ArticlePlants (Basel, Switzerland)2023
Reflectance Spectroscopy for the Classification and Prediction of Pigments in Agronomic Crops.
Article in Plants (Basel, Switzerland), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
10 citing papers in PubMed, 25 citations in OpenAlex.
- Pre-Visual Detection of Pine Wilt Disease Using an Optimized PSRI Derived from Hyperspectral Drone Imagery.Plants (Basel, Switzerland) · 2026Article
- An open-source spectral measurement platform for plant reflectance and material identification.HardwareX · 2026Article
- Hyperspectral-Imaging-Based ECNN-1D for Accurate Origin Classification of Fragrant Pears.Foods (Basel, Switzerland) · 2026Article
- Method of Characterization and Classification of the Physicochemical Quality of Polished White Rice Grains Using VIS/NIR/SWIR Techniques and Machine Learning Models for Lot Segregation and Commercialization in Storage and Processing Units.Foods (Basel, Switzerland) · 2025Article
- Variation in Assessment of Leaf Pigment Content from Vegetation Indices Caused by Positions and Widths of Spectral Channels.Plants (Basel, Switzerland) · 2025Article
- Predictive Modeling of Lignocellulosic Content in Crop Straws Using NIR Spectroscopy.Plants (Basel, Switzerland) · 2025Article
- Fluorescence and Hyperspectral Sensors for Nondestructive Analysis and Prediction of Biophysical Compounds in the Green and Purple Leaves ofSensors (Basel, Switzerland) · 2024Article
- Article
- Chemometric Analysis for the Prediction of Biochemical Compounds in Leaves Using UV-VIS-NIR-SWIR Hyperspectroscopy.Plants (Basel, Switzerland) · 2023Article
- Non-Invasive Assessment, Classification, and Prediction of Biophysical Parameters Using Reflectance Hyperspectroscopy.Plants (Basel, Switzerland) · 2023Article
Corrections and comments
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Authors and funding
4 authors at 2 institutions in 1 country.
Funding
Abstract
Reflectance spectroscopy, in combination with machine learning and artificial intelligence algorithms, is an effective method for classifying and predicting pigments and phenotyping in agronomic crops. This study aims to use hyperspectral data to develop a robust and precise method for the simultaneous evaluation of pigments, such as chlorophylls, carotenoids, anthocyanins, and flavonoids, in six agronomic crops: corn, sugarcane, coffee, canola, wheat, and tobacco. Our results demonstrate high classification accuracy and precision, with principal component analyses (PCAs)-linked clustering and a kappa coefficient analysis yielding results ranging from 92 to 100% in the ultraviolet-visible (UV-VIS) to near-infrared (NIR) to shortwave infrared (SWIR) bands. Predictive models based on partial least squares regression (PLSR) achieved R
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.