Self-supervised Fusarium Head Blight Detection with Hyperspectral Image and Feature Mining

TL;DR

This paper introduces a self-supervised hyperspectral image analysis method for detecting Fusarium Head Blight in crops, leveraging feature mining and spectral band selection to improve practicality and accuracy.

Contribution

It presents a novel unsupervised classification approach using endmember extraction and top-K band selection for FHB detection in hyperspectral images.

Findings

Effective validation in AI for Agriculture Challenge 2024

No need for expensive devices or complex algorithms

Source code is publicly available

Abstract

Fusarium Head Blight (FHB) is a serious fungal disease affecting wheat (including durum), barley, oats, other small cereal grains, and corn. Effective monitoring and accurate detection of FHB are crucial to ensuring stable and reliable food security. Traditionally, trained agronomists and surveyors perform manual identification, a method that is labor-intensive, impractical, and challenging to scale. With the advancement of deep learning and Hyper-spectral Imaging (HSI) and Remote Sensing (RS) technologies, employing deep learning, particularly Convolutional Neural Networks (CNNs), has emerged as a promising solution. Notably, wheat infected with serious FHB may exhibit significant differences on the spectral compared to mild FHB one, which is particularly advantageous for hyperspectral image-based methods. In this study, we propose a self-unsupervised classification method based on HSI endmember extraction strategy and top-K bands selection, designed to analyze material signatures in HSIs to derive discriminative feature representations. This approach does not require expensive device or complicate algorithm design, making it more suitable for practical uses. Our method has been effectively validated in the Beyond Visible Spectrum: AI for Agriculture Challenge 2024. The source code is easy to reproduce and available at {https://github.com/VanLinLin/Automated-Crop-Disease-Diagnosis-from-Hyperspectral-Imagery-3rd}.