ArticleFrontiers in plant science2025
Advanced hyperspectral image processing and machine learning approaches for early detection of wheat stem rust.
Article in Frontiers in plant science, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
3 citing papers in PubMed.
- Hyperspectral imaging-based early stress and disease detection for sustainable crop management.Planta · 2026Review
- Pre-symptomatic detection of wheat stem rust using hyperspectral imaging and deep learning.Frontiers in plant science · 2026Article
- Cross-Crop Transferability of Machine Learning Models for Early Stem Rust Detection in Wheat and Barley Using Hyperspectral Imaging.Plants (Basel, Switzerland) · 2025Article
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Hyperspectral remote sensing has shown great promise for early detection of plant diseases, yet its adoption is often hindered by spectral variability, noise, and distribution shifts across acquisition conditions. In this study, we present a systematic preprocessing pipeline tailored for hyperspectral data in plant disease detection, combining pixel-wise correction, curve-wise normalization and smoothing, and channel-wise standardization. The pipeline was evaluated on an experiment on early detection of stem rust (
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