second set of yield properties, and generating seed recommendations for the one or more target environments based on the predicted yield performance for the third set of seeds. In the present example, the method also includes causing display, on a display device communicatively coupled to the server computer system, the seed recommendations.

Techniques for recommending side-by-side plantings of pairs of hybrids or seeds include a server computer receiving agricultural data records that represent crop seed data describing seed and yield properties of hybrid seeds and first data for agricultural fields where the hybrid seeds were planted. The server receives second data for available hybrids and seeds and automatically calculates a dataset of success probability scores that describe the probability of a successful yield on the target fields. Data is organized as pairs to facilitate comparison of actual plantings to optimized plantings that have a probability of success (POS), in terms of yield lift or increased yield season-over-season, for different yield values. Confidence values are generated and stored in association with the POS values and can be used as a basis of visual output to support planting and/or field management decisions as part of an automated intelligent agricultural decision support system.

Techniques are provided for generating target success group of hybrid seeds for target fields include a server receiving agricultural data records that represent crop seed data describing seed and yield properties of hybrid seeds and first field geo-location data for agricultural fields where the hybrid seeds were planted. The server receives second geo-locations data for target fields where hybrid seeds are to be planted. The server generates a dataset of hybrid seed properties that include yield values and environmental classifications for hybrid seeds and then a dataset of success probability scores that describe the probability of a successful yield on the target fields based on the dataset of hybrid seed properties and the second geo-location data. The server generates target success yield group of hybrid seeds and probability of success values based on success probability scores and a yield threshold. The server causes display of the target success yield group.

Techniques are provided for generating a set of target hybrid seeds with optimal yield and risk performance, including a server receiving a candidate set of hybrid seeds along with probability of successful yield values, associated historical agricultural data and property information, and selecting a subset of the hybrid seeds that have probability of success values greater than a filtering threshold. The server generates representative yield values for hybrid seeds based on the historical agricultural data and risk values for each hybrid seed. The server generates a dataset of target hybrid seeds for planting based on the risk values, the yield values, and the properties for the target fields. The dataset of target hybrid seeds includes target hybrid seeds that meet a specific threshold for a range of risk values. The server causes display of the dataset of target hybrid seeds including yield values and risk values for the target fields.

Techniques are provided for receiving a first set of historical agricultural data and a second set of historical agricultural data; generating a plurality of projected target yield ranges using the first set and the second set of historical agricultural data by generating a historic yield distribution; generating one or more yield ranking scores for one or more fields of a grower using the first set of historical agricultural data, and assigning a projected target yield range of the plurality of projected target yield ranges to each of the one or more fields based on the one or more yield ranking scores to generate assigned projected target yield ranges; receiving a third set of historical agricultural data comprising seed optimization data, and generating a recommended change in seed population or a recommended change in seed density; causing displaying the yield improvement recommendation for each of the one or more fields.

Growing community of inventors

St. Peters, MO, United States of America

Tonya S Ehlmann