ArticleFrontiers in plant science2018
Proximal Phenotyping and Machine Learning Methods to Identify Septoria Tritici Blotch Disease Symptoms in Wheat.
Article in Frontiers in plant science, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.
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Who cites it
15 citing papers in PubMed.
- Monitoring wheat leaf rust severity using machine learning techniques.Scientific reports · 2025Article
- Genomic Loci for Sclerotinia Stem Rot Resistance and Chlorophyll Stability inPlant-environment interactions (Hoboken, N.J.) · 2025Article
- Optical coherence tomography for early detection of crop infection.Plant methods · 2025Article
- Plant genotype-specific modulation of Clonostachys rosea-mediated biocontrol of septoria tritici blotch disease in wheat.BMC plant biology · 2025Article
- Article
- Study of the Spectral Characteristics of Crops of Winter Wheat Varieties Infected with Pathogens of Leaf Diseases.Plants (Basel, Switzerland) · 2024Article
- Functional phenomics for improved climate resilience in Nordic agriculture.Journal of experimental botany · 2022Review
- Predicting yellow rust in wheat breeding trials by proximal phenotyping and machine learning.Plant methods · 2022Article
- Status and advances in mining for blackleg (Leptosphaeria maculans) quantitative resistance (QR) in oilseed rape (Brassica napus).TAG. Theoretical and applied genetics. Theoretische und angewandte Genetik · 2021Review
- Proximal Hyperspectral Imaging Detects Diurnal and Drought-Induced Changes in Maize Physiology.Frontiers in plant science · 2021Article
- Using Rapid Chlorophyll Fluorescence Transients to ClassifyPlants (Basel, Switzerland) · 2020Article
- GWAS-Assisted Genomic Prediction to Predict Resistance to Septoria Tritici Blotch in Nordic Winter Wheat at Seedling Stage.Frontiers in genetics · 2019Article
- In-Field Detection and Quantification ofFrontiers in plant science · 2019Article
- Article
- Identification and Analysis of Rice Yield-Related Candidate Genes by Walking on the Functional Network.Frontiers in plant science · 2018Article
Corrections and comments
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Phenotyping with proximal sensors allow high-precision measurements of plant traits both in the controlled conditions and in the field. In this work, using machine learning, an integrated analysis was done from the data obtained from spectroradiometer, infrared thermometer, and chlorophyll fluorescence measurements to identify most predictive proxy measurements for studying Septoria tritici blotch (STB) disease of wheat. The random forest (RF) models for chlorosis and necrosis identified photosystem II quantum yield (QY) and vegetative indices (VIs) associated with the biochemical composition of leaves as the top predictive variables for identifying disease symptoms. The RF model for chlorosis was validated with a validation set (
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