Precision Agriculture Platform Data 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 data using machine learning techniques to create comprehensive models for predicting output conditions, such as crop health and anomalies, by processing satellite spectral data and merging it with orchard and weather data to generate actionable recommendations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If data is collected and analyzed in silos focusing on single parameters, then analysis simplicity is maintained, but comprehensiveness and robustness of agricultural insights deteriorate
Solution Approach 1:
The patent merges multiple disparate data sources including satellite imagery, weather data, soil data, and field data into a unified agricultural data platform. This integration combines previously siloed datasets to provide comprehensive agricultural insights, directly addressing the contradiction by prioritizing robustness over simplicity through systematic data consolidation.
Solution Approach 2:
The agricultural data platform is designed with multi-functional capabilities to handle diverse data types and provide various agricultural analytics functions. The system universally processes satellite imagery, weather patterns, soil characteristics, and field observations through integrated machine learning models, enabling a single platform to perform multiple agricultural analysis functions simultaneously.
2Loss of information
If comprehensive data integration from multiple sources is implemented, then agricultural insight comprehensiveness is improved, but system complexity and data processing requirements increase
Solution Approach 1:
The patent introduces an intermediary layer of integrated machine learning models that mediate between raw disparate data sources and agricultural insights. These models serve as intermediaries that standardize and process multiple data types (satellite imagery, weather data, soil data, field data) into unified agricultural metrics, reducing the complexity burden of comprehensive data integration while maintaining information completeness.
Solution Approach 2:
The system transforms diverse agricultural data sources into standardized parameters and metrics through machine learning processing. By converting satellite imagery, weather patterns, soil characteristics, and field observations into unified agricultural parameters, the system maintains complete information while simplifying subsequent analysis and decision-making processes.
3Productivity
If single-parameter analysis is used, then processing speed is maintained, but ability to provide actionable insights deteriorates
Solution Approach 1:
The patent implements preliminary action by pre-processing and integrating multiple data sources before agricultural analysis is needed. The system continuously ingests and processes satellite imagery, weather data, soil data, and field data in advance, maintaining updated agricultural models that can quickly generate actionable insights when queries are made, thus reducing real-time processing time while maintaining comprehensive analysis capabilities.
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.