Onboard Kernel Processing Score Detection Using Crop Imaging
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Solution Overview
Problem
Determining the kernel processing score (KPS) for harvested crop material is complex, costly, and time-consuming, typically requiring laboratory analysis, which delays further processing and adds financial disadvantage for farmers and breeders, and existing onboard methods suffer from inaccuracies in identifying small kernel particles.
Innovation Solution
A method using an optical sensor and image processing system on an agricultural machine to capture and analyze image data, generate a histogram, apply mathematical functions, and determine KPS in real-time or near real-time, enhancing accuracy by identifying and classifying kernel particles and accounting for machine and crop properties.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If laboratory analysis is used to determine KPS, then measurement precision is improved, but loss of time increases
Solution Approach 1:
The patent replaces the mechanical laboratory analysis system with an optical sensing and image processing system. The optical sensor captures images of kernel particles, and an image processing system analyzes these images to determine KPS values, eliminating the need for physical laboratory testing while maintaining measurement accuracy.
Solution Approach 2:
The patent creates optical copies (images) of the kernel particles using an optical sensor. These image copies are then processed digitally to determine KPS values, allowing for rapid analysis without the time-consuming physical handling and processing required in traditional laboratory methods.
2Productivity
If machine learning is used to analyze images of processed kernels, then productivity is improved, but measurement precision deteriorates
Solution Approach 1:
The patent segments the image analysis process into distinct stages: capturing images of individual kernel particles, identifying kernel particles versus other material, measuring particle sizes, and classifying particles by size category. This segmentation allows each stage to be optimized independently, maintaining both speed and accuracy.
Solution Approach 2:
The patent introduces an intermediary histogram analysis step between image capture and final KPS determination. The histogram represents the distribution of kernel particle sizes and serves as an intermediate data structure that preserves measurement precision while enabling efficient processing and accurate KPS calculation.
3Measurement precision
If small kernel particles are analyzed in images, then measurement precision is improved, but device complexity increases
Solution Approach 1:
The patent addresses the difficulty of detecting small particles by analyzing images from multiple dimensions and angles. The optical sensor captures images that reveal particle size, shape, and orientation, allowing the system to distinguish small kernel particles from other small debris through multi-dimensional feature analysis rather than relying on a single measurement parameter.
Data Source
AI summary
A method for determining a kernel processing score (KPS) onboard of an agricultural machine. The agricultural machine includes a conveyor for transferring harvested crop material and an optical sensor. The optical sensor is configured to generate image data of the harvested crop material transferred by and/or within the conveyor. An image processing system is configured for processing the generated image data to determine a kernel processing score (KPS).