Predicting soil organic carbon content
AssigneeMineral Earth Sciences LLC
InventorsCheng-En Guo, Jie Yang, Zhiqiang Yuan, Elliott Grant
Life of this patent
Estimating the term…
Abstract
Implementations are described herein for predicting soil organic carbon (“SOC”) content for agricultural fields detected in digital imagery. In various implementations, one or more digital images depicting portion(s) of one or more agricultural fields may be processed. The one or more digital images may have been acquired by a vision sensor carried through the field(s) by a ground-based vehicle. Based on the processing, one or more agricultural inferences indicating agricultural practices or conditions predicted to affect SOC content may be determined. Based on the agricultural inferences, one or more predicted SOC measurements for the field(s) may be determined.
Claims20
A method implemented using one or more processors, comprising:
- obtaining a plurality of digital images depicting a field over multiple growing seasons;
- applying the plurality of digital images as input to one or more machine learning models to generate first output indicative of two or more inferred agricultural management practices implemented in the field across the multiple growing seasons, wherein the two or more inferred agricultural management practices include:
- an inferred tillage practice employed in the field during the multiple growing seasons;
- a crop rotation employed in the field during the multiple growing seasons;
- or
- a cover crop grown in the field during the multiple growing seasons;
- and
- applying data indicative of the two or more inferred agricultural management practices as input to one or more additional machine learning models to generate second output, wherein the second output represents a predicted measure of soil organic carbon (SOC) of the field.
A method implemented using one or more processors, comprising:
- obtaining a plurality of digital images depicting a field over multiple growing seasons;
- obtaining ground truth data indicative of a plurality of ground truth measures of soil organic content (SOC) during the multiple growing seasons;
- applying the plurality of digital images as input to one or more machine learning models to generate first output indicative of two or more inferred agricultural management practices implemented in the field across the multiple growing seasons, wherein the two or more inferred agricultural management practices include:
- an inferred tillage practice employed in the field during the multiple growing seasons;
- a crop rotation employed in the field during the multiple growing seasons;
- or
- a cover crop grown in the field during the multiple growing seasons;
- applying data indicative of the two or more inferred agricultural management practices as input to one or more additional machine learning models to generate second output, wherein the second output represents a plurality of predicted measures of SOC of the field;
- comparing the plurality of predicted measures of SOC to the corresponding ground truth measures of SOC;
- and
- training one or more of the additional machine learning models based on the comparing.
A system comprising one or more processors and memory storing instructions that, in response to execution by the one or more processors, cause the one or more processors to:
- obtain a plurality of digital images depicting a field over multiple growing seasons;
- apply the plurality of digital images as input to one or more machine learning models to generate first output indicative of two or more inferred agricultural management practices implemented in the field across the multiple growing seasons, wherein the two or more inferred agricultural management practices include:
- an inferred tillage practice employed in the field during the multiple growing seasons;
- a crop rotation employed in the field during the multiple growing seasons;
- or
- a cover crop grown in the field during the multiple growing seasons;
- and
- apply data indicative of the two or more inferred agricultural management practices as input to one or more additional machine learning models to generate second output, wherein the second output represents a predicted measure of soil organic carbon (SOC) of the field.
Dependencies are inferred from the claim text.