Corn Yield Estimation via Kernel Dimension Imaging
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Solution Overview
Problem
Current methods for estimating corn yield are inaccurate due to reliance on subjective assessments and circular logic, leading to potential biases and errors in kernel count conversions, especially with varying kernel sizes and growing conditions.
Innovation Solution
A method and system that processes digital images of corn ears to determine kernel dimensions, estimate average kernel volume, weight, and subsequently calculate kernels per bushel, using image processing techniques and calibration references to improve accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If fixed conversion factors (e.g., 90,000 kernels-per-bushel) are used for yield estimation, then the estimation process is simplified, but accuracy deteriorates due to variation in actual kernel sizes and growing conditions
Solution Approach 1:
The patent changes the parameter being measured from abstract growing condition categories to concrete kernel physical dimensions (length, width, depth). By measuring actual kernel size parameters and using them to calculate kernels-per-bushel, the system adapts to varying growing conditions rather than relying on fixed conversion factors, thereby resolving the contradiction between simplicity and accuracy.
2Ease of operation
If subjective assessments of growing conditions are used to estimate kernels-per-bushel, then the estimation process remains simple, but reliability deteriorates due to inherent bias and circular logic
Solution Approach 1:
The patent substitutes the mechanical/subjective process of visual assessment with an optical measurement system using digital imaging. The image processing system objectively measures kernel dimensions without human intervention, eliminating subjective bias and circular logic while maintaining operational simplicity through automated image analysis.
3Ease of operation
If ear length is used as a proxy for kernel count, then the measurement process is simplified, but accuracy deteriorates due to variation in potential kernel number under different growing conditions
Solution Approach 1:
The patent segments the measurement process into distinct components: measuring ear length, counting actual kernels, and measuring individual kernel dimensions. By separating these measurements and using kernel dimension data to calculate actual kernel count rather than relying solely on ear length proxy, the system improves accuracy while maintaining simplicity through systematic data collection.
Data Source
AI summary
A method for generating a yield estimate for a crop of corn includes capturing a digital image of an ear of corn; processing the digital image of an ear of corn to determine a plurality of dimensions for each of a plurality of kernels on the ear of corn; estimating, from the plurality of dimensions, an average kernel volume for the ear of corn; determining, from the average kernel volume and an estimated kernel density, an average kernel weight for the ear of corn; and estimating, from the average kernel weight, a kernels-per-bushel for the ear of corn.