Precision Agriculture Platform Integrating Satellite and Orchard Data Models
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Solution Overview
Problem
Current agricultural data collection and analysis methods focus on single parameters in silos, lacking integration across multiple data sources, which limits the comprehensive application of information technology in precision agriculture.
Innovation Solution
A precision agriculture system that integrates satellite-generated data, weather data, soil data, and field data using machine learning to create comprehensive models for crop management, identifying issues such as water leaks, disease, and soil anomalies through spectral analysis and data integration.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If single parameter data collection is used, then data collection simplicity is improved, but data integration capability deteriorates
Solution Approach 1:
The patent merges multiple data sources including satellite imagery, weather data, soil data, and field data into a unified precision agriculture platform. This integration allows the system to analyze multiple parameters simultaneously (crop health, water needs, soil conditions, weather patterns) rather than collecting single parameters in isolation, thereby resolving the contradiction between operational simplicity and data integration capability.
2Measurement precision
If comprehensive data integration is implemented, then analysis accuracy is improved, but system complexity increases
Solution Approach 1:
The patent segments the comprehensive data integration system into distinct functional modules: satellite data acquisition module, weather data module, soil data module, field data module, and analysis engine. Each module handles specific data types and processing tasks independently. This segmentation maintains high analysis accuracy through comprehensive data integration while managing system complexity through modular architecture, where each segment can be developed, maintained, and scaled independently.
3Loss of information
If multiple data sources are integrated, then information completeness is improved, but data processing complexity increases
Solution Approach 1:
The patent introduces an intermediary data standardization layer that receives data from multiple sources (satellite, weather, soil, field sensors) and transforms them into a unified format before processing. This intermediary layer includes data validation, format conversion, and normalization functions that ensure information completeness from all sources while simplifying subsequent processing by presenting standardized data to the analysis engine, thereby reducing overall data processing complexity.
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.