# US 12,118,626 B2 · Generating context-aware process-based model determinations for greenhouse gas emissions from agricultural fields > Agents: this data is available as JSON from the patentgrep API and MCP server — see https://patentgrep.com/llms.txt - Number: US 12,118,626 B2 - Document ID: US12118626B2 - Status: Granted - Kind: B2 - Application number: 17/495,573 - Filed: 2021-10-06 - Published: 2023-04-06 - Granted: 2024-10-15 - Earliest claimed: 2021-10-06 - Assignees: International Business Machines Corporation - Applicants: International Business Machines Corporation - Inventors: Manikandan Padmanaban; Ranjini Bangalore Guruprasad; Isaac Waweru Wambugu; Kumar Saurav; Ivan Kayongo; Jagabondhu Hazra - Web page: https://patentgrep.com/patent/US12118626B2 ## CPC - G06Q 50/02: Agriculture; Fishing; Forestry; Mining - G06Q 10/04: Forecasting or optimisation specially adapted for administrative or management purposes, e.g. linear programming or "cutting stock problem" (market predictions or forecasting for commercial activities G06Q30/0202) - G06Q 30/018: Certifying business or products - Y02P 90/84: Greenhouse gas [GHG] management systems - G06Q: INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR - Y02P: CLIMATE CHANGE MITIGATION TECHNOLOGIES IN THE PRODUCTION OR PROCESSING OF GOODS ## Abstract Methods, systems, and computer program products for generating context-aware process-based model determinations for greenhouse gas emissions from agricultural fields are provided herein. A computer-implemented method includes obtaining data related to multiple conditions pertaining to at least one agricultural field; deriving one or more contextual features for one or more activities associated with the at least one agricultural field, wherein deriving the contextual feature(s) includes processing at least a portion of the obtained data using one or more activity-related models; updating one or more greenhouse gas emission estimates, pertaining to the at least one agricultural field, generated by at least one process-based model by processing at least a portion of the one or more greenhouse gas emission estimates and at least a portion of the derived contextual feature(s) using a spatio-temporal learning model; and performing one or more automated actions based on the one or more updated greenhouse gas emission estimates. ## Claims ### Claim 1 A computer-implemented method comprising: obtaining data related to multiple conditions pertaining to at least one agricultural field; deriving one or more contextual features for one or more activities associated with the at least one agricultural field, wherein deriving the one or more contextual features comprises processing at least a portion of the obtained data using one or more activity-related models, and wherein deriving the one or more contextual features comprises deriving one or more contextual features for weeding-related activity and deriving one or more contextual features for at least one of soil disturbance activity, temperature inversion activity, pest-related activity, and disease-related activity; updating one or more greenhouse gas emission estimates, pertaining to the at least one agricultural field, generated by at least one process-based model by processing at least a portion of the one or more greenhouse gas emission estimates and at least a portion of the one or more derived contextual features using a spatio-temporal learning model, wherein the spatio-temporal learning model comprises at least one neural network, and wherein updating the one or more greenhouse gas emission estimates comprises updating the one or more greenhouse gas emission estimates based at least in part on one or more greenhouse gas emission quantity updates and one or more greenhouse gas emission temporal updates, wherein the one or more greenhouse gas emission temporal updates comprise one or more contextual feature-effected time delay corrections to the one or more greenhouse gas emission estimates generated by the at least one process-based model, the one or more contextual feature-effected time delay corrections being related to forecasted timing of greenhouse gas emission relative to performance of at least one of the one or more activities associated with the at least one agricultural field; and performing one or more automated actions based at least in part on the one or more updated greenhouse gas emission estimates, wherein performing the one or more automated actions comprises automatically training the spatio-temporal learning model using at least a portion of the one or more updated greenhouse gas emission estimates; wherein the method is carried out by at least one computing device. ### Claim 2 (depends on 1) The computer-implemented method of claim 1, wherein obtaining data comprises obtaining hyperspectral optical data related to one or more conditions pertaining to the at least one agricultural field. ### Claim 3 (depends on 1) The computer-implemented method of claim 1, wherein obtaining data comprises obtaining microwave data related to one or more conditions pertaining to the at least one agricultural field. ### Claim 4 (depends on 1) The computer-implemented method of claim 1, wherein obtaining data comprises obtaining one or more of soil-related data, agricultural field practices-related data, weather-related data, and geolocation data. ### Claim 5 (depends on 1) The computer-implemented method of claim 1, wherein the spatio-temporal learning model comprises at least one long short-term memory model. ### Claim 6 (depends on 1) The computer-implemented method of claim 1, wherein performing one or more automated actions comprises automatically training the one or more activity-related models based at least in part on one or more of the one or more derived contextual features and the one or more updated greenhouse gas emission estimates. ### Claim 7 (depends on 1) The computer-implemented method of claim 1, wherein performing one or more automated actions comprises generating one or more agricultural-related instructions to be carried out by at least one of one or more automated systems and one or more users. ### Claim 8 (depends on 1) The computer-implemented method of claim 1, wherein performing one or more automated actions comprises performing one or more automated agricultural-related modifications to at least a portion of the at least one agricultural field. ### Claim 9 (depends on 1) The computer-implemented method of claim 1, wherein software implementing the method is provided as a service in a cloud environment. ### Claim 10 A computer program product comprising a non-transitory computer readable storage medium having program instructions embodied therewith, the program instructions executable by a computing device to cause the computing device to: obtain data related to multiple conditions pertaining to at least one agricultural field; derive one or more contextual features for one or more activities associated with the at least one agricultural field, wherein deriving the one or more contextual features comprises processing at least a portion of the obtained data using one or more activity-related models, and wherein deriving the one or more contextual features comprises deriving one or more contextual features for weeding-related activity and deriving one or more contextual features for at least one of soil disturbance activity, temperature inversion activity, pest-related activity, and disease-related activity; update one or more greenhouse gas emission estimates, pertaining to the at least one agricultural field, generated by at least one process-based model by processing at least a portion of the one or more greenhouse gas emission estimates and at least a portion of the one or more derived contextual features using a spatio-temporal learning model, wherein the spatio-temporal learning model comprises at least one neural network, and wherein updating the one or more greenhouse gas emission estimates comprises updating the one or more greenhouse gas emission estimates based at least in part on one or more greenhouse gas emission quantity updates and one or more greenhouse gas emission temporal updates, wherein the one or more greenhouse gas emission temporal updates comprise one or more contextual feature-effected time delay corrections to the one or more greenhouse gas emission estimates generated by the at least one process-based model, the one or more contextual feature-effected time delay corrections being related to forecasted timing of greenhouse gas emission relative to performance of at least one of the one or more activities associated with the at least one agricultural field; and perform one or more automated actions based at least in part on the one or more updated greenhouse gas emission estimates, wherein performing the one or more automated actions comprises automatically training the spatio-temporal learning model using at least a portion of the one or more updated greenhouse gas emission estimates. ### Claim 11 (depends on 10) The computer program product of claim 10, wherein obtaining data comprises obtaining hyperspectral optical data related to one or more conditions pertaining to the at least one agricultural field. ### Claim 12 (depends on 10) The computer program product of claim 10, wherein obtaining data comprises obtaining microwave data related to one or more conditions pertaining to the at least one agricultural field. ### Claim 13 (depends on 10) The computer program product of claim 10, wherein the spatio-temporal learning model comprises at least one long short-term memory model. ### Claim 14 A system comprising: a memory configured to store program instructions; and a processor operatively coupled to the memory to execute the program instructions to: obtain data related to multiple conditions pertaining to at least one agricultural field; derive one or more contextual features for one or more activities associated with the at least one agricultural field, wherein deriving the one or more contextual features comprises processing at least a portion of the obtained data using one or more activity-related models, and wherein deriving the one or more contextual features comprises deriving one or more contextual features for weeding-related activity and deriving one or more contextual features for at least one of soil disturbance activity, temperature inversion activity, pest-related activity, and disease-related activity; update one or more greenhouse gas emission estimates, pertaining to the at least one agricultural field, generated by at least one process-based model by processing at least a portion of the one or more greenhouse gas emission estimates and at least a portion of the one or more derived contextual features using a spatio-temporal learning model, wherein the spatio-temporal learning model comprises at least one neural network, and wherein updating the one or more greenhouse gas emission estimates comprises updating the one or more greenhouse gas emission estimates based at least in part on one or more greenhouse gas emission quantity updates and one or more greenhouse gas emission temporal updates, wherein the one or more greenhouse gas emission temporal updates comprise one or more contextual feature-effected time delay corrections to the one or more greenhouse gas emission estimates generated by the at least one process-based model, the one or more contextual feature-effected time delay corrections being related to forecasted timing of greenhouse gas emission relative to performance of at least one of the one or more activities associated with the at least one agricultural field; and perform one or more automated actions based at least in part on the one or more updated greenhouse gas emission estimates, wherein performing the one or more automated actions comprises automatically training the spatio-temporal learning model using at least a portion of the one or more updated greenhouse gas emission estimates. ### Claim 15 (depends on 14) The system of claim 14, wherein performing one or more automated actions comprises automatically training the one or more activity-related models based at least in part on one or more of the one or more derived contextual features and the one or more updated greenhouse gas emission estimates. ### Claim 16 (depends on 14) The system of claim 14, wherein performing one or more automated actions comprises generating one or more agricultural-related instructions to be carried out by at least one of one or more automated systems and one or more users. ### Claim 17 (depends on 14) The system of claim 14, wherein performing one or more automated actions comprises performing one or more automated agricultural-related modifications to at least a portion of the at least one agricultural field. ### Claim 18 (depends on 14) The system of claim 14, wherein obtaining data comprises obtaining hyperspectral optical data related to one or more conditions pertaining to the at least one agricultural field. ### Claim 19 (depends on 14) The system of claim 14, wherein obtaining data comprises obtaining microwave data related to one or more conditions pertaining to the at least one agricultural field. ### Claim 20 (depends on 14) The system of claim 14, wherein obtaining data comprises obtaining one or more of soil-related data, agricultural field practices-related data, weather-related data, and geolocation data.