Irrigation Control Using Localized Weather Prediction Correlation
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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, leading to sub-optimal production outcomes.
Innovation Solution
An irrigation system that incorporates an irrigation actuator, a receiver for wide-area meteorological prediction data, a sensor network, and a processor to determine correlations between historical wide-area and local-area data, enabling precise predictions of water supply relative to demand and optimizing irrigation based on these predictions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If farmers use wide-area meteorological prediction data alone for irrigation decisions, then the decision-making process is simple, but the prediction accuracy is insufficient due to inability to capture local micro-climatic variations
Solution Approach 1:
The patent combines wide-area meteorological prediction data with local-area sensor data into a unified prediction system. The processor integrates both data sources to generate localized predictions that capture micro-climatic variations while maintaining system simplicity through automated data fusion rather than manual analysis of multiple complex datasets
Solution Approach 2:
The patent introduces a correlation model as an intermediary that bridges wide-area meteorological data and local sensor observations. This correlation model learns the relationship between broad weather patterns and local conditions, enabling accurate local predictions without requiring complex direct modeling of all local factors
2Measurement precision
If farmers deploy sensor networks to capture local micro-climatic variations, then prediction accuracy improves, but the cost and complexity of the system increases
Solution Approach 1:
The patent uses a limited number of sensors strategically deployed to capture key micro-climatic parameters rather than comprehensive coverage. The system achieves sufficient accuracy with partial observation of local conditions, avoiding the need for extensive sensor networks that would dramatically increase deployment costs
Solution Approach 2:
The patent creates a virtual model of local micro-climate by copying and adapting wide-area meteorological data through learned correlation relationships. This virtual representation captures essential local variations without requiring physical sensors for every parameter, reducing hardware costs while maintaining prediction accuracy
3Measurement precision
If complex modeling is used to capture local-area characteristics, then prediction accuracy improves, but the robustness and ease of deployment decreases
Solution Approach 1:
The system performs self-calibration by automatically learning correlation relationships between wide-area meteorological data and local sensor observations during an initial period. This self-service approach eliminates the need for manual model configuration and tuning, making the system robust to different locations and easy to deploy without expert intervention
Solution Approach 2:
The patent uses parameter-based correlation models that adapt to local conditions through learned parameters rather than fixed complex physical models. These parameters capture local micro-climatic characteristics and can be automatically determined from data, providing both accuracy and robustness across diverse terrains and crop types without requiring complex modeling
Data Source
AI summary
An irrigation system for an area receives wide-area meteorological prediction data and sensors deployed within the 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 relationship 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 a local-area parameter for a future point in time based on current wide-area meteorological prediction data, and the calculated relationship. The area is then controlled based on the prediction.