ArticleSensors (Basel, Switzerland)2020
Development and Evaluation of a New Spectral Disease Index to Detect Wheat Fusarium Head Blight Using Hyperspectral Imaging.
Article in Sensors (Basel, Switzerland), 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.
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Who cites it
13 citing papers in PubMed, 40 citations in OpenAlex.
- Hyperspectral Imaging for Agricultural and Food Sensing: Applications, Data Analytics, and Deployment Challenges.Foods (Basel, Switzerland) · 2026Review
- Hyperspectral Imaging and Machine Learning for Diagnosing Rice Bacterial Blight Symptoms Caused byPlants (Basel, Switzerland) · 2025Article
- Article
- Wheat Fusarium Head Blight Automatic Non-Destructive Detection Based on Multi-Scale Imaging: A Technical Perspective.Plants (Basel, Switzerland) · 2024Review
- Hyperspectral Imaging and Selected Biological Control Agents for the Management of Fusarium Head Blight in Spring Wheat.Plants (Basel, Switzerland) · 2023Article
- Development of Methods for Remote Monitoring of Leaf Diseases in Wheat Agrocenoses.Plants (Basel, Switzerland) · 2023Article
- Hyperspectral imaging-based classification of rice leaf blast severity over multiple growth stages.Plant methods · 2022Article
- Current State of Hyperspectral Remote Sensing for Early Plant Disease Detection: A Review.Sensors (Basel, Switzerland) · 2022Review
- A non-destructive testing method for early detection of ginseng root diseases using machine learning technologies based on leaf hyperspectral reflectance.Frontiers in plant science · 2022Article
- Novel Vegetation Indices to Identify Broccoli Plants Infected WithFrontiers in plant science · 2022Article
- Detection of wheatFrontiers in plant science · 2022Article
- Early Detection of Plant Viral Disease Using Hyperspectral Imaging and Deep Learning.Sensors (Basel, Switzerland) · 2021Article
- Hyperspectral Imaging Tera Hertz System for Soil Analysis: Initial Results.Sensors (Basel, Switzerland) · 2020Article
Corrections and comments
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Authors and funding
6 authors at 2 institutions in 1 country.
Funding
Abstract
Fusarium head blight (FHB) is a major disease threatening worldwide wheat production. FHB is a short cycle disease and is highly destructive under conducive environments. To provide technical support for the rapid detection of the FHB disease, we proposed to develop a new Fusarium disease index (FDI) based on the spectral data of 374-1050 nm. This study was conducted through the analysis of reflectance spectral data of healthy and diseased wheat ears at the flowering and filling stages by hyperspectral imaging technology and the random forest method. The characteristic wavelengths selected were 570 nm and 678 nm for the late flowering stage, 565 nm and 661 nm for the early filling stage, 560 nm and 663 nm for the combined stage (combining both flowering and filling stages) by random forest. FDI at each stage was derived from the wavebands of each corresponding stage. Compared with other 16 existing spectral indices, FDI demonstrated a stronger ability to determine the severity of the FHB disease. Its determination coefficients (
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Registered trials
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