Precision Agriculture Platform Integrating Satellite and Orchard Data
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Solution Overview
Problem
Current agricultural data collection and analysis methods focus on single parameters in silos, lacking integration of disparate datasets from multiple sources, which limits the comprehensive application of information technology for improving crop cultivation outcomes.
Innovation Solution
A precision agriculture system that integrates satellite data, orchard data, and weather data through machine learning models to analyze and predict output conditions, such as crop health and anomalies, by processing satellite imagery and field data, and incorporating farming knowledge to provide actionable recommendations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If data is collected and analyzed in silos focusing on single parameters, then analysis simplicity is maintained, but comprehensive understanding of crop conditions deteriorates
Solution Approach 1:
The patent merges multiple data sources including satellite imagery, weather data, soil data, and historical farm data into a unified precision agriculture platform. This integration combines previously siloed datasets to provide comprehensive crop condition analysis, resolving the contradiction between data integration complexity and information completeness.
Solution Approach 2:
The precision agriculture platform performs multiple functions including disease detection, yield prediction, irrigation optimization, and fertilizer management within a single integrated system. This multi-functional approach consolidates various single-parameter analysis tools into one comprehensive platform, addressing the contradiction by providing universal data integration capabilities.
2Productivity
If comprehensive data integration from multiple sources is implemented, then crop management effectiveness is improved, but system complexity increases
Solution Approach 1:
The patent segments the comprehensive data integration system into modular components: satellite data processing module, weather data integration module, soil analysis module, and recommendation engine. Each module handles specific data types independently before integration, reducing overall system complexity while maintaining comprehensive crop management capabilities.
Solution Approach 2:
The patent introduces intermediary processing layers including data normalization modules, feature extraction algorithms, and machine learning models that mediate between raw multi-source data and final management recommendations. These intermediaries simplify the integration process by standardizing data formats and extracting key features before comprehensive 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.