ArticlePlant phenomics (Washington, D.C.)2024
Maturity Classification of Rapeseed Using Hyperspectral Image Combined with Machine Learning.
Article in Plant phenomics (Washington, D.C.), 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
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Who cites it
11 citing papers in PubMed.
- Article
- Quantification of lettuce leaf DUS test traits and phenotypic fingerprint construction for variety identification.Plant phenomics (Washington, D.C.) · 2026Article
- KG-FT-Transformer: a knowledge-guided feature tokenizer transformer for hyperspectral prediction of lettuce quality trait and fingerprint analysis.Food chemistry: X · 2026Article
- Climate-robust evaluation of alfalfa seed maturity via an EMD-guided deep learning framework using multispectral imaging.Plant phenomics (Washington, D.C.) · 2026Article
- A lightweight hybrid transformer approach for hyperspectral imaging-based drought tolerance evaluation in tea plants.Plant methods · 2025Article
- Determination of optimal sampling time of grape embryo rescue based on near infrared spectroscopy combined with machine learning.Plant phenomics (Washington, D.C.) · 2025Article
- SSP-MambaNet: An automated system for detection and counting of missing seedlings in glass greenhouse-grown virus-free strawberry.Plant phenomics (Washington, D.C.) · 2025Article
- Classification of different gluten wheat varieties based on hyperspectral preprocessing, feature screening, and machine learning.Food chemistry: X · 2025Article
- Non-destructive analysis ofFrontiers in chemistry · 2025Article
- Optimized wavelength selection for eggplant seed vitality classification using information acquisition techniques.Frontiers in plant science · 2025Article
- A Small-Sample Classification Strategy for Extracting Fractional Cover of Native Grass Species and Noxious Weeds in the Alpine Grasslands.Sensors (Basel, Switzerland) · 2024Article
Corrections and comments
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Authors and funding
8 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Oilseed rape is an important oilseed crop planted worldwide. Maturity classification plays a crucial role in enhancing yield and expediting breeding research. Conventional methods of maturity classification are laborious and destructive in nature. In this study, a nondestructive classification model was established on the basis of hyperspectral imaging combined with machine learning algorithms. Initially, hyperspectral images were captured for 3 distinct ripeness stages of rapeseed, and raw spectral data were extracted from the hyperspectral images. The raw spectral data underwent preprocessing using 5 pretreatment methods, namely, Savitzky-Golay, first derivative, second derivative (D2nd), standard normal variate, and detrend, as well as various combinations of these methods. Subsequently, the feature wavelengths were extracted from the processed spectra using competitive adaptive reweighted sampling, successive projection algorithm (SPA), iterative spatial shrinkage of interval variables (IVISSA), and their combination algorithms, respectively. The classification models were constructed using the following algorithms: extreme learning machine,
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Registered trials
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