Local Light-Spot Grain Detection for Empty Chaff and White Caps
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Solution Overview
Problem
Existing grain analysis systems, such as the grain cam in WO 2006/010761 A1, cannot distinguish between empty chaff particles and chaff particles still holding a grain kernel (white caps), leading to different countermeasures being necessary for each, which current systems cannot effectively differentiate.
Innovation Solution
A device with a local light source producing a light spot smaller than 5mm in diameter is used to illuminate the grain sample, allowing differentiation between empty chaff particles and white caps by analyzing the reflection patterns, with a controller processing the images to distinguish between the two.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a standard grain cam system is used to detect MOG content, then the relative amount of MOG or broken kernels can be determined, but the system cannot distinguish between empty chaff particles and white caps (chaff particles holding a grain kernel)
Solution Approach 1:
The illumination is segmented into multiple local light spots distributed across the grain sample, rather than using a single broad light source. This segmentation allows each light spot to interact with individual particles, creating distinct reflection patterns that enable differentiation between empty chaff and white caps based on their different optical properties
Solution Approach 2:
Different regions of the grain sample are illuminated with localized light spots of specific sizes (where the light spot diameter is smaller than 5mm). This local illumination creates quality differences in the reflected light based on the local composition (empty chaff vs. white caps), allowing the imaging sensor to detect these local variations and distinguish between the two particle types
2Productivity
If control settings are optimized to maximize grain yield, then the amount of harvested grain increases, but grain breakage and MOG content in the grain tank increase
Solution Approach 1:
The system provides real-time feedback by continuously monitoring the grain sample for empty chaff and white cap content using the localized illumination and imaging system. This feedback enables dynamic adjustment of combine harvester settings to maintain optimal performance while minimizing harmful effects like grain breakage and excessive MOG
Solution Approach 2:
The system enables dynamic adjustment of operational parameters (such as rotor speed, concave clearance, fan speed) based on detected white cap and empty chaff levels. By changing these parameters in response to real-time detection, the system optimizes the balance between grain yield and quality, reducing grain breakage and MOG content while maintaining high productivity
3Area of stationary object
If a broad light source is used to illuminate the grain sample, then the entire sample is visible, but empty chaff particles and white caps cannot be differentiated in the captured images
Solution Approach 1:
The broad illumination is divided into multiple discrete local light spots, each illuminating a specific region of the grain sample. This segmentation preserves the ability to see different parts of the sample while creating distinct reflection patterns that enable particle type differentiation
Solution Approach 2:
The system transitions from considering only the spatial coverage dimension (broad vs. narrow light) to adding the reflection pattern dimension. By analyzing the characteristics of reflected light from each local spot (such as intensity, distribution, and pattern), the system can differentiate particle types even with localized illumination, effectively adding a new dimension of information
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
Enables accurate differentiation between empty chaff particles and white caps, allowing for tailored adjustments to the combine harvester settings to minimize excess chaff or white caps, thereby optimizing grain yield and quality.
Implementation Method 1
When the local light spot hits an empty chaff particle, at least a portion of the light will pass through the chaff shell and reflect at an interior surface of the empty shell
Implementation Method 2
When the local light spot hits an empty chaff particle, at least a portion of the light will pass through the chaff shell
Implementation Method 3
the grain kernel absorbs the light that initially passes through the chaff shell. This absorption of light by the grain kernel prevents the light from traveling any deeper into the chaff particle
Data Source
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AI summary
A device (100) for analysing a grain sample comprising a light source (130, 140), an image sensor (120), and a controller (150). The light source (130, 140) is configured for illuminating the grain sample. The image sensor (120) is used for capturing images of the grain sample. The (150) controller is coupled to the image sensor (120) for receiving the images of the grain sample therefrom and configured to analyse the images to detect at least one material other than grain in the grain sample. The light source (140) is configured to illuminate the grain sample with a local light spot having a size that is smaller than a width of an average wheat kernel. The image analysis and the detection of material other than grain may, at least partly, be performed using trained neural networks and other artificial intelligence (Al) algorithms.