Machine Learning Irrigation Control Using Multi-Source Field Data
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Solution Overview
Problem
Existing irrigation systems lack the ability to effectively utilize the vast amounts of data collected from various sources to model and control irrigation processes, relying heavily on operator intuition and snapshots of data streams, limiting decision-making capabilities.
Innovation Solution
A system and method utilizing a machine learning module that integrates data from multiple sources, including historical applications, UAVs, satellites, and field-based sensors, to create predictive models for field objects, enabling advanced data analysis and control of irrigation systems.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If traditional irrigation control systems are used with user interfaces, then operators can monitor and control irrigation functions, but the decision-making process remains limited to operator intuition and snapshots of data
Solution Approach 1:
The patent replaces manual operator intuition and snapshot-based decision making with continuous machine learning models that automatically process sensor data streams. The system substitutes human cognitive processing with automated algorithms that continuously optimize irrigation decisions based on real-time environmental and crop data.
Solution Approach 2:
The system enables self-service through autonomous machine learning models that automatically adjust irrigation parameters without requiring continuous operator intervention. The models independently analyze data from multiple sensors and execute control decisions, freeing operators from manual monitoring while maintaining optimized performance.
2Quantity of substance
If multiple data sources are collected (sensors, UAVs, satellites), then comprehensive field data is available, but the ability to model and use this data effectively is lacking
Solution Approach 1:
The patent merges data from heterogeneous sources including soil sensors, weather stations, UAV imagery, and satellite data into unified machine learning models. The system integrates these diverse data streams to create comprehensive field representations, enabling the models to leverage information from all sources simultaneously for improved prediction accuracy.
Solution Approach 2:
The system adds the temporal dimension by processing continuous data streams rather than static snapshots. Machine learning models analyze time-series data from sensors and periodic data from UAVs and satellites, transforming spatial data into spatio-temporal models that capture dynamic field conditions and enable predictive analytics.
3Reliability
If machine learning models are implemented, then predictive capabilities are enhanced, but system complexity increases
Solution Approach 1:
The patent segments the machine learning system into modular components: data ingestion modules for different sensor types, preprocessing modules for data cleaning and normalization, training modules for model development, and deployment modules for executing predictions. This segmentation allows each component to be independently optimized and maintained, reducing overall system complexity despite the advanced capabilities.
Data Source
AI summary
The present invention provides a system and method which includes a machine learning module which analyzes data collected from one or more sources such as UAVs, satellites, span mounted crop sensors, direct soil sensors and climate sensors. According to a further preferred embodiment, the machine learning module preferably creates sets of field objects from within a given field and uses the received data to create a predictive model for each defined field object based on detected characteristics from each field object within the field.