Irrigation Control Using Local Sensor-Correlated Weather Forecasts
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Solution Overview
Problem
Agricultural production is hindered by the complexity of environmental influences, making it difficult for farmers to make accurate decisions regarding irrigation and crop management due to the multitude of factors involved, leading to sub-optimal production outcomes.
Innovation Solution
A computer-implemented method that uses local-area sensor data to create more accurate predictions by correlating with wide-area meteorological data, allowing for precise control of irrigation and crop management, adaptable to various terrains and sensor types, and capable of learning over time for improved accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Area of stationary object
If wide-area meteorological forecasts are used for agricultural decision-making, then coverage area is large, but prediction accuracy deteriorates due to inability to capture local micro-climatic variations
Solution Approach 1:
The patent applies local quality by combining wide-area meteorological forecasts with local-area sensor data to create location-specific predictions. The system determines correlations between wide-area forecast data and local sensor measurements, then uses these correlations to adjust predictions for each specific agricultural production area, capturing micro-climatic variations while maintaining broad coverage capability
Solution Approach 2:
The patent uses local-area sensor data as an intermediary to bridge the gap between wide-area forecasts and local conditions. The sensor data serves as a mediator that captures local micro-climatic effects and translates them into corrected predictions, allowing the system to maintain both wide coverage and high accuracy
2Measurement precision
If local-area sensor data is collected and processed, then prediction accuracy is improved, but system complexity increases
Solution Approach 1:
The patent applies universality by designing a correlation determination mechanism that can work with various types of sensors and wide-area forecast data sources. The system establishes general correlation relationships between forecast parameters and sensor measurements that can be applied across different agricultural production areas and sensor types, reducing the need for area-specific complex modeling
Solution Approach 2:
The patent uses parameter changes by determining correlation coefficients between wide-area forecast data and local sensor data. These correlation parameters are used to adjust and transform the wide-area predictions into local-area predictions, simplifying the processing compared to full physical modeling while maintaining accuracy
3Measurement precision
If complex environmental modeling is performed to capture micro-climatic effects, then prediction accuracy is improved, but computational cost and time increase
Solution Approach 1:
The patent applies copying by using historical correlations between wide-area forecast data and local sensor measurements as a template for current predictions. Instead of performing complex real-time physical modeling, the system copies the relationship patterns established from historical data and applies them to current forecast data, significantly reducing computational time while maintaining accuracy
Data Source
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AI summary
This disclosure relates to an irrigation system for an agricultural production area. The system receives wide-area meteorological prediction data and sensors deployed within the agricultural production area collect local-area sensor data. A processor stores received data as historical wide-area meteorological prediction data and data from the sensors as historical local-area sensor data. The processor determines a correlation between the historical wide-area meteorological prediction data and the historical local-area sensor data based on the historical wide-area meteorological prediction data and the historical local-area sensor data, and calculates a prediction on water supply relative to water demand within the agricultural production area based on current wide-area meteorological prediction data, and the calculated correlation. The irrigation actuator is then controlled based on the prediction on water supply relative to water demand to define an amount of water to be used for irrigating the agricultural production area.