The Climate Corporation

Erik Andrejko

Sivan Aldor-Noiman

Ying Xu

James Ethington

Tristan D'Orgeval

Evin Levey

Moorea Lee Brega

Eli J Pollak

Samuel Alexander Wimbush

Katherine Krumme

Doug Sauder

Wei Guan

Alex Wimbush

Cory Muhlbauer

Coco Krumme

Methods are provided for improving performance of a computing system used to model potential crop yield. In one example embodiment, a computer-implemented method includes generating a model of potential crop yield, as a function of planting date and relative maturity based, at least in part, on one or more relative maturity maps, one or more planting date maps, and one or more actual production history maps, and storing the model in a memory of the server computer system. The method also includes receiving, via an interface at a field manager computing device, a selection of a particular field and computing, from the model of potential crop yield, a potential yield for the particular field based, at least in part, on a planting date for the particular field, a relative maturity value, and values representing actual production history for the particular field.

Generating digital models of crop yield based on crop planting dates and relative maturity values

A computer-implemented method for recommending agricultural activities is implemented by an agricultural intelligence computer system in communication with a memory. The method includes receiving, over a network, field definition data and environmental data; determining soil data from at least the environmental data; determining, based on at least the soil data, carbon load data indicating carbon load present in the field during a planting date; receiving first input indicating a plurality of starter application criteria; based on the first input and the carbon load present in the field, generating application data that include at least an amount of the starter fertilizer that provides nutrients to seeds throughout a nutrient immobilization period between the planting date and a beginning of a microorganism mineralization period; generating control instructions for controlling an agricultural implement based on the application data.

Methods and systems for recommending agricultural activities

A computer-implemented method for recommending agricultural activities is implemented by an agricultural intelligence computer system in communication with a memory. The method includes receiving a plurality of field definition data, retrieving a plurality of input data from a plurality of data networks, determining a field region based on the field definition data, identifying a subset of the plurality of input data associated with the field region, determining a plurality of field condition data based on the subset of the plurality of input data, identifying a plurality of field activity options, determining a recommendation score for each of the plurality of field activity options based at least in part on the plurality of field condition data, and providing a recommended field activity option from the plurality of field activity options based on the plurality of recommendation scores.

Methods and systems for managing agricultural activities

A method for generating digital models of potential crop yield based on planting date, relative maturity, and actual production history is provided. In an embodiment, data representing historical planting dates, relative maturity values, and crop yield is received by an agricultural intelligence computer system. Based on the historical data, the system generates spatial and temporal maps of planting dates, relative maturity, and actual production history. Using the maps, the system creates a model of potential yield that is dependent on planting date and relative maturity. The system may then receive actual production history data for a particular field. Using the received actual production history data, a particular planting date, and a particular relative maturity value, the agricultural intelligence computer system computes a potential yield for a particular field.

A method and system for modeling trends in crop yields is provided. In an embodiment, the method comprises receiving, over a computer network, electronic digital data comprising yield data representing crop yields harvested from a plurality of agricultural fields and at a plurality of time points; in response to receiving input specifying a request to generate one or more particular yield data; determining one or more factors that impact yields of crops that were harvested from the plurality of agricultural fields; decomposing the yield data into decomposed yield data that identifies one or more data dependencies according to the one or more factors; generating, based on the decomposed yield data, the one or more particular yield data; generating forecasted yield data or reconstructing the yield data by incorporating the one or more particular yield data into the yield data.

Modeling trends in crop yields

Systems and method for computing yield values through a neural network from a plurality of different data inputs are disclosed. In an embodiment, a server computer system receives a particular dataset relating to one or more agricultural fields wherein the particular data set comprises particular crop identification data, particular environmental data, and particular management practice data. Using a first neural network, the server computer system computes a crop identification effect on crop yield from the particular crop identification data. Using a second neural network, the server computer system computes an environmental effect on crop yield from the particular environmental data. Using a third neural network, the server computer system computes a management practice effect on crop yield from the management practice data. Using a master neural network, the server computer system computes one or more predicted yield values from the crop identification effect on crop yield, the environmental effect on crop yield, and the management practice effect on crop yield.

Crop yield estimation using agronomic neural network

A method and system for modeling trends in crop yields is provided. In an embodiment, the method comprises receiving, over a computer network, electronic digital data comprising yield data representing crop yields harvested from a plurality of agricultural fields and at a plurality of time points; in response to receiving input specifying a request to generate one or more particular yield data: determining one or more factors that impact yields of crops that were harvested from the plurality of agricultural fields; decomposing the yield data into decomposed yield data that identifies one or more data dependencies according to the one or more factors; generating, based on the decomposed yield data, the one or more particular yield data; generating forecasted yield data or reconstructing the yield data by incorporating the one or more particular yield data into the yield data.

Growing community of inventors

Oakland, CA, United States of America

Erik Andrejko