Agricultural Modeling System Data Fusion
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Solution Overview
Problem
Current agricultural data collection and analysis methods are fragmented, as they consider each data source independently and fail to aggregate data effectively, leading to inaccuracies in crop yield forecasting, increased production costs, and a greater environmental impact.
Innovation Solution
An agricultural modeling system that integrates a scanning platform for generating 3D point cloud data, a geospatial database for storing various data layers, and a computing resource for geographically referencing and fusing these data layers to generate multi-layered data models, enabling the prediction of yield, crop fertility, and irrigation characteristics.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If data sources are considered independently without aggregation, then data processing is simpler, but prediction accuracy and yield estimation deteriorate
Solution Approach 1:
The patent merges multiple independent data sources (satellite imagery, ground sensors, weather data, soil data) into a unified agricultural data model. This integration allows the system to leverage complementary information from different sources, improving prediction accuracy while managing complexity through standardized data processing pipelines and cloud-based computing resources.
2Measurement precision
If high-resolution 3D point cloud data is collected, then spatial detail and plant-level analysis are improved, but data storage and processing requirements increase
Solution Approach 1:
The patent segments the agricultural field into discrete plant-level units and further into point cloud data structures. By organizing high-resolution 3D data into manageable point cloud segments with specific spatial coordinates, the system maintains detailed spatial information while enabling efficient storage and processing through distributed computing and selective data retrieval based on query parameters.
3Loss of information
If multiple data layers are fused and geographically referenced, then comprehensive agricultural insights are improved, but computational requirements and processing time increase
Solution Approach 1:
The patent performs preliminary georeferencing and fusion of multiple data layers (satellite imagery, ground truth data, weather data, soil data) into a pre-integrated agricultural data model stored in cloud databases. This preliminary processing allows downstream applications to query pre-fused data without repeating computationally intensive fusion operations, significantly reducing processing time for specific agricultural insights while maintaining information completeness.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
The system enhances the value of diverse data sources by organizing and linking them across different scales and methodologies, improving the accuracy of agricultural predictions, reducing production costs, and minimizing environmental impact.
Implementation Method 1
a scanning platform (e.g. light detection and ranging (LiDAR)) configured to generate 3D point cloud data of the agricultural geographic area
Data Source
AI summary
An agricultural modeling system may include a scanning platform configured to generate 3D point cloud data of an agricultural geographic area, a geospatial database configured to store a data layer for the agricultural geographic area, and a computing resource in communication with the scanning platform, client devices, and the geospatial database. The computing resource may be configured to geographically reference the data layer fused with the 3D point cloud data of the agricultural geographic area, and generate a multi-layered data model for the geographically referenced data layer fused with the 3D point cloud data of the agricultural geographic area.