Automated Irrigation System Using Look-Ahead Sensor Data
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Solution Overview
Problem
Existing irrigation systems face inefficiencies in watering due to reliance on detailed field maps, which require significant human effort or machine learning, and are prone to errors from environmental factors like wind or pre-existing moisture levels, leading to wasted water and potential crop damage.
Innovation Solution
An automated irrigation system with a movable structure equipped with nozzles, sensors, and a controller that uses predictive models based on look-ahead and look-behind sensor data to adjust watering prescriptions in real-time, optimizing water distribution and reducing human intervention.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If detailed field maps are used to generate watering prescriptions, then watering precision is improved, but device complexity and human labor requirements increase
Solution Approach 1:
The irrigation system performs self-calibration by automatically comparing predicted water application with actual sensor measurements and adjusting its own parameters without external intervention. The controller continuously learns from sensor data to improve future watering prescriptions, eliminating the need for manual field mapping and complex preprocessing.
Solution Approach 2:
The patent replaces manual field mapping and complex machine learning workflows with a simplified sensor-based feedback system. Instead of relying on pre-generated field maps requiring human effort, the system uses real-time sensor measurements and predictive models that automatically adapt to field conditions.
2Measurement precision
If field maps are manually developed by human operators, then measurement precision is improved, but loss of time and labor costs increase
Solution Approach 1:
The system performs preliminary calibration by collecting sensor data during initial irrigation operations and using this data to establish baseline parameters. This preliminary action enables the system to generate accurate watering prescriptions without requiring time-consuming manual field mapping beforehand.
Solution Approach 2:
The irrigation system automatically builds its own field-specific knowledge base through continuous sensor monitoring and predictive modeling, eliminating the need for manual field map creation. The system serves itself by learning from operational data to improve future performance.
3Productivity
If watering prescriptions are generated based on field maps, then water distribution is optimized, but adaptability to real-time environmental changes deteriorates
Solution Approach 1:
The system incorporates continuous sensor feedback from both look-ahead (unwatered) and look-behind (watered) areas to dynamically adjust watering prescriptions. This feedback loop enables real-time adaptation to environmental changes such as wind conditions, pre-existing moisture levels, and crop water needs while maintaining high watering efficiency.
Solution Approach 2:
The patent transitions from static field map-based prescriptions to dynamic, real-time adaptive control. The system continuously updates watering decisions based on current sensor measurements and predictive models, allowing it to respond dynamically to changing environmental conditions during irrigation operations.
4Loss of energy
If look-ahead and look-behind sensor data are used to adjust watering, then water usage efficiency is improved, but device complexity increases
Solution Approach 1:
The sensor system serves multiple functions: it measures pre-existing moisture levels in unwatered areas, monitors water application in watered areas, provides feedback for predictive models, and enables real-time prescription adjustment. This multi-functionality justifies the sensor complexity by delivering comprehensive water management benefits.
Solution Approach 2:
The dual sensor configuration (look-ahead and look-behind) creates a comprehensive feedback system that measures both target conditions and actual results. This feedback enables the predictive model to learn from discrepancies between predicted and actual water application, continuously improving water usage efficiency.
Data Source
AI summary
An automated irrigation system includes a movable structure that has nozzles for spraying water on aimed area of ground. A controller selects a nozzle and determines a location in an unwatered area to be sprayed after the movable structure has moved forward. A predictive model that takes into account look-ahead sensor data for the location before watering and prior look-behind sensor data from a last time the location was watered is used to generate a watering prescription for the nozzle. After watering, the controller determines an error in detected moisture of the location according to updated look-behind sensor data and updates the predictive model if the error exceeds a threshold. When generating watering prescriptions for multiple nozzles, the controller normalizes their duty cycles by adjusting the speed of the movable structure and further customizes their nozzle control signals to ensure the nozzles do not turn off at same time.