X-ray CT Detection of Internal Onion Damage
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Solution Overview
Problem
Current methods for evaluating onion bulb quality, particularly for Vidalia sweet onions, are inadequate in detecting internal damage and diseases such as Botrytis, Burkholderia cepacia, and Pseudomonas viridiflava infections, leading to significant economic losses due to undetected issues that can spread in controlled atmosphere storage.
Innovation Solution
A CT imaging method that involves acquiring and processing CT images of onion bulbs to identify and classify internal voids, shape, and disease progression, using image analysis techniques to determine the quality and marketability of onions, thereby detecting internal damage and diseases non-destructively.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If human visual inspection is used to screen onions for disease or damage, then the inspection process is simple and fast, but internal damage and latent infections cannot be detected
Solution Approach 1:
The patent replaces the mechanical human visual inspection system with an X-ray imaging system that uses electromagnetic radiation to penetrate and visualize internal onion structures. This substitution enables detection of internal damage, voids, and latent infections that are invisible to human eyes, directly resolving the contradiction between operational simplicity and detection accuracy.
2Measurement precision
If CT imaging is used to detect internal damage and diseases, then detection accuracy is improved, but device complexity increases
Solution Approach 1:
The patent segments the complex CT imaging process into distinct functional components: X-ray source, detector array, rotation mechanism, and image reconstruction system. This segmentation allows each component to be optimized independently and facilitates modular implementation, reducing overall system complexity while maintaining high detection accuracy for internal onion defects.
3Productivity
If human inspection is used to sort onions by quality, then sorting is fast and simple, but internal quality factors such as bulb shape and consistency cannot be evaluated
Solution Approach 1:
The patent introduces CT imaging as an intermediary between onion harvesting and final sorting decisions. The imaging system captures comprehensive internal and external quality data, which is then processed by image analysis algorithms to generate quality assessments. This intermediary step enables both rapid automated sorting and detailed evaluation of internal quality factors like bulb shape, consistency, and hidden defects, simultaneously improving productivity and information completeness.
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
This method enables accurate and non-destructive detection of internal damage and diseases in onion bulbs, improving the selection of high-quality onions for storage and reducing post-harvest losses by identifying latent infections and defects that human inspection may miss.
Implementation Method 1
X-ray computed tomography scanning
Data Source
AI summary
The present disclosure encompasses embodiments of X-ray computed tomography-based methods for the detection of onion quality factors. Such methods are advantageous in detecting internal damage to onion bulbs due to bacterial and fungal rots and mechanical damage while also providing for the overall assessment of onion bulb quality and market value. Because CT images provide cross-sectional reconstructions of the subject under study, CT scans of onion bulbs can be used not only to detect damage from disease, but also cuts and bruises that increase an onion bulb's susceptibility to disease, and the presence of shoots or seed stems and overall shape of the bulbs.