Machine Learning Irrigation Modeling for Field-Level Water Decisions
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Solution Overview
Problem
Current irrigation systems lack the ability to effectively utilize the vast amounts of data collected from various sources to model and predict outcomes, relying on intuition and snapshots of data streams for decision-making, which limits their efficiency and effectiveness in optimizing water, chemical, and nutrient application.
Innovation Solution
A system and method utilizing a machine learning module that integrates data from historical applications, UAVs, satellites, field-based sensors, and climate sensors to create predictive models for field objects, allowing for data-driven decision-making and optimization of irrigation processes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If operators use intuition and snapshots of available data streams to make adjustments, then the system is easy to operate, but the decision-making effectiveness does not improve despite large amounts of data
Solution Approach 1:
The patent introduces a machine learning module as an intermediary between the irrigation system sensors and the operator. This module collects data from multiple sources (soil moisture sensors, weather stations, crop sensors), processes it through trained models, and generates actionable insights. The intermediary handles the complexity of data integration and analysis, allowing operators to receive processed recommendations without managing the underlying complexity themselves.
Solution Approach 2:
The patent replaces the mechanical/manual decision-making process with an automated machine learning system. Instead of operators manually analyzing snapshots of data and making intuitive adjustments, the system uses trained machine learning models to automatically process continuous data streams and generate optimization recommendations, substituting human intuition with algorithmic analysis.
2Reliability
If the system collects and integrates data from multiple sources, then the predictive capability improves, but the device complexity increases
Solution Approach 1:
The patent segments the data collection and processing system into distinct modular components: soil moisture sensors, weather stations, crop sensors, a data collection module, and a machine learning module. Each component handles specific data types or processing tasks independently. The machine learning module further segments analysis into different models for different field objects (zones, sectors, management units), allowing complex multi-source data integration to be managed through organized, independent modules that can be developed and maintained separately.
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.