ArticleSensors (Basel, Switzerland)2022
Comparison of Selected Dimensionality Reduction Methods for Detection of Root-Knot Nematode Infestations in Potato Tubers Using Hyperspectral Imaging.
Article in Sensors (Basel, Switzerland), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
What it found
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Who cites it
6 citing papers in PubMed, 27 citations in OpenAlex.
- Feature extraction in sensor plant disease datasets using reformed membership functions independent of class variables.Scientific reports · 2026Article
- Detecting nematodes in potato plants an explainable machine learning approach for detection of potato cyst nematode infections using hyperspectral imaging.Plant phenomics (Washington, D.C.) · 2025Article
- Enhancing Water-Deficient Potato Plant Identification: Assessing Realistic Performance of Attention-Based Deep Neural Networks and Hyperspectral Imaging for Agricultural Applications.Plants (Basel, Switzerland) · 2024Article
- Hyperspectral imaging for the detection of plant pathogens in seeds: recent developments and challenges.Frontiers in plant science · 2024Review
- Enhancing host-pathogen phenotyping dynamics: early detection of tomato bacterial diseases using hyperspectral point measurement and predictive modeling.Frontiers in plant science · 2023Article
- Methodologies Used in Remote Sensing Data Analysis and Remote Sensors for Precision Agriculture.Sensors (Basel, Switzerland) · 2022Article
Corrections and comments
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Authors and funding
3 authors at 1 institution in 1 country.
Funding
Abstract
Hyperspectral imaging is a popular tool used for non-invasive plant disease detection. Data acquired with it usually consist of many correlated features; hence most of the acquired information is redundant. Dimensionality reduction methods are used to transform the data sets from high-dimensional, to low-dimensional (in this study to one or a few features). We have chosen six dimensionality reduction methods (partial least squares, linear discriminant analysis, principal component analysis, RandomForest, ReliefF, and Extreme gradient boosting) and tested their efficacy on a hyperspectral data set of potato tubers. The extracted or selected features were pipelined to support vector machine classifier and evaluated. Tubers were divided into two groups, healthy and infested with
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Registered trials
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