Enhancing greenhouse gas emission estimates for stubble burning areas

Patent metadata

Field	Value
Publication number	US-12393995-B2
Application number	US-202217805926-A
Country	US
Kind code	B2
Filing date	Jun 8, 2022
Priority date	Jun 8, 2022
Publication date	Aug 19, 2025
Grant date	Aug 19, 2025

Abstract

A method, computer system, and a computer program product for improving environmental impact estimations is provided. The present invention may include obtaining data pertaining to an agricultural area. The present invention may include deriving one or more features from the data pertaining to the agricultural area. The present invention may include identifying one or more stubble burning areas within the agricultural areas based on the one or more derived features. The present invention may include determining an environmental impact for each of the one or more stubble burning areas.

First claim

What is claimed is:

1. A method for environmental impact estimations, the method comprising:
obtaining data pertaining to an agricultural area identified by a user in an environmental impact user interface, wherein the agricultural area is comprised of a plurality pixels corresponding to a ground area, wherein the data includes coarse resolution greenhouse gas emissions data and fine resolution explanatory variable data;
deriving one or more features from the data pertaining to the agricultural area;
displaying one or more stubble burning areas to the user in the environmental impact user interface, wherein the one or more stubble burning areas are identified within the agricultural area using a trained machine learning model, wherein the trained machine learning model utilizes the one or more derived features as input for a crop agnostic binary classification model;
determining greenhouse gas emission estimates for the one or more stubble burning areas using a stacked super resolution convolutional neural network by performing a layer by layer enhancement based on the coarse resolution greenhouse gas emissions data and the fine resolution explanatory variable data, wherein the convolutional neural network learns a functional mapping between the fine resolution and coarse resolution data; and
determining an environmental impact for each of the one or more stubble burning areas, wherein the environmental impact includes the greenhouse gas emission estimates for each pixel of the one or more stubble burnings areas within the plurality of pixels corresponding to the ground area based on the functional mapping of the one or more stubble burning areas.

2. The method of claim 1 , wherein the one or more derived features from the data pertaining to the agricultural area include a SAR power spectrum, a SAR decomposition, and a signal superposition.

3. The method of claim 2 , wherein the one or more stubble burning areas are comprised of one or more burnt pixels, the one or more burnt pixels being classified using the crop agnostic binary classification model, wherein the trained machine learning model is trained utilizing data from a verified fire information database, wherein at least the SAR power spectrum and the signal superposition are normalized using cohort analytics to build the crop agnostic binary classification model.

4. The method of claim 1 , wherein determining the environmental impact for each of the one or more stubble burning areas further comprises:
utilizing one or more explanatory variables and greenhouse gas emissions data as input for a deep learning model, wherein the deep learning model is the convolutional neural network; and
upscaling the one or more explanatory variables using one or more standard interpolation techniques.

5. The method of claim 4 , further comprising:
determining the greenhouse gas emissions for each of one or more burnt pixels classified by the crop agnostic binary classification model using the deep learning model; and
displaying the greenhouse gas emissions for each of the one or more burnt pixels to the user within the environmental impact user interface, wherein the greenhouse gas emissions data is presented separately for each of a plurality of greenhouse gases.

6. The method of claim 1 , wherein determining the environmental impact for each of the one or more stubble burning areas further comprises:
allocating coarse greenhouse gas emissions data to a finer spatial resolution using a statistical model, wherein the statistical model utilizes a FRK technique in enhancing the coarse resolution greenhouse gas emissions data based on one or more fine resolution covariates.

7. The method of claim 1 , wherein the data pertaining to the agricultural area includes contextual information, wherein the contextual information is utilized in deriving cohort analytics to normalize one or more time series features of the one or more derived features from the data pertaining to the agricultural area.

8. The method of claim 7 , further comprising:
identifying one or more cohort groups within the agricultural area based on a similarity score generated for each of a plurality of agricultural fields within the agricultural area, wherein the similarity score is generated by a multi-class classification model utilizing the cohort analytics as input, wherein the contextual information is weighted according to preferences specified by the user in the environmental impact user interface.

9. The method of claim 8 , further comprising:
utilizing the one or more cohort groups identified to refine the input for the crop agnostic binary classification model.

10. The method of claim 1 , further comprising:
displaying the one or more stubble burning areas identified to the user in the environmental impact user interface using one or more visual representations and a classification for each of a plurality of pixels comprising the agricultural area.

11. The method of claim 1 , wherein the greenhouse gas emissions estimates are an output of a deep learning model utilizing a stacked super resolution convolutional neural network technique to enhance a spatial resolution of greenhouse gas emissions data by utilizing both coarse resolution greenhouse gas emissions data and fine resolution explanatory variable data as input.

12. A computer system for environmental impact estimations, comprising:
one or more processors, one or more computer-readable memories, one or more computer-readable tangible storage medium, and program instructions stored on at least one of the one or more tangible storage medium for execution by at least one of the one or more processors via at least one of the one or more memories, wherein the computer system is capable of performing a method comprising:
obtaining data pertaining to an agricultural area identified by a user in an environmental impact user interface, wherein the agricultural area is comprised of a plurality pixels corresponding to a ground area, wherein the data includes coarse resolution greenhouse gas emissions data and fine resolution explanatory variable data;
deriving one or more features from the data pertaining to the agricultural area;
displaying one or more stubble burning areas to the user in the environmental impact user interface, wherein the one or more stubble burning areas are identified within the agricultural area using a trained machine learning model, wherein the trained machine learning model utilizes the one or more derived features as input for a crop agnostic binary classification model;
determining greenhouse gas emission estimates for the one or more stubble burning areas using a stacked super resolution convolutional neural network by performing a layer by layer enhancement based on the coarse resolution greenhouse gas emissions data and the fine resolution explanatory variable data, wherein the convolutional neural network learns a functional mapping between the fine resolution and coarse resolution data; and
determining an environmental impact for each of the one or more stubble burning areas, wherein the environmental impact includes the greenhouse gas emission estimated for each pixel of the one or more stubble burning areas within the plurality of pixels corresponding to the ground area based on the functional mapping of the one or more stubble burning areas.

13. The computer system of claim 12 , wherein the one or more derived features from the data pertaining to the agricultural area include a SAR power spectrum, a SAR decomposition, and a signal superposition.

14. The computer system of claim 13 , wherein the one or more stubble burning areas are

Assignees

• IBM

Inventors

• Hazra Jagabondhu
• Padmanaban Manikandan
• Wambugu Isaac Waweru
• Treinish Lloyd A
• Kayongo Ivan
• SAURAV Kumar
• Guruprasad Ranjini Bangalore

Classifications

• G06Q10/063

  Operations research, analysis or management · CPC title

• G06Q50/02 Primary

  Agriculture; Fishing; Forestry; Mining · CPC title