# Hyperspectral Imaging-Based Grain Quality Assessment With Limited Labelled Data

arXiv: https://arxiv.org/abs/2411.10924 · HTML: https://mlsanity.com/paper/2411.10924/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 16 of its 64 references that are in the map.
- First posted on arXiv 17 Nov 2024.

## Builds on (most cited first, 16 of 16)

- [Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks](https://mlsanity.com/paper/1703.03400/) (2017)
- [Squeeze-and-Excitation Networks](https://mlsanity.com/paper/1709.01507/) (2017)
- [Prototypical Networks for Few-shot Learning](https://mlsanity.com/paper/1703.05175/) (2017)
- [Matching Networks for One Shot Learning](https://mlsanity.com/paper/1606.04080/) (2016)
- [Learning to Compare: Relation Network for Few-Shot Learning](https://mlsanity.com/paper/1711.06025/) (2017)
- [Learning from Few Examples: A Summary of Approaches to Few-Shot Learning](https://mlsanity.com/paper/2203.04291/) (2022)
- [HyperSIGMA: Hyperspectral Intelligence Comprehension Foundation Model](https://mlsanity.com/paper/2406.11519/) (2024)
- [Diversity with Cooperation: Ensemble Methods for Few-Shot Classification](https://mlsanity.com/paper/1903.11341/) (2019)
- [Motion Deblurring with Real Events](https://mlsanity.com/paper/2109.13695/) (2021)
- [A Closer Look at Few-shot Classification Again](https://mlsanity.com/paper/2301.12246/) (2023)
- [Deep Metric Learning for Few-Shot Image Classification: A Review of Recent Developments](https://mlsanity.com/paper/2105.08149/) (2021)
- [A laboratory-created dataset with ground-truth for hyperspectral unmixing evaluation](https://mlsanity.com/paper/1902.08347/) (2019)
- [Adaptive Parametric Prototype Learning for Cross-Domain Few-Shot Classification](https://mlsanity.com/paper/2309.01342/) (2023)
- [Unlocking the capabilities of explainable fewshot learning in remote sensing](https://mlsanity.com/paper/2310.08619/) (2023)
- [Transfer Learning for Segmenting Dimensionally-Reduced Hyperspectral Images](https://mlsanity.com/paper/1906.09631/) (2019)
- [A Guide to Employ Hyperspectral Imaging for Assessing Wheat Quality at Different Stages of Supply Chain in Australia: A Review](https://mlsanity.com/paper/2209.05727/) (2022)

## Abstract (arXiv)

Recently hyperspectral imaging (HSI)-based grain quality assessment has gained research attention. However, unlike other imaging modalities, HSI data lacks sufficient labelled samples required to effectively train deep convolutional neural network (DCNN)-based classifiers. In this paper, we present a novel approach to grain quality assessment using HSI combined with few-shot learning (FSL) techniques. Traditional methods for grain quality evaluation, while reliable, are invasive, time-consuming, and costly. HSI offers a non-invasive, real-time alternative by capturing both spatial and spectral information. However, a significant challenge in applying DCNNs for HSI-based grain classification is the need for large labelled databases, which are often difficult to obtain. To address this, we explore the use of FSL, which enables models to perform well with limited labelled data, making it a practical solution for real-world applications where rapid deployment is required. We also explored the application of FSL for the classification of hyperspectral images of bulk grains to enable rapid quality assessment at various receival points in the grain supply chain. We evaluated the performance of few-shot classifiers in two scenarios: first, classification of grain types seen during training, and second, generalisation to unseen grain types, a crucial feature for real-world applications. In the first scenario, we introduce a novel approach using pre-computed collective class prototypes (CCPs) to enhance inference efficiency and robustness. In the second scenario, we assess the model's ability to classify novel grain types using limited support examples. Our experimental results show that despite using very limited labelled data for training, our FSL classifiers accuracy is comparable to that of a fully trained classifier trained using a significantly larger labelled database.