A New Compact VNIR Hyperspectral Imaging System for Non-Invasive Analysis in the FineArt and Architecture Fields

Dietary supplements of anthocyanin-rich vegetables have been known to increase potential health benefits for humans. The optimization of environmental conditions to increase the level of anthocyanin accumulations in vegetables during the cultivation periods is particularly important in terms of the improvement of agricultural values in the indoor farm using artificial light and climate controlling systems. This study reports on the measurement of variations in anthocyanin accumulations in leaf tissues of four different cultivars in Brassica rapa var. chinensis (bok choy) grown under the different environmental conditions of the indoor farm using hyperspectral imaging. Anthocyanin accumulations estimated by hyperspectral imaging were compared with the measured anthocyanin accumulation obtained by destructive analysis. Between hyperspectral imaging and destructive analysis values, no significant differences in anthocyanin accumulation were observed across four bok choy cultivars grown under the anthocyanin stimulation environmental condition, whereas the estimated anthocyanin accumulations displayed cultivar-dependent significant differences, suggesting that hyperspectral imaging can be employed to measure variations in anthocyanin accumulations of different bok choy cultivars. Increased accumulation of anthocyanin under the stimulation condition for anthocyanin accumulation was observed in “purple magic” and “red stem” by both hyperspectral imaging and destructive analysis. In the different growth stages, no significant differences in anthocyanin accumulation were found in each cultivar by both hyperspectral imaging and destructive analysis. These results suggest that hyperspectral imaging can provide comparable analytic capability with destructive analysis to measure variations in anthocyanin accumulation that occurred under the different light and temperature conditions of the indoor farm. Leaf image analysis measuring the percentage of purple color area in the total leaf area displayed successful classification of anthocyanin accumulation in four bok choy cultivars in comparison to hyperspectral imaging and destructive analysis, but it also showed limitation to reflect the level of color saturation caused by anthocyanin accumulation under different environmental conditions in “red stem,” “white stem,” and “green stem.” Finally, our hyperspectral imaging system was modified to be applied onto the high-throughput plant phenotyping system, and its test to analyze the variation of anthocyanin accumulation in four cultivars showed comparable results with the result of the destructive analysis.

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Research of the spectral characteristics of healthy and fusarium-infected oat seeds of the variety “ZALP” with hyperspectral camera

E3S Web of Conferences ◽

10.1051/e3sconf/202128502015 ◽

2021 ◽

Vol 285 ◽

pp. 02015

Author(s):

Maksim Moskovskiy ◽

Stanislav Gerasimenko ◽

Andrey Boiko ◽

Sergey Vorobev

Keyword(s):

Hyperspectral Imaging ◽

Imaging System ◽

Spectral Characteristics ◽

Central Zone ◽

Spectral Lines ◽

Hyperspectral Images ◽

Genus Fusarium ◽

Oat Seeds ◽

The Russian Federation ◽

Hyperspectral Camera

The paper presents the research of the spectral lines of healthy and fusarium-infected oat seeds using a hyperspectral imaging system. Hyperspectral images of healthy and diseased kernels were studied using the Specim IQ hyperspectral camera and Albedo 4.0.23 program (for subsequent processing). The spectral diagrams of the horizon (800 nm - 1000 nm) have been determined. They can identify the oat seeds of the ZALP variety which infected with pathogenic microflora by fungi of the genus Fusarium. Variety harvested in the central zone of the Russian Federation.

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Mapping of fatty acids composition in shelled almonds analysed in bulk using a Hyperspectral Imaging system

LWT ◽

10.1016/j.lwt.2020.110678 ◽

2021 ◽

Vol 138 ◽

pp. 110678

Author(s):

Irina Torres ◽

Dolores Pérez-Marín ◽

Miguel Vega-Castellote ◽

María-Teresa Sánchez

Keyword(s):

Fatty Acids ◽

Hyperspectral Imaging ◽

Imaging System ◽

Fatty Acids Composition

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Forensic analysis of beverage stains using hyperspectral imaging

Scientific Reports ◽

10.1038/s41598-021-85737-x ◽

2021 ◽

Vol 11 (1) ◽

Author(s):

Binu Melit Devassy ◽

Sony George

Keyword(s):

Hyperspectral Imaging ◽

Crime Scene ◽

Forensic Analysis ◽

Classification Model ◽

Support Vector ◽

Additional Information ◽

Non Invasive ◽

Spectral Bands ◽

Data Dimensionality Reduction ◽

Gradient Based

AbstractDocumentation and analysis of crime scene evidences are of great importance in any forensic investigation. In this paper, we present the potential of hyperspectral imaging (HSI) to detect and analyze the beverage stains on a paper towel. To detect the presence and predict the age of the commonly used drinks in a crime scene, we leveraged the additional information present in the HSI data. We used 12 different beverages and four types of paper hand towel to create the sample stains in the current study. A support vector machine (SVM) is used to achieve the classification, and a convolutional auto-encoder is used to achieve HSI data dimensionality reduction, which helps in easy perception, process, and visualization of the data. The SVM classification model was re-established for a lighter and quicker classification model on the basis of the reduced dimension. We employed volume-gradient-based band selection for the identification of relevant spectral bands in the HSI data. Spectral data recorded at different time intervals up to 72 h is analyzed to trace the spectral changes. The results show the efficacy of the HSI techniques for rapid, non-contact, and non-invasive analysis of beverage stains.

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Material characterisation of a painted beehive panel by advanced spectroscopic and chromatographic techniques in combination with hyperspectral imaging

Heritage Science ◽

10.1186/s40494-020-00468-y ◽

2020 ◽

Vol 8 (1) ◽

Author(s):

Klara Retko ◽

Maša Kavčič ◽

Lea Legan ◽

Polonca Ropret ◽

Bojana Rogelj Škafar ◽

...

Keyword(s):

Raman Spectroscopy ◽

Hyperspectral Imaging ◽

Cross Sections ◽

Extreme Temperature ◽

Transformation Products ◽

Paint Layer ◽

Non Invasive ◽

Painting Technique ◽

Invasive Techniques ◽

Conservation Interventions

AbstractIn this study, a painted beehive panel from the collection of the Slovene Ethnographic Museum was examined with respect to its material composition with the aim to reveal the painting technique. Due to the state of degradation due to outdoor weathering (UV irradiation, rainfall, extreme temperature and humidity fluctuations), as well as past conservation interventions, the object represented a complex analytical challenge. We aimed for non-invasive techniques (FTIR in reflection mode, Raman spectroscopy and hyperspectral imaging in the range of 400–2500 nm); however, in order to explore paint layers, cross-sections were also analysed using Raman spectroscopy. FTIR spectroscopy in transmission mode and gas chromatography coupled to mass spectrometry were also used on sample fragments. Various original materials were identified such as pigments and binders. The surface coating applied during conservation interventions was also characterised. Additionally, organic compounds were found (oxalate, carboxylate), representing transformation products. The potential use of Prussian blue as a background paint layer is discussed.

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Precise Estimation of NDVI with a Simple NIR Sensitive RGB Camera and Machine Learning Methods for Corn Plants

Sensors ◽

10.3390/s20113208 ◽

2020 ◽

Vol 20 (11) ◽

pp. 3208 ◽

Cited By ~ 1

Author(s):

Liangju Wang ◽

Yunhong Duan ◽

Libo Zhang ◽

Tanzeel U. Rehman ◽

Dongdong Ma ◽

...

Keyword(s):

Machine Learning ◽

Professional Training ◽

Imaging System ◽

Estimation Method ◽

Machine Learning Algorithms ◽

Plant Phenotyping ◽

Band Pass Filter ◽

Pass Filter ◽

Corn Plants ◽

Hyperspectral Camera

The normalized difference vegetation index (NDVI) is widely used in remote sensing to monitor plant growth and chlorophyll levels. Usually, a multispectral camera (MSC) or hyperspectral camera (HSC) is required to obtain the near-infrared (NIR) and red bands for calculating NDVI. However, these cameras are expensive, heavy, difficult to geo-reference, and require professional training in imaging and data processing. On the other hand, the RGBN camera (NIR sensitive RGB camera, simply modified from standard RGB cameras by removing the NIR rejection filter) have also been explored to measure NDVI, but the results did not exactly match the NDVI from the MSC or HSC solutions. This study demonstrates an improved NDVI estimation method with an RGBN camera-based imaging system (Ncam) and machine learning algorithms. The Ncam consisted of an RGBN camera, a filter, and a microcontroller with a total cost of only $70 ~ 85. This new NDVI estimation solution was compared with a high-end hyperspectral camera in an experiment with corn plants under different nitrogen and water treatments. The results showed that the Ncam with two-band-pass filter achieved high performance (R2 = 0.96, RMSE = 0.0079) at estimating NDVI with the machine learning model. Additional tests showed that besides NDVI, this low-cost Ncam was also capable of predicting corn plant nitrogen contents precisely. Thus, Ncam is a potential option for MSC and HSC in plant phenotyping projects.

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