Precision Agriculture Satellite Data Model Integration
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Solution Overview
Problem
Current agricultural data collection and analysis methods focus on single parameters in silos, lacking comprehensive integration of disparate datasets from multiple sources, which limits the ability to provide robust and actionable insights for precision agriculture.
Innovation Solution
A precision agriculture system that integrates satellite-generated data, weather data, soils data, and field-acquired data using machine learning models to generate actionable recommendations for crop management, including water management, soil amendments, and disease detection, by processing satellite imagery and merging it with orchard and weather data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If data is collected and analyzed in silos focusing on single parameters, then data processing is simple and focused, but the comprehensiveness and robustness of agricultural insights are limited
Solution Approach 1:
The patent merges multiple disparate data sources including satellite imagery, weather data, soil data, and field data into a unified data model. This integration combines previously siloed datasets to provide comprehensive agricultural insights, directly addressing the limitation of single-parameter analysis while managing complexity through systematic data fusion.
Solution Approach 2:
The unified data model serves multiple functions simultaneously: it integrates diverse data types, provides comprehensive crop monitoring, enables various agricultural analyses, and supports multiple decision-making processes. This multi-functional approach improves insight robustness without proportionally increasing system complexity.
2Loss of information
If comprehensive data integration from multiple sources is implemented, then the robustness and actionability of agricultural insights improve, but system complexity increases
Solution Approach 1:
The patent combines satellite imagery data, weather data, soil data, and field data into a unified data model, ensuring no critical agricultural information is lost. This systematic merging approach maintains data completeness while managing integration complexity through a structured framework.
Solution Approach 2:
The unified data model acts as an intermediary layer that standardizes and harmonizes disparate data sources before analysis. This mediator structure reduces integration complexity by providing a common interface for diverse data types while preserving the completeness of all input data.
3Productivity
If satellite imagery processing is integrated with orchard and weather data, then actionable recommendations for crop management are enhanced, but computational requirements and processing time increase
Solution Approach 1:
The system performs preliminary processing and integration of satellite imagery with orchard and weather data in advance, creating a ready-to-use unified data model. This preliminary action reduces processing time when generating actionable crop management recommendations, as the data integration work is already completed.
Solution Approach 2:
The unified data model enables continuous monitoring and analysis of crop conditions by maintaining integrated satellite, weather, and orchard data. This continuous integration approach improves the timeliness and actionability of recommendations without requiring repeated full-data processing for each analysis.
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.