Abstract: Systems and methods are provided for use in mapping tillage in fields based in remote data. One example computer-implemented method includes accessing, by a computing device, an image of one or more fields, where the image includes multiple pixels and where each of the pixels includes a value for each of multiple bands. The method also includes deriving, by the computing device, at least one index value the image and generating a map of tillage for the one or more fields using a trained model and the at least one index value for each of the pixels of the image. The method further includes storing the map of tillage for the one or more fields in a memory and causing display of the map of tillage for the one or more fields at an output device.

Abstract: Systems and methods are provided for use in mapping irrigation in fields based on remote data. One example computer-implemented method includes accessing, by a computing device, at least one image of one or more fields; applying, by the computing device, a trained model to identity at least one irrigation segment in the at least one image; compiling a map of the one or more fields including the at least one identified irrigation segment; and storing, by the computing device, the map of the at least one identified irrigation segment for the one or more fields in a memory; and/or causing display of the map of the at least one identified irrigation segment for the one or more fields at an output device.

Abstract: In an embodiment, a computer-implemented method for predicting subfield soil properties for an agricultural field comprises: receiving satellite remote sensing data that includes a plurality of images capturing imagery of an agricultural field in a plurality of optical domains; receiving a plurality of environmental characteristics for the agricultural field; generating a plurality of preprocessed images based on the plurality of satellite remote sensing data and the plurality of environmental characteristics; identifying, based on the plurality preprocessed images, a plurality of features of the agricultural field; generating a subfield soil property prediction for the agricultural field by executing one or more machine learning models on the plurality of features; transmitting the subfield soil property prediction to an agricultural computer system.

Abstract: In some embodiments, a computer-implemented method for predicting agronomic field property data for one or more agronomic fields using a trained machine learning model is disclosed. The method comprises receiving, at an agricultural intelligence computer system, agronomic training data; training a machine learning model, at the agricultural intelligence computer system, using the agronomic training data; in response to receiving a request from a client computing device for agronomic field property data for one or more agronomic fields, automatically predicting the agronomic field property data for the one or more agronomic fields using the machine learning model configured to predict agronomic field property data; based on the agronomic field property data, automatically generating a first graphical representation; and causing to display the first graphical representation on the client computing device.

Abstract: In an embodiment, a computer-implemented method for predicting subfield soil properties for an agricultural field comprises: receiving satellite remote sensing data that includes a plurality of images capturing imagery of an agricultural field in a plurality of optical domains; receiving a plurality of environmental characteristics for the agricultural field; generating a plurality of preprocessed images based on the plurality of satellite remote sensing data and the plurality of environmental characteristics; identifying, based on the plurality preprocessed images, a plurality of features of the agricultural field; generating a subfield soil property prediction for the agricultural field by executing one or more machine learning models on the plurality of features; transmitting the subfield soil property prediction to an agricultural computer system.