ArticleFrontiers in plant science2019
In-Field Detection and Quantification of
Article in Frontiers in plant science, 2019. 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.
- High-Resolution Leaf Image Sequences with Geometric Alignment for Dynamic Phenotyping of Foliar Diseases.Scientific data · 2026Article
- Optical coherence tomography for early detection of crop infection.Plant methods · 2025Article
- The FIP 1.0 Data Set: Highly resolved annotated image time series of 4,000 wheat plots grown in 6 years.GigaScience · 2025Article
- Temporal resolution trumps spectral resolution in UAV-based monitoring of cereal senescence dynamics.Plant methods · 2024Article
- SYMPATHIQUE: image-based tracking of symptoms and monitoring of pathogenesis to decompose quantitative disease resistance in the field.Plant methods · 2024Article
- SeptoSympto: a precise image analysis of Septoria tritici blotch disease symptoms using deep learning methods on scanned images.Plant methods · 2024Article
- Genome-wide association study of lipase and esterase in wholegrain wheat flour (Triticum aestivum L.).PloS one · 2023Article
- Combining High-Resolution Imaging, Deep Learning, and Dynamic Modeling to Separate Disease and Senescence in Wheat Canopies.Plant phenomics (Washington, D.C.) · 2023Article
- To What Extent Does Yellow Rust Infestation Affect Remotely Sensed Nitrogen Status?Plant phenomics (Washington, D.C.) · 2023Article
- In-Field Wheat Reflectance: How to Reach the Organ Scale?Sensors (Basel, Switzerland) · 2022Article
- Predicting yellow rust in wheat breeding trials by proximal phenotyping and machine learning.Plant methods · 2022Article
- Omics for the Improvement of Abiotic, Biotic, and Agronomic Traits in Major Cereal Crops: Applications, Challenges, and Prospects.Plants (Basel, Switzerland) · 2021Review
- Field Phenomics: Will It Enable Crop Improvement?Plant phenomics (Washington, D.C.) · 2021Review
Corrections and comments
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Hyperspectral remote sensing holds the potential to detect and quantify crop diseases in a rapid and non-invasive manner. Such tools could greatly benefit resistance breeding, but their adoption is hampered by i) a lack of specificity to disease-related effects and ii) insufficient robustness to variation in reflectance caused by genotypic diversity and varying environmental conditions, which are fundamental elements of resistance breeding. We hypothesized that relying exclusively on temporal changes in canopy reflectance during pathogenesis may allow to specifically detect and quantify crop diseases while minimizing the confounding effects of genotype and environment. To test this hypothesis, we collected time-resolved canopy hyperspectral reflectance data for 18 diverse genotypes on infected and disease-free plots and engineered spectral-temporal features representing this hypothesis. Our results confirm the lack of specificity and robustness of disease assessments based on reflectance spectra at individual time points. We show that changes in spectral reflectance over time are indicative of the presence and severity of
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