Thermal/Visible Imager Segmentation for Crop ET Accuracy
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Solution Overview
Problem
Current crop water stress monitoring and evapotranspiration (ET) estimation rely on approximations based on single radiometer measurements, which mix crop and soil temperatures, especially under sunlit or shaded conditions, lacking accurate imagery-based methods adapted for agricultural applications.
Innovation Solution
A dual smart camera system integrating a miniature thermal and RGB camera with image processing algorithms segments images into sunlit and shaded components of soil, residue, vegetation, and snow, using color and texture features, and applies energy balance models for precise ET estimation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If single radiometer measurements are used for ET estimation, then device complexity is reduced, but measurement precision deteriorates due to mixing crop and soil temperatures
Solution Approach 1:
The patent segments the thermal image into distinct components (crop canopy, soil, residue, water) using image processing algorithms. This allows separate temperature measurement of each component, eliminating the mixing problem inherent in single radiometer measurements while maintaining a practical system architecture.
Solution Approach 2:
The patent introduces an intermediary processing layer (image processing algorithms) between the thermal imaging sensor and the temperature measurement. This intermediary enables the extraction of component-specific temperatures from the mixed thermal signal, resolving the contradiction between measurement precision and device complexity.
2Measurement precision
If combined thermal and visible imagery is used for accurate ET assessment, then measurement precision improves, but device complexity increases
Solution Approach 1:
The patent merges thermal and visible imagery into a unified processing framework. By co-registering the two image types and integrating their information through component masks, the system achieves accurate component temperature separation while managing the complexity of operating dual sensors.
Solution Approach 2:
The patent creates a multi-functional processing algorithm that handles both thermal and visible imagery through a single unified workflow. This universal approach manages the complexity of the dual-camera system by using common processing steps for both sensor types.
3Measurement precision
If image processing algorithms are implemented for component temperature separation, then measurement precision improves, but loss of time increases due to processing requirements
Solution Approach 1:
The patent performs preliminary actions by pre-defining component masks and classification rules based on spectral and spatial characteristics. This preparation work is done beforehand, allowing the actual temperature separation process to proceed more efficiently when images are acquired, reducing real-time processing delays.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
Accurately measures separate temperatures of soil, residue, and vegetation under varying conditions, improving ET estimation accuracy and enabling precise irrigation management.
Implementation Method 1
a thermal image is acquired substantially simultaneously... each capturing a target of interest... A component temperature is assigned to each of the surface temperature components by applying component masks to the registered thermal image
Implementation Method 2
ET is estimated based on the component temperatures using an energy balance model or other ET model
Data Source
AI summary
Method, apparatus, and computer program product are disclosed for estimating evapotranspiration (ET) using thermal and optical images. In some embodiments, a color image and a thermal image are acquired substantially simultaneously, each capturing a target of interest associated with an agricultural crop. Features, including color features and/or texture features, are extracted from the color image. The color image is segmented into surface temperature components based on the extracted features. The surface temperature components are selected from the group consisting of sunlit soil, sunlit residue, sunlit vegetation, sunlit snow, shaded soil, shaded residue, shaded vegetation, and shaded snow. The color image and the thermal image are co-registered to provide a registered thermal image. A component temperature is assigned to each of the surface temperature components by applying component masks to the registered thermal image. ET is estimated based on the component temperatures using an energy balance model or other ET model.