Crop Image Analysis for Real-Time Leaf-to-Stem Measurement
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Solution Overview
Problem
Conventional methods for measuring leaf-to-stem ratio in crops are manual and time-consuming, limiting their application in real-time agricultural processes such as bale quality determination and dry down period prediction.
Innovation Solution
An automated system using an imaging device and computing device to analyze crop images, segment leaves and stems, and determine the leaf-to-stem ratio on-the-go, facilitating real-time nutritional value assessment and dry down period prediction.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual methods are used to measure leaf-to-stem ratio, then measurement accuracy can be maintained, but measurement time and labor requirements increase significantly
Solution Approach 1:
The patent replaces manual mechanical measurement methods with an automated image processing system. The system captures images of crop samples and uses computational algorithms to automatically segment and quantify leaf and stem portions, eliminating the need for manual visual assessment and measurement while maintaining or improving measurement accuracy.
Solution Approach 2:
The patent creates digital copies (images) of the crop samples and analyzes these copies computationally. By working with image data rather than physically measuring the actual samples, the system enables rapid, non-contact measurement of leaf-to-stem ratios without handling or damaging the biological material.
2Productivity
If manual measurement methods are used, then simple equipment is required, but productivity and real-time analysis capability are limited
Solution Approach 1:
The patent integrates multiple functions into a single automated system: image capture, image processing, leaf-stem segmentation, ratio calculation, and data output. This multi-functional approach increases productivity by eliminating manual steps while the modular design keeps device complexity manageable.
Solution Approach 2:
The system performs self-service through automated image processing algorithms that independently analyze crop images without human intervention. The computational models automatically segment leaves from stems and calculate ratios, enabling high-throughput measurement while reducing the need for complex manual measurement protocols.
3Speed
If automated image processing is implemented, then measurement speed increases, but measurement precision may be affected by image quality and processing algorithm accuracy
Solution Approach 1:
The patent incorporates feedback mechanisms where the image processing system iteratively refines its segmentation and measurement based on image quality assessment. The system can detect and correct processing errors, adjust algorithm parameters based on image characteristics, and validate measurements against expected ranges to maintain precision while operating at high speed.
Solution Approach 2:
The system dynamically adjusts processing parameters based on image characteristics such as lighting conditions, resolution, and crop maturity stage. By adapting algorithm parameters to match the specific imaging conditions, the system maintains measurement precision across varying operational conditions while preserving high measurement speed.
Data Source
AI summary
Systems and methods are provided for determining a leaf-to-stem ratio of a crop automatically from an image of the crop. An agricultural vehicle may include a crop analysis system having a computing device and an imaging device for capturing an image of a crop. The computing device analyzes images acquired by the imaging determine to determine a leaf-to-stem ratio of the crop.