Precision Agriculture Data Model Integrating Satellite and Soil Inputs
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Solution Overview
Problem
Current agricultural data collection and analysis methods focus on single parameters in silos, lacking integration of diverse data sources, which limits the comprehensive understanding and application of data for improving agricultural techniques.
Innovation Solution
A precision agriculture system that integrates satellite-generated data, weather data, soils data, and field data using machine learning to create models that analyze and predict output conditions such as crop health, soil issues, and water management, incorporating farming knowledge to provide actionable recommendations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If data is collected and analyzed in silos focusing on single parameters, then analysis simplicity is maintained, but comprehensive understanding and accuracy of agricultural outcomes deteriorate
Solution Approach 1:
The patent combines multiple disparate data sources including satellite imagery, weather data, soil data, and field data into a unified data model. This merging of previously siloed data sources enables comprehensive analysis of agricultural outcomes by integrating information that was previously analyzed separately, thereby improving measurement precision without requiring separate analysis systems for each data type.
Solution Approach 2:
The unified data model serves multiple functions by simultaneously processing and analyzing various types of agricultural data (satellite, weather, soil, field data) through a single integrated system. This multi-functional approach allows the system to handle diverse data sources and provide comprehensive agricultural outcome analysis, reducing the need for multiple separate analysis tools while improving overall accuracy.
2Reliability
If comprehensive data from multiple sources is integrated, then robustness and accuracy of predictions improve, but system complexity and difficulty of implementation increase
Solution Approach 1:
The patent segments the complex data integration process into manageable components by organizing data into a structured unified model with specific data types (satellite imagery, weather data, soil data, field data) and corresponding data structures. This segmentation allows the system to handle complexity through modular organization, making implementation more tractable while maintaining comprehensive data integration for reliable predictions.
Solution Approach 2:
The unified data model acts as an intermediary layer that mediates between diverse data sources and the analysis algorithms. This intermediary structure standardizes and harmonizes data from multiple sources, simplifying the integration process while enabling robust predictions by providing a consistent interface for processing various data types.
3Productivity
If traditional analytical techniques are used without data integration, then ease of operation is maintained, but ability to compute comprehensive information deteriorates
Solution Approach 1:
The system automatically processes and integrates data from multiple sources through the unified data model without requiring manual intervention for data harmonization. This self-service capability enables comprehensive information computation by automatically combining satellite, weather, soil, and field data, improving productivity while maintaining ease of operation through automated processes.
Solution Approach 2:
The patent transforms the operational approach by changing from separate parameter analysis to integrated multi-parameter analysis through the unified data model. This parameter change enables the system to compute comprehensive information by simultaneously considering multiple data types and their interrelationships, significantly improving analytical capability while the standardized model structure maintains operational simplicity.
Data Source
AI summary
A computer platform implements a precision agriculture system that predicts output conditions, such as diseases, salt damage, soil problems, water leaks and generic anomalies, for orchards under analysis. The computer platform stores site and crop datasets and processed satellite image for the orchards. An orchard data learned model predicts a propensity for existence of output conditions associated with the permanent crops based on the data values for the variables of the site and crop datasets. Also, a satellite model predicts a propensity for existence of the output conditions at the orchard based on processed satellite images. A precision agriculture management model is disclosed that integrates the orchard data learned model with the satellite model to accurately predict the output conditions.