Summary of Hyperspectral Imaging-based Grain Quality Assessment with Limited Labelled Data, by Priyabrata Karmakar et al.
Hyperspectral Imaging-Based Grain Quality Assessment With Limited Labelled Data
by Priyabrata Karmakar, Manzur Murshed, Shyh Wei Teng
First submitted to arxiv on: 17 Nov 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary A recent trend in agricultural research involves assessing grain quality using hyperspectral imaging (HSI). However, training deep convolutional neural network (DCNN)-based classifiers for HSI data is challenging due to the lack of sufficient labelled samples. This paper presents a novel approach that combines HSI with few-shot learning (FSL) techniques to overcome this limitation. Traditional methods for grain quality evaluation are invasive and time-consuming, making HSI a promising non-invasive alternative. The proposed FSL-based approach enables models to perform well with limited labelled data, making it suitable for real-world applications where rapid deployment is required. The paper evaluates the performance of few-shot classifiers in two scenarios: classifying seen grain types during training and generalizing to unseen grain types. The results show that despite using very limited labelled data, FSL classifiers achieve comparable accuracy to fully trained classifiers. This work contributes to the development of efficient and robust HSI-based grain quality assessment systems. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Imagine being able to quickly check the quality of grains without having to touch or examine them closely. That’s what this research is all about! Currently, people use methods that are time-consuming and invasive to evaluate grain quality, but a new way using special imaging technology called hyperspectral imaging (HSI) could be a game-changer. The problem is that training computers to analyze HSI data requires lots of labelled examples, which can be hard to come by. To solve this issue, the researchers came up with an innovative approach that uses a technique called few-shot learning (FSL). This method allows computers to learn from very little labelled data, making it perfect for real-world applications where speed and efficiency matter. The scientists tested their approach on two scenarios: classifying familiar grain types and identifying new ones they hadn’t seen before. Their results show that even with limited training data, the FSL-based approach can be just as accurate as traditional methods. |
Keywords
» Artificial intelligence » Few shot » Neural network
