X-Ray Seed Imaging And Segmentation For Germination Prediction
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Solution Overview
Problem
Current methods for assessing seed germination potential are invasive, labor-intensive, and time-consuming, and rely heavily on morphological descriptors, missing important information and requiring significant manual interaction, leading to inaccurate and inefficient seed classification.
Innovation Solution
Implementing a system that uses x-ray imaging and machine learning models for automated image segmentation and classification of seeds, leveraging deep learning algorithms to determine germination potential based on characteristic labels, with minimal user intervention.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual sorting and classification is used, then easily determined physical differences can be identified, but the process is time-consuming and error-prone
Solution Approach 1:
The patent replaces manual mechanical sorting with an automated imaging system that captures images of seeds and uses machine learning algorithms to classify them. The system uses cameras to acquire seed images, processes them through deep learning models to identify characteristics, and automatically determines germination potential, eliminating the need for manual visual inspection and sorting operations.
Solution Approach 2:
The patent creates digital copies of seeds through imaging rather than physically handling each seed. By capturing images of seeds and analyzing these visual representations through machine learning, the system can classify thousands of seeds without physical contact, thereby reducing time and human error while maintaining classification accuracy.
2Loss of information
If manual sorting is used, then physical differences are observable, but invisible features of seeds are not taken into account
Solution Approach 1:
The patent replaces manual visual inspection with an automated imaging and analysis system that can detect both visible and invisible seed features. The machine learning model processes image data to identify characteristics such as embryo presence, seed integrity, and other features that are not easily observable to the human eye, thereby preventing information loss.
Solution Approach 2:
The patent introduces an intermediary processing layer between the physical seed and the classification decision. The imaging system captures visual data, the machine learning model analyzes both obvious and subtle features from the images, and only then does the system determine germination potential. This intermediary digital analysis layer ensures that invisible features are not overlooked.
3Reliability
If traditional germination tests are used, then direct assessment is possible, but the tests are invasive and time-consuming
Solution Approach 1:
The patent performs preliminary visual assessment through imaging before any actual germination testing occurs. By capturing images of seeds and using machine learning to predict germination potential, the system can identify high-quality seeds in advance without needing to wait for actual germination to occur. This preliminary digital assessment eliminates the need for time-consuming physical germination tests while maintaining reliability.
Solution Approach 2:
The patent uses digital copies of seeds in the form of images to assess germination potential rather than conducting physical germination tests on actual seeds. The machine learning model analyzes image data to predict whether seeds will germinate, providing reliable assessment results without the time consumption and invasiveness of traditional germination testing procedures.
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
Enables accurate and efficient prediction of seed germination potential, reducing time and labor, and allowing for classification across various seed types with high throughput and minimal resource requirements.
Implementation Method 1
receiving an image comprising a plurality of seeds, and segmenting the image to identify instance masks associated with the plurality of seeds... the image may be an x-ray image
Data Source
AI summary
Systems/methods for predicting the germination potential of seeds are disclosed. The methods include, by a processor: receiving an image comprising a plurality of seeds, and segmenting the image to identify instance masks associated with the plurality of seeds. For each of the plurality of instance masks, the methods include determining one or more of a plurality of characteristic labels, and determining based on the one or more of the plurality of characteristic labels, a germination potential of a seed associated with that instance mask.