The Climate Corporation

Gardar Johannesson

Wei Guan

Pramithus Khadka

Nicholas Charles Cizek

Moslem Ladoni

Ricardo T Lemos

Patrick Lee Dumstorff

Brian Lutz

Maria Antonia Terres

Carlos Carrion

James Delaney

Yaqi Chen

Wayne Tai Lee

Kyle Plattner

Hunter Merrill

Tavis Easton Bones

Dariusz Andrzej Blasiak

Seth Robert Smoot

Michael Joseph Lyons

Alex Raymond Kreig

Michael Peter Marlow

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.

Digital modeling of probabilistic crop yields for implementing agricultural field trials

Systems and methods for utilizing a spatial statistical model to maximize efficacy in performing trials on agronomic fields are disclosed herein. In an embodiment, a system receives first yield data for a first portion of an agronomic field, the first portion of the agronomic field having received a first treatment, and second yield data, for a second portion of the agronomic field, the second portion of the agronomic field having received a second treatment that is different than the first treatment. The system uses a spatial statistical model and the first yield data to compute a yield value for the second portion of the agronomic field, the yield value indicating an agronomic yield for the second portion of the agronomic field if the second portion of the agronomic field had received the first treatment instead of the second treatment. Based on the computed yield value and the second yield data, the system selects the second treatment. In an embodiment, in response to selecting the second treatment, the system generates a prescription map, the prescription map including the second treatment. The system may also generate one or more scripts which, when executed by an application controller, cause the application controller to control an operating parameter of an agricultural implement to apply the second treatment.

Utilizing spatial statistical models for implementing agronomic trials

Systems and methods for utilizing a spatial statistical model to maximize efficacy in performing trials on agronomic fields are disclosed herein. In an embodiment, a system receives first yield data for a first portion of an agronomic field having received a first treatment, and second yield data for a second portion of the agronomic field having received a second treatment different than the first treatment. The system uses a spatial statistical model and the first yield data to compute a yield value for the second portion of the agronomic field, where the yield value indicates an agronomic yield for the second portion of the agronomic field if the second portion of the agronomic field had received the first treatment instead of the second treatment. Based on the computed yield value and the second yield data, the system selects the second treatment and generates a prescription map including the second treatment.

In an embodiment, digital images of agricultural fields are received at an agricultural intelligence processing system. Each digital image includes a set of pixels having pixel values, and each pixel value of a pixel includes a plurality of spectral band intensity values. Each spectral band intensity value describes a spectral band intensity of one band among several bands of electromagnetic radiation. For each of the agricultural fields, spectral band intensity values of each band are preprocessed at a field level using the digital images for that agricultural field resulting in preprocessed intensity values. The preprocessed intensity values are provided as input to a machine learning model. The model generates a predicted yield value for each field. The predicted yield value is used to update field yield maps of agricultural fields for forecasting and can be displayed via a graphical user interface (GUI) of a client computing device.

In-season field level yield forecasting

Systems and methods for generation of images of a particular type from images of a different type are disclosed. In an embodiment, an agricultural intelligence computer system receives a first plurality of images of a first type and a second plurality of images of a second type. The first and second types may refer to variances in resolution, frequency ranges of frequency bands, and/or types of frequency bands used to generate the images. Based on the first plurality of images and the second plurality of images, the agricultural intelligence computer system generates a feature set dictionary comprising mappings from features of the first plurality of images to features of the second plurality of images. When the agricultural intelligence computer system receives a particular image of the first type, the agricultural intelligence computer system uses the received image and the feature set dictionary to generate an image of the second type.

Using digital images of a first type and a feature set dictionary to generate digital images of a second type

Growing community of inventors

Oakland, CA, United States of America

Gardar Johannesson