The Climate Corporation

Yao Xie

Jason Kendrick Bull

Xiao Yang

Tonya S Ehlmann

Timothy Reich

Bruce J Schnicker

Morrison Jacobs

Allan Trapp

Dongming Jiang

Matthew Sorge

Jyoti Dharna

Samuel Alexander Wimbush

Herbert Ssegane

Shilpa Sood

Susan Andrea Macisaac

Nikisha Shah

David Rock

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

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

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

San Francisco, CA, United States of America

Yao Xie