ReviewGigaScience2020
Technical workflows for hyperspectral plant image assessment and processing on the greenhouse and laboratory scale.
Review in GigaScience, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers.
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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
19 citing papers in PubMed.
- Overcoming difficulties in segmentation of hyperspectral plant images with small projection areas using machine learning.Scientific reports · 2026Article
- Light Sources in Hyperspectral Imaging Simultaneously Influence Object Detection Performance and Vase Life of Cut Roses.Plants (Basel, Switzerland) · 2026Article
- Evaluating the Performance of Hyperspectral Imaging Endoscopes: Mitigating Parameters Affecting Spectral Accuracy.Biosensors · 2025Article
- Hyperspectral Imaging Techniques for Lyophilization: Advances in Data-Driven Modeling Strategies and Applications.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2025Review
- A hyperspectral open-source imager (HOSI).BMC biology · 2025Article
- Quality assurance of hyperspectral imaging systems for neural network supported plant phenotyping.Plant methods · 2024Article
- Systematic Meta-Analysis of Computer-Aided Detection of Breast Cancer Using Hyperspectral Imaging.Bioengineering (Basel, Switzerland) · 2024Review
- Detection Methods for Pine Wilt Disease: A Comprehensive Review.Plants (Basel, Switzerland) · 2024Review
- RGB Imaging as a Tool for Remote Sensing of Characteristics of Terrestrial Plants: A Review.Plants (Basel, Switzerland) · 2024Review
- Advances in the tea plants phenotyping using hyperspectral imaging technology.Frontiers in plant science · 2024Review
- Systematic meta-analysis of computer-aided detection to detect early esophageal cancer using hyperspectral imaging.Biomedical optics express · 2023Article
- A Synthetic Review of Various Dimensions of Non-Destructive Plant Stress Phenotyping.Plants (Basel, Switzerland) · 2023Review
- Cheminformatics and artificial intelligence for accelerating agrochemical discovery.Frontiers in chemistry · 2023Review
- LED color gradient as a new screening tool for rapid phenotyping of plant responses to light quality.GigaScience · 2022Article
- Estimation of soybean yield parameters under lodging conditions using RGB information from unmanned aerial vehicles.Frontiers in plant science · 2022Article
- Combination of spectral index and transfer learning strategy for glyphosate-resistant cultivar identification.Frontiers in plant science · 2022Article
- Assessing expected utility and profitability to support decision-making for disease control strategies in ornamental heather production.Precision agriculture · 2022Article
- Digital plant pathology: a foundation and guide to modern agriculture.Journal of plant diseases and protection : scientific journal of the German Phytomedical Society (DPG) · 2022Article
- Genome-Wide Association Studies of Soybean Yield-Related Hyperspectral Reflectance Bands Using Machine Learning-Mediated Data Integration Methods.Frontiers in plant science · 2021Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundThe use of hyperspectral cameras is well established in the field of plant phenotyping, especially as a part of high-throughput routines in greenhouses. Nevertheless, the workflows used differ depending on the applied camera, the plants being imaged, the experience of the users, and the measurement set-up.
resultsThis review describes a general workflow for the assessment and processing of hyperspectral plant data at greenhouse and laboratory scale. Aiming at a detailed description of possible error sources, a comprehensive literature review of possibilities to overcome these errors and influences is provided. The processing of hyperspectral data of plants starting from the hardware sensor calibration, the software processing steps to overcome sensor inaccuracies, and the preparation for machine learning is shown and described in detail. Furthermore, plant traits extracted from spectral hypercubes are categorized to standardize the terms used when describing hyperspectral traits in plant phenotyping. A scientific data perspective is introduced covering information for canopy, single organs, plant development, and also combined traits coming from spectral and 3D measuring devices.
conclusionsThis publication provides a structured overview on implementing hyperspectral imaging into biological studies at greenhouse and laboratory scale. Workflows have been categorized to define a trait-level scale according to their metrological level and the processing complexity. A general workflow is shown to outline procedures and requirements to provide fully calibrated data of the highest quality. This is essential for differentiation of the smallest changes from hyperspectral reflectance of plants, to track and trace hyperspectral development as an answer to biotic or abiotic stresses.
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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.