ArticlePlant methods2017
Mid-infrared spectroscopy combined with chemometrics to detect Sclerotinia stem rot on oilseed rape (
Article in Plant methods, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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Who cites it
9 citing papers in PubMed, 30 citations in OpenAlex.
- Electrochemical Biosensors for Oilseed Crops: Nanomaterial-Driven Detection and Smart Agriculture.Foods (Basel, Switzerland) · 2025Review
- Convolutional Neural Network Model for Variety Classification and Seed Quality Assessment of Winter Rapeseed.Sensors (Basel, Switzerland) · 2023Article
- Detection of Sulfite Dioxide Residue on the Surface of Fresh-Cut Potato Slices Using Near-Infrared Hyperspectral Imaging System and Portable Near-Infrared Spectrometer.Molecules (Basel, Switzerland) · 2020Article
- Investigation on Data Fusion of Multisource Spectral Data for Rice Leaf Diseases Identification Using Machine Learning Methods.Frontiers in plant science · 2020Article
- Application of Near-Infrared Hyperspectral Imaging with Machine Learning Methods to Identify Geographical Origins of Dry Narrow-Leaved Oleaster (Foods (Basel, Switzerland) · 2019Article
- Identifying Freshness of Spinach Leaves Stored at Different Temperatures Using Hyperspectral Imaging.Foods (Basel, Switzerland) · 2019Article
- Identification of Maize Kernel Vigor under Different Accelerated Aging Times Using Hyperspectral Imaging.Molecules (Basel, Switzerland) · 2018Article
- Discrimination ofMolecules (Basel, Switzerland) · 2018Article
- Detection ofSensors (Basel, Switzerland) · 2018Article
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Authors and funding
6 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundDetection of plant diseases in a fast and simple way is crucial for timely disease control. Conventionally, plant diseases are accurately identified by DNA, RNA or serology based methods which are time consuming, complex and expensive. Mid-infrared spectroscopy is a promising technique that simplifies the detection procedure for the disease. Mid-infrared spectroscopy was used to identify the spectral differences between healthy and infected oilseed rape leaves. Two different sample sets from two experiments were used to explore and validate the feasibility of using mid-infrared spectroscopy in detecting Sclerotinia stem rot (SSR) on oilseed rape leaves.
resultsThe average mid-infrared spectra showed differences between healthy and infected leaves, and the differences varied among different sample sets. Optimal wavenumbers for the 2 sample sets selected by the second derivative spectra were similar, indicating the efficacy of selecting optimal wavenumbers. Chemometric methods were further used to quantitatively detect the oilseed rape leaves infected by SSR, including the partial least squares-discriminant analysis, support vector machine and extreme learning machine. The discriminant models using the full spectra and the optimal wavenumbers of the 2 sample sets were effective for classification accuracies over 80%. The discriminant results for the 2 sample sets varied due to variations in the samples.
conclusionThe use of two sample sets proved and validated the feasibility of using mid-infrared spectroscopy and chemometric methods for detecting SSR on oilseed rape leaves. The similarities among the selected optimal wavenumbers in different sample sets made it feasible to simplify the models and build practical models. Mid-infrared spectroscopy is a reliable and promising technique for SSR control. This study helps in developing practical application of using mid-infrared spectroscopy combined with chemometrics to detect plant disease.
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