Abstract: 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.

Abstract: Systems and methods define field regions within agricultural fields. An example computer-implemented method includes identifying a field, retrieving data for the identified field, and displaying a graphical display including the identified field and at least some of the retrieved data. The method also includes receiving, via the graphical display, an input to create a field region within the identified field based on passes of an agricultural apparatus in the identified field and retrieving pass data for the passes of the agricultural apparatus in the field. The method further includes displaying, on the graphical display, the pass data, defining the field region based on the pass data, so that a boundary of the field region corresponds to a start point and an end point of each of the passes of the agricultural apparatus, and displaying, on the graphical display, field performance data constrained to the field region.

Abstract: Systems and methods are provided for monitoring an agricultural implement. An example method includes, as an agricultural implement traverses a field, transmitting command signals to an implement actuator of the agricultural implement, whereby the implement actuator implements operating parameters associated with prescription data for the field. The method also includes receiving, at a monitor device, raw as-applied data from an implement sensor associated with the implement actuator, processing the raw as-applied data into processed as-applied data, and storing the processed as-applied data in a memory. The method further includes rendering a map representing the processed as-applied data, where the rendered map includes one or more application map images indicative of the processed as-applied data over an image of at least part of the field, and then displaying the rendered map.

Abstract: A method is provided for determining soil properties for an area of land, from soil spectrum data. In an embodiment, the method includes receiving soil spectrum data records from hyperspectral sensors that represent a mean soil spectrum of a specific geo-location of the area of land and removing interference signals from the spectrum data records to create soil spectral bands. The method also includes predicting a plurality of soil property datasets based on a partial least-square regression and the soil spectral bands and selecting specific soil property datasets from the plurality of soil property datasets to represent soil properties of the specific geo-location, wherein the specific soil property datasets include property data and spectral band data for spectral bands used to determine the property data. The specific soil property datasets may then be used to generate a crop prescription of recommended hybrid seeds or population densities for the specific geo-location.

Abstract: In one embodiment is a carbon sequestration system that includes a crop residue pickup configured to ingest crop residue disposed on a field; a pyrolyzer configured to receive the crop residue output from the crop residue pickup and process the crop residue into biochar; a biochar/water or slurry mixing system configured to receive the biochar output from the pyrolyzer and quench the biochar in water and/or slurry; a soil injection system configured to receive the quenched biochar from the biochar/water or slurry mixing system and inject the quenched biochar into the field; and one or more sensors configured to detect one or more operations states of the crop residue pickup, the pyrolyzer, the biochar/water or slurry mixing system, and/or the soil injection system.

Abstract: A system for measuring soil properties has at least one sensor coupled to a row unit of an agricultural implement and a camera coupled to the row unit of the agricultural implement. The at least one sensor is configured to measure at least one soil property of soil worked by the row unit and/or of soil in a trench opened by an opening assembly of the row unit. And, the camera is arranged, on the row unit, to capture images of the soil worked by the row unit and/or the trench opened by the opening assembly of the row unit.

Abstract: Systems and methods are provided for updating an agricultural prescription for a geographic region. An example computer-implemented method includes executing an agricultural prescription, via farming equipment, in a geographic region and receiving, during execution of the prescription, sensor data from the farming equipment and operating data representing local operating settings for the farming equipment. Task execution data for the agricultural prescription is generated based on the received sensor data and received operating data for the farming equipment, and compared to expected data for execution of the prescription by the farming equipment.

Abstract: Systems, methods and apparatus are provided for soil testing. In some embodiments, a soil sample quality criterion is determined and associated with the soil sample. In some embodiments a soil characteristic measurement is additionally taken and associated with the soil sample. In some embodiments, the soil sample and its associated data are associated with a container in which the soil sample is placed.

Abstract: Systems and methods for determining a risk of damage to a crop on a field are described. In an example embodiment, a method for limiting such damage to a crop includes receiving, for multiple hours, weather data identifying temperature values and humidity values for a geographic location of the field, determining, for the multiple hours, that a temperature value is within a first range of values and a humidity value is within a second range of values and, identifying each of the multiple hours as a risk hour for a disease. The method also includes computing a risk value for the field based on the identified risk hours, determining that the risk value is above a threshold, and determining that the crop on the field is at risk for the disease. The method then includes spraying the crop on the field with a damage mitigating chemical specific to the disease.

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: A method begins by agriculture equipment collecting current on-site gathered agriculture data regarding an agriculture region and sending at least a representation of the current on-site gathered agriculture data to a host device. The method continues with the host device processing one or more of the at least a representation of the current on-site gathered agriculture data, current off-site gathered agriculture data, historical on-site gathered agriculture data, historical off-site gathered agriculture data, and historical analysis of agriculture predictions regarding the agriculture region to produce a current agriculture prediction for the agriculture region. The method continues with the host device generating an agriculture prescription regarding at least a portion of the agriculture region based on the current agriculture prediction and sending the agriculture prescription to one or more of the agriculture equipment.

Abstract: 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.

Abstract: Methods and systems provided for generating an irrigation map. In one computer-implemented method, a user is instructed, by a computing device, to draw an irrigation boundary in a map of an agricultural field. In response, the computing device receives a first input, from the user, at the map of the agricultural field displayed to the user, where the input includes a point of the irrigation boundary. A second input is solicited from the user and, in turn, the computing device receives the second input, which defines a dimension of the irrigation boundary. The computing device then appends a shape of the irrigation boundary to the map, based on the point and the dimension, wherein the irrigation boundary is positioned based on the point indicated by the user.

Abstract: A method for controlling application of agrichemical products, comprises acquiring remotely sensed digital image data; developing a prescription to apply at least one agrichemical product in a variable manner based on at least the digital image data, wherein the prescription describes a plurality of passes of a particular autonomous vehicle over a field to apply the at least one agrichemical product; applying the at least one agrichemical product to a crop in the variable manner by the particular autonomous vehicle according to the prescription.

Abstract: Systems and methods for identifying clouds and cloud shadows in satellite imagery are described herein. In an embodiment, a system receives a plurality of images of agronomic fields produced using one or more frequency bands. The system also receives corresponding data identifying cloud and cloud shadow locations in the images. The system trains. a machine learning system to identify at least cloud locations using the images as inputs and at least data identifying pixels as cloud pixels or non-cloud pixels as outputs. When the system receives one or more particular images of a particular agronomic field produced using the one or more frequency bands, the system uses the one or more particular images as inputs into the machine learning system to identify a plurality of pixels in the one or more particular images as particular cloud locations.

Abstract: Techniques for providing improvements in agricultural science by optimizing irrigation treatment placements for testing are provided, including analyzing a plurality of digital images of a field to determine vegetation density changes in a sector of the field. The techniques proceed by comparing a distribution of pixel characteristics in the digital images for each field sector to determine sectors in which minimal differences are present. Instructions for irrigation placements and testing may then be displayed or modified based on the results of the sector determinations.

Abstract: Systems and methods for determining a degree or rate of error for treatments at a field are disclosed herein. In some embodiments, a system receives first data corresponding to a first agronomic treatment at a field and second data corresponding to a second agronomic treatment at the field. The system uses the first and second data to determine differences and overlaps between characteristics of the first and second data to quantify an error rate at individual subsets of the field. Based on the quantified error rate, a number of metrics and yield data may be interpreted differently or modified to fit the result of the quantified error rate. The system uses the quantified error rate to predict the effectiveness of treatments, modify the rate of experienced yields at a field, and recommend real-time and subsequent corrective actions to take at the field.

Abstract: A computer-implemented method for improving crop yield in fields includes accessing data representative of predicted performance of seeds for a field and creating an experiment for the field based on the predicted performance of the seeds. The experiment includes a specific prescription to plant the seeds in the field. The method also includes transmitting the prescription to a planter and/or a harvester associated with the field and collecting, in real time, planting data for the seeds as the seeds are planted in the field and harvesting data for a crop grown in the field from the planted seeds. The method further includes validating that execution of the experiment in the field is consistent with the prescription and generating a recommendation based on a comparison of the collected harvesting data for the crop grown in the field to the predicted performance of the seeds included in the prescription.

Abstract: A computer-implemented method of prescribing spatially-variable application rates of one or more nutrients for an agricultural field is disclosed.