RGB-to-Hyperspectral Imaging for Full-Range Crop Sensing
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Solution Overview
Problem
Current imaging technologies face limitations in transforming RGB images to hyperspectral data beyond 700 nm, leading to high costs, bulky hardware, and insufficient spatial, spectral, and temporal resolution, which hinders precise crop parameter measurement and agricultural decision-making.
Innovation Solution
A method and system that utilizes artificial intelligence to extract full-range hyperspectral data from RGB images through low-cost, high-resolution ground-based devices, employing optical flow models and pre-trained datasets to convert RGB images to hyperspectral data, including crop phenotyping and health parameter mapping.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If hyperspectral devices are used to capture full-range hyperspectral data, then measurement precision and spectral information are improved, but device complexity, cost and bulkiness increase
Solution Approach 1:
The patent creates a computational copy of hyperspectral data from RGB images using deep learning models. Instead of using physical hyperspectral sensors, the system trains neural networks to map RGB image data to hyperspectral signatures, effectively copying the spectral information through software rather than hardware.
Solution Approach 2:
The patent replaces the mechanical/optical hyperspectral imaging system with a computational approach. Deep learning models substitute for physical spectrometers, using AI algorithms to infer spectral characteristics from standard RGB camera data, thereby eliminating complex mechanical scanning systems.
2Measurement precision
If hyperspectral devices are used to capture full-range hyperspectral data, then measurement precision is improved, but cost increases
Solution Approach 1:
The patent uses inexpensive RGB camera sensors instead of expensive hyperspectral sensors. The approach treats spectral analysis as a computational problem solvable with cheap consumer-grade cameras and software, rather than requiring costly specialized hardware.
Solution Approach 2:
The system creates spectral information computationally from廉价的RGB images, copying the value of hyperspectral data without the hyperspectral hardware price tag.
3Device complexity
If RGB images are used instead of hyperspectral images, then device complexity and cost are reduced, but measurement precision and spectral information are lost
Solution Approach 1:
The patent transforms the parameter space by using deep learning to map from RGB parameter space (3 channels) to hyperspectral parameter space (multiple spectral bands). The neural network learns the transformation parameters that convert simple RGB values into rich spectral signatures.
Solution Approach 2:
The deep learning model acts as an intermediary that bridges RGB images and hyperspectral data. The trained network serves as a computational mediator that translates between the two data types, enabling spectral analysis without direct spectral measurement.
4Device complexity
If conventional RGB to hyperspectral transformation is used, then processing simplicity is improved, but spectral accuracy beyond 700 nm is worsened
Solution Approach 1:
The patent performs preliminary training of deep learning models using paired RGB-hyperspectral datasets before actual spectral extraction. This pre-training phase allows the model to learn accurate mappings including spectral regions beyond 700 nm, which are then applied during operational use.
Solution Approach 2:
The trained deep learning model serves as an intelligent intermediary that extends spectral information beyond the physical limitations of RGB sensors, particularly in the 700 nm and above range where RGB cameras have no direct sensitivity.
Data Source
AI summary
A system and method for extracting a full range hyperspectral data from one or more RGB images. The method encompasses pre-processing, the one or more RGB images. Further the method encompasses estimating, an illumination component associated with each pre-processed RGB image. The method thereafter comprises removing, the illumination component from the each pre-processed RGB image. Further the method encompasses tracking, a trajectory of pixel(s) over frame(s) associated with the each pre-processed RGB image. The method then leads to identifying, a position of the pixel(s) in one or more adjacent frames of the frame(s) based on a patch defined around said one or more pixels. Thereafter the method encompasses extracting, the full range hyperspectral data from the each pre-processed RGB image based on at least one of the removal of the illumination component, the trajectory of the pixel(s) and the position of the pixel(s).