ArticlePlants (Basel, Switzerland)2023
Enhancing Pigment Phenotyping and Classification in Lettuce through the Integration of Reflectance Spectroscopy and AI Algorithms.
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 9 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.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
9 citing papers in PubMed.
- Toward Field Deployment: Tackling the Energy Challenge in Environmental Sensors.Sensors (Basel, Switzerland) · 2025Review
- From aerial drone to quantitative trait locus: leveraging next-generation phenotyping to reveal the genetics of color and height in field-grown Lactuca sativa.The Plant journal : for cell and molecular biology · 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
- Prediction of leaf nitrogen in sugarcane (Heliyon · 2024Article
- Real-Time AI-Assisted Push-Broom Hyperspectral System for Precision Agriculture.Sensors (Basel, Switzerland) · 2024Article
- An Integrated Multi-Omics and Artificial Intelligence Framework for Advance Plant Phenotyping in Horticulture.Biology · 2023Review
- Non-Invasive Assessment, Classification, and Prediction of Biophysical Parameters Using Reflectance Hyperspectroscopy.Plants (Basel, Switzerland) · 2023Article
- Reflectance Spectroscopy for the Classification and Prediction of Pigments in Agronomic Crops.Plants (Basel, Switzerland) · 2023Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors.
Funding
Abstract
In this study, we investigated the use of artificial intelligence algorithms (AIAs) in combination with VIS-NIR-SWIR hyperspectroscopy for the classification of eleven lettuce plant varieties. For this purpose, a spectroradiometer was utilized to collect hyperspectral data in the VIS-NIR-SWIR range, and 17 AIAs were applied to classify lettuce plants. The results showed that the highest accuracy and precision were achieved using the full hyperspectral curves or the specific spectral ranges of 400-700 nm, 700-1300 nm, and 1300-2400 nm. Four models, AdB, CN2, G-Boo, and NN, demonstrated exceptional 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.