Regenerative Agriculture Adoption Index With GHG Forecasting
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Solution Overview
Problem
Conventional models for estimating greenhouse gas emissions in agriculture are limited to personalized recommendations based on user inputs and fail to account for historical trends and real-time variations, making it difficult for farmers to transition to regenerative agriculture practices effectively.
Innovation Solution
A knowledge graph-driven machine learning approach that incorporates causal relationships between agricultural practices, crops, and emissions, using feature embeddings and graph-based regularization to forecast GHG emissions and recommend sustainable practices, while estimating a dynamic adoption index and transition time for regenerative agriculture.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If conventional models use only user inputs on agricultural practices, then the model complexity is reduced, but the measurement precision of GHG emissions estimation deteriorates
Solution Approach 1:
The patent introduces a knowledge graph as an intermediary structure that connects user inputs with emissions estimation. The knowledge graph stores pre-established relationships between agricultural practices, crops, and emissions factors, serving as a mediator that enhances estimation precision without requiring complex model architecture. This resolves the contradiction by providing accurate emissions calculations through structured knowledge rather than complex computational models.
2Ease of operation
If conventional models focus on personalized recommendations only, then the ease of operation is improved, but the adaptability to historical trends and real-time variations deteriorates
Solution Approach 1:
The patent performs preliminary actions by pre-building a knowledge graph that encapsulates historical trends, causal relationships, and expert knowledge about agricultural practices and emissions. This pre-computed knowledge structure enables the system to adapt to both historical patterns and real-time variations while maintaining ease of operation. The knowledge graph serves as a prepared resource that can be quickly queried and applied without complex real-time analysis.
3Device complexity
If conventional models lack causal relationship modeling, then the device complexity is reduced, but the reliability of emissions forecasting deteriorates
Solution Approach 1:
The knowledge graph acts as an intermediary that encodes causal relationships between agricultural practices and emissions outcomes. Rather than implementing complex causal inference algorithms, the system uses the knowledge graph to represent established causal knowledge from agricultural science and emissions research. This provides reliable forecasting through structured domain knowledge while avoiding the complexity of sophisticated causal modeling.
Data Source
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AI summary
Climate change has become a matter of concern due to increase in green-house gases (GHG) emissions. Agriculture is major contributor in GHG emissions. The present disclosure provides a system and method for estimation of dynamic adoption index and transition time for shifting to regenerative agriculture. The present disclosure utilizes knowledge graph-driven machine learning for GHG emissions forecasting from an agricultural land using a historical information associated with the agricultural land derived from remote sensing and on-field sensors. Further, a dynamic adoption index is estimated with knowledge-driven machine learning and data integration using data on forecasted GHG emissions, recommended agricultural practices and followed agricultural practices on the farm. Furthermore, a dynamic transition time required for shifting to regenerative agriculture is estimated with machine learning and context-sensitive modeling.