Crop Water Estimation Using Multi-Sensor Soil Moisture Fusion
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Solution Overview
Problem
Current crop water requirement estimation models are limited by the availability of accurate weather observations, weather forecasts, information about crop growth phase, and soil water balance, leading to challenges in precise water and energy use in agriculture.
Innovation Solution
A method using multi-sensor data fusion that integrates IoT sensor data, agro-meteorological weather data, satellite earth observation data, soil properties, historical irrigation data, and weather forecasts to estimate crop water requirement through evapotranspiration calculations, runoff estimation, and soil water balance, employing inverse modeling and ensemble-based field soil moisture estimation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If current crop water requirement estimation models are used, then water and energy use can be reduced, but the accuracy of estimation is limited by availability of accurate weather observations, weather forecasts, crop growth phase information, and soil water balance data
Solution Approach 1:
The patent combines multiple data sources including IoT sensor data, satellite earth observation data, agro-meteorological weather data, soil properties, historical irrigation data, and weather forecast data into a unified crop water requirement estimation system. This merging of diverse data sources resolves the technical contradiction by improving measurement precision through comprehensive data integration while managing device complexity through systematic data fusion architecture.
Solution Approach 2:
The system performs multiple functions using the integrated data: estimating reference crop evapotranspiration, crop evapotranspiration under standard and non-standard conditions, remote sensing based evapotranspiration, runoff estimation, and soil water balance computation. This multi-functionality approach improves estimation accuracy by addressing various aspects of crop water requirements simultaneously while leveraging a single integrated system.
2Measurement precision
If more sensor data and data fusion methods are used, then the accuracy of crop water requirement estimation is enhanced, but the system complexity and data processing requirements increase
Solution Approach 1:
The patent segments the crop water requirement estimation into distinct components: reference crop evapotranspiration estimation, crop evapotranspiration under standard conditions, crop evapotranspiration under non-standard conditions, remote sensing based evapotranspiration, and runoff estimation. Each segment processes specific data types and applies appropriate methods, reducing the overall difficulty of integrating and processing multi-sensor data while maintaining high estimation accuracy.
Solution Approach 2:
The system introduces an intermediary data fusion layer that processes and integrates data from multiple sources before final crop water requirement estimation. This intermediary layer standardizes data formats, handles data quality issues, and coordinates information flow between different data sources, thereby reducing the difficulty of integrating and processing multi-sensor data while preserving measurement precision.
Data Source
AI summary
This disclosure relates generally to system and method for estimating crop water requirement using multi-sensor data fusion. Increasing global population is imparting pressure on both agriculture for food demand and limited freshwater resources for consumption. Estimating crop water requirement reduces water demand for crop production. The method divides soil and crop into multiple vertical and horizontal profiles to estimate water balance thereby reducing the errors in estimation of crop water requirement. Additionally, the method has capability to interlink the multiple data sets such as satellite based earth observations, weather observations from IoT sensors, Weather forecasts from global circulation models, and crop knowledge base for crop water requirement estimation. The method is based on spatio-temporal modeling for multi-layer crop and soil water balance and helps to generate the additional insights on crop water requirement like moisture at different levels in soil profile, crop canopy growth at different locations.