Onboard Kernel Processing Score Determination Using Particle Histograms
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Solution Overview
Problem
Determining the kernel processing score (KPS) during harvesting and processing of crop material on agricultural machines is complex, costly, and time-consuming, with existing methods suffering from inaccuracies in identifying small kernel particles and foreign matter, leading to delayed and less accurate KPS values.
Innovation Solution
A method using an optical sensor and image processing system to capture and analyze crop material, generate histograms, and apply mathematical functions to determine KPS values onboard, enhancing accuracy and enabling real-time feedback for improved feed quality.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If KPS determination is performed using conventional laboratory methods, then measurement precision is improved, but loss of time increases significantly
Solution Approach 1:
The patent replaces the mechanical/physical laboratory analysis system with an optical sensing and image processing system. Optical sensors capture images of crop material, and image processing algorithms automatically analyze kernel particle sizes and calculate KPS values, eliminating the need for time-consuming physical sieving and laboratory procedures while maintaining measurement accuracy.
Solution Approach 2:
The patent creates a digital copy (image) of the physical crop material using optical sensors. These images serve as replicas that can be analyzed computationally to determine KPS values, allowing multiple measurements and analyses to be performed rapidly without physically handling or transporting the actual crop material to a laboratory.
2Loss of time
If onboard KPS determination is implemented using image processing, then loss of time is reduced, but measurement precision deteriorates due to inability to recognize small kernel particles
Solution Approach 1:
The patent transitions from two-dimensional image data to three-dimensional particle characterization by analyzing pixel distributions, areas, and shapes across multiple image dimensions. This dimensional transformation enables the system to distinguish small kernel particles from foreign matter and crop residue by examining their spatial relationships and morphological features in multiple dimensions, improving detection accuracy for particles that would be difficult to identify in single-plane images.
Solution Approach 2:
The patent employs multiple parameter thresholds and criteria for particle identification, including area thresholds, shape factors, and size distribution parameters. By dynamically adjusting and analyzing multiple parameters simultaneously, the system can accurately identify small kernel particles and differentiate them from other materials, maintaining measurement precision while enabling rapid onboard analysis.
3Productivity
If image processing is used to identify small kernel particles, then productivity is improved, but device complexity increases
Solution Approach 1:
The patent divides the complex task of KPS determination into distinct processing stages: image capture, particle detection, size measurement, histogram generation, and KPS calculation. Each stage is handled by specialized software modules that process specific aspects of the data independently, improving processing efficiency and reducing overall system complexity through functional decomposition.
Solution Approach 2:
The patent implements a multi-functional image processing system that performs multiple tasks using the same hardware platform. The optical sensing system captures images for both quality control and KPS determination, while the image processing software simultaneously identifies particles, measures sizes, generates histograms, and calculates KPS values, maximizing the utility of each component and reducing the need for separate specialized devices.
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
The method provides accurate, real-time KPS determination, allowing for enhanced automation and control of feed processing, resulting in higher quality feed and economic benefits for farmers.
Implementation Method 1
an optical sensor wherein the optical sensor is configured to generate image data of the harvested crop material
Data Source
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AI summary
Method for determining a kernel processing score (KPS) onboard of an agricultural machine (20), the agricultural machine (20) comprising a conveyor means (23) for transferring harvested crop material (15) and an optical sensor (21), wherein the optical sensor (21) is configured to generate image data of the harvested crop material (15) transferred by and/or within the conveyor means (23), an image processing system configured for processing the generated image data to determine a kernel processing score (KPS), wherein the method comprises the steps of: a) receiving image data from the optical sensor (21); b) identifying at least parts of kernel particles and a quantity thereof within the received image data; c) determining a size or surface of each identified kernel particle in said image data; d) generating a histogram based on the detected size or surface of the identified kernel particles and their quantities; e) analysing the histogram by applying a mathematical function to the generated histogram to increase the informative value of the image data; and f) determining the KPS based on the analysing step.