Hyperspectral Imaging-Based Grain Quality Assessment With Limited Labelled Data

TL;DR

This paper introduces a few-shot learning approach for hyperspectral imaging-based grain quality assessment, enabling accurate classification with limited labeled data, thus offering a non-invasive, rapid, and cost-effective alternative to traditional methods.

Contribution

The paper presents a novel few-shot learning framework with collective class prototypes for hyperspectral grain classification, improving efficiency and generalization to unseen grain types.

Findings

FSL classifiers achieve accuracy comparable to fully trained models with less data.

Proposed CCP method enhances inference efficiency and robustness.

Model generalizes well to unseen grain types with limited support examples.

Abstract

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.