ArticleSensors (Basel, Switzerland)2021
Monitoring Wheat Powdery Mildew Based on Hyperspectral, Thermal Infrared, and RGB Image Data Fusion.
Article in Sensors (Basel, Switzerland), 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.
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14 citing papers in PubMed, 70 citations in OpenAlex.
- Weakly Supervised Fine-Grained Discrimination of Wheat Mold Using Local RGB-HSI Fusion.Foods (Basel, Switzerland) · 2026Article
- Predicting yellow mosaic disease severity in yardlong bean using visible imaging coupled with machine learning model.Scientific reports · 2025Article
- An intelligent identification for pest and disease detection in wheat leaf based on environmental data using multimodal data fusion.Frontiers in plant science · 2025Article
- Intelligent agriculture: deep learning in UAV-based remote sensing imagery for crop diseases and pests detection.Frontiers in plant science · 2024Review
- Plant disease recognition datasets in the age of deep learning: challenges and opportunities.Frontiers in plant science · 2024Article
- Monitoring of drought stress and transpiration rate using proximal thermal and hyperspectral imaging in an indoor automated plant phenotyping platform.Plant methods · 2023Article
- Combining biophysical parameters with thermal and RGB indices using machine learning models for predicting yield in yellow rust affected wheat crop.Scientific reports · 2023Article
- Silencing of the calcium-dependent protein kinase TaCDPK27 improves wheat resistance to powdery mildew.BMC plant biology · 2023Article
- Detecting Grapevine Virus Infections in Red and White Winegrape Canopies Using Proximal Hyperspectral Sensing.Sensors (Basel, Switzerland) · 2023Article
- Food Image Segmentation Using Multi-Modal Imaging Sensors with Color and Thermal Data.Sensors (Basel, Switzerland) · 2023Article
- Deep learning for plant bioinformatics: an explainable gradient-based approach for disease detection.Frontiers in plant science · 2023Article
- Methodologies Used in Remote Sensing Data Analysis and Remote Sensors for Precision Agriculture.Sensors (Basel, Switzerland) · 2022Article
- Study on the Classification Method of Rice Leaf Blast Levels Based on Fusion Features and Adaptive-Weight Immune Particle Swarm Optimization Extreme Learning Machine Algorithm.Frontiers in plant science · 2022Article
- End-to-End Fusion of Hyperspectral and Chlorophyll Fluorescence Imaging to Identify Rice Stresses.Plant phenomics (Washington, D.C.) · 2022Article
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Authors and funding
7 authors at 1 institution in 1 country.
Funding
Abstract
Powdery mildew severely affects wheat growth and yield; therefore, its effective monitoring is essential for the prevention and control of the disease and global food security. In the present study, a spectroradiometer and thermal infrared cameras were used to obtain hyperspectral signature and thermal infrared images data, and thermal infrared temperature parameters (TP) and texture features (TF) were extracted from the thermal infrared images and RGB images of wheat with powdery mildew, during the wheat flowering and filling periods. Based on the ten vegetation indices from the hyperspectral data (VI), TF and TP were integrated, and partial least square regression, random forest regression (RFR), and support vector machine regression (SVR) algorithms were used to construct a prediction model for a wheat powdery mildew disease index. According to the results, the prediction accuracy of RFR was higher than in other models, under both single data source modeling and multi-source data modeling; among the three data sources, VI was the most suitable for powdery mildew monitoring, followed by TP, and finally TF. The RFR model had stable performance in multi-source data fusion modeling (VI&TP&TF), and had the optimal estimation performance with 0.872 and 0.862 of R
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