Low-Cost Hyperspectral Imaging with a Smartphone

Journal of Imaging

Article Low-Cost Hyperspectral Imaging with a Smartphone

Mary B. Stuart 1 , Andrew J. S. McGonigle 2 , Matthew Davies 1 , Matthew J. Hobbs 1 , Nicholas A. Boone 1 , Leigh R. Stanger 1 , Chengxi Zhu 1,3 , Tom D. Pering 2 and Jon R. Willmott 1,*

1 Department of Electronic and Electrical Engineering, University of Sheffield, Sheffield S1 4DE, UK; mbstuart1@sheffield.ac.uk (M.B.S.); matt.davies@sheffield.ac.uk (M.D.); m.hobbs@sheffield.ac.uk (M.J.H.); nick.boone@sheffield.ac.uk (N.A.B.); leigh.r.stanger@gmail.com (L.R.S.); [email protected] (C.Z.) 2 Department of Geography, University of Sheffield, Sheffield S10 2TN, UK; a.mcgonigle@sheffield.ac.uk (A.J.S.M.); t.pering@sheffield.ac.uk (T.D.P.) 3 Cambridge Advanced Imaging Centre, University of Cambridge, Cambridge CB2 3DY, UK * Correspondence: j.r.willmott@sheffield.ac.uk

Abstract: Recent advances in smartphone technologies have opened the door to the development of accessible, highly portable sensing tools capable of accurate and reliable data collection in a range of environmental settings. In this article, we introduce a low-cost smartphone-based hyperspectral imaging system that can convert a standard smartphone camera into a visible wavelength hyperspectral sensor for ca. £100. To the best of our knowledge, this represents the first smartphone capable of hyperspectral data collection without the need for extensive post processing. The Hyperspec- tral Smartphone’s abilities are tested in a variety of environmental applications and its capabilities directly compared to the laboratory-based analogue from our previous research, as well as the Citation: Stuart, M.B.; wider existing literature. The Hyperspectral Smartphone is capable of accurate, laboratory- and McGonigle, A.J.S.; Davies, M.; Hobbs, M.J.; Boone, N.A.; field-based hyperspectral data collection, demonstrating the significant promise of both this device Stanger, L.R.; Zhu, C.; Pering, T.D.; and smartphone-based hyperspectral imaging as a whole. Willmott, J.R. Low-Cost Hyperspectral Imaging with a Keywords: hyperspectral; smartphone; low-cost; environmental monitoring; field deployable; portable Smartphone. J. Imaging 2021, 7, 136. https://doi.org/10.3390/jimaging 7080136 1. Introduction Academic Editors: Juan Luis Nieves The rapid progression of smartphone technologies in recent years has led to a signifi- Gómez and Miguel cant uptake in smartphone-based components in the design of optical sensing technologies. A. Martínez-Domingo The inclusion of these consumer market components provides a significant advantage to device developers due to the substantial reduction in costs provided by the off-the-shelf Received: 25 June 2021 nature of these components [1,2]. Furthermore, the increased processing power and the Accepted: 2 August 2021 Published: 5 August 2021 high-resolution camera systems often associated with these low-cost devices provides substantial opportunities to develop state-of-the-art optical sensing technologies at a frac-

Publisher’s Note: MDPI stays neutral tion of the cost of currently available systems without compromising on the data quality with regard to jurisdictional claims in captured. To date, smartphone technologies have been utilised in a variety of devices such published maps and institutional afﬁl- as smartphone spectrometers [3–5] and multispectral sensors [6–8]. These devices have iations. been implemented in a wide variety of settings, from point-of-care analysis to environmental monitoring applications. As such, smartphones represent one of the most promising platforms for the development of cost-effective portable measurement technologies. More recently, there has been a drive toward developing smartphone-based hyperspectral imaging to provide a level of data resolution not possible with single pixel and Copyright: © 2021 by the authors. Licensee MDPI, Basel, Switzerland. multispectral designs. A number of studies have produced low-cost hyperspectral im- This article is an open access article agers either comprised of smartphone components or capable of communication with a distributed under the terms and smartphone for data viewing processes, e.g., [9–12]. Furthermore, studies such as those conditions of the Creative Commons of He and Wang [13] and Park et al. [14] have developed smartphone-based hyperspec- Attribution (CC BY) license (https:// tral imagers through the virtual transformation of the RGB data acquired by the built-in creativecommons.org/licenses/by/ camera sensor. Whilst the examples above demonstrate that accessibility is beginning 4.0/). to improve, affordable, ﬁeld deployable hyperspectral sensors remain in short supply

J. Imaging 2021, 7, 136. https://doi.org/10.3390/jimaging7080136 https://www.mdpi.com/journal/jimaging J. Imaging 2021, 7, 136 2 of 13

across a wide range of application areas [15], with the increasing focus on our changing climate highlighting the growing need for low-cost, field deployable hyperspectral sensors across environmental monitoring applications. By developing a range of low-cost, portable hyperspectral sensors, we increase the potential for valuable data collection across a wider range of key environmental areas. This, in turn, allows us to improve our understanding of the processes that effect these highly important, dynamic environments. To date, very low-cost spectrometers have been designed for incorporation with smartphone devices. These devices have largely been designed as teaching aids, providing easily accessible, educational set-ups, for example, Public Lab [16], presents a simple papercraft spectrometer, where a cardboard housing and a simple diffraction grating utilising a digital video disc (DVD) fragment are used to capture spectral datasets from a smartphone camera. Whilst this simple design does allow for spectral datasets to be observed, its basic components limit its potential uses, for example, the use of a DVD fragment as a diffraction grating substitute can introduce potential data quality issues, resulting from scattered light interference as well as transmittance shifts leading to the intensity variations of imaged objects at each wavelength of interest [17–19]. Despite these limitations, this basic design highlights the potential availability of low-cost, accessible, smartphone-based data collection. In this article, we aim to demonstrate the opportunities available for low-cost smartphone- based sensing units through the construction of a low-cost unit capable of accurate, field- based data collection. Using the Public Lab spectrometer design as a foundation for further innovation, we introduce a low-cost, field deployable system where a standard smartphone can be utilised as a hyperspectral imager through the addition of a 3-D printed attachment. This device is capable of data capture across the visible spectrum (400–700 nm) and represents a valuable, easily implemented design, which, to the best of our knowledge, is the first fully incorporated smartphone-based hyperspectral imaging system of its kind. In this article, we will describe the design and build process required for this instrument. We then demonstrate its efficacy in a variety of environmental applications, providing a clear comparison to the existing literature and our previous work [1]. In so doing, we highlight the standard of data collection possible with such a low-cost, portable design.

2. Materials and Methods 2.1. The Hyperspectral Smartphone The Hyperspectral Smartphone (Figure1) is comprised of an easily attachable 3-D printed spectral housing containing an Edmund Optics transmission diffraction grating (#49-580). This instrument is intended to be versatile, and as such the attachment grip and spectral housing position are adjustable, increasing usability across a variety of smartphone widths and camera configurations. The 3-D printed housing provides a robust structure, increasing the overall reliability of this design. Furthermore, a standard slit is present within the printed design, which can be narrowed to best fit the intended target scene. The diffraction grating is positioned directly in front of the smartphone camera sensor. To minimise potential light leaks at this location, cushioning foam has been attached to provide a secure fit between the smartphone and spectrometer components. This new design can be easily and reliably attached to a wide range of smartphone devices allowing for accurate, repeatable data collection in a range of environmental settings. Furthermore, the optics provide substantial improvements to the quality of data capture possible without introducing significant costs. The Hyperspectral Smartphone costs ca. £100, making it a highly accessible, low-cost hyperspectral instrument when compared to currently available portable hyperspectral imagers in commercial markets. J. Imaging 2021, 7, 136 3 of 13 J. Imaging 2021, 7, x FOR PEER REVIEW 3 of 14

FigureFigure 1. SchematicSchematic diagram diagram of of the the Hyperspectral Hyperspectral Smartp Smartphonehone mounted mounted to the to thetranslation translation stage. stage. (A) (andA) and (B) (showB) show the thefront front and and rear rear views, views, respectively, respectively, (C) ( Cshows) shows the the Hyperspectral Hyperspectral Smartphone Smartphone attachment prior to connection with a smartphone, highlighting the location of the spectral optics. (D) attachment prior to connection with a smartphone, highlighting the location of the spectral optics. shows a cross section of the smartphone spectrometer system and shows how the marginal and (D) shows a cross section of the smartphone spectrometer system and shows how the marginal and chief rays travel through the system. chief rays travel through the system.

2.2.2.2. Image Capture and Data Processing The HyperspectralHyperspectral Smartphone Smartphone is is a a push push broom broom style style sensor sensor that that can can be usedbe used as eitheras ei- ather hand-held a hand-held or tripod-based or tripod-based instrument; instrument; however, however, it should it beshould noted be that, noted due that, to operator due to shake,operator datasets shake, captureddatasets captured as a hand-held as a hand-h deviceeld can device be subject can be tosubject greater to distortion.greater distor- To minimisetion. To minimise these effects, these theeffects, device the candevice be mountedcan be mounted on an automatedon an automated translation translation stage, wherestage, where a stepper a stepper motor ismotor used is to used track to across track theacross target the scene. target The scene. slit-width The slit-width of the system of the issystem 0.5 mm is 0.5 and mm the and slit-lens the slit-len distances distance is 90 mm. is 90 The mm. inclusion The inclusion of the translation of the translation stage allows stage forallows stable, for repeatablestable, repeatable scene passes, scene resultingpasses, resulting in clearer in datasets. clearer datasets. Whilst it Whilst remains it possibleremains topossible collect to datasets collect bydatasets hand, by the hand, use of the the use translation of the translation stage results stage in results much clearerin much imagery. clearer Dataimagery. collection Data collection was completed was completed using the using video the function video function of the smartphone of the smartphone camera. cam- For allera. data For collection,all data collection, the video the frame video rate frame was rate set atwas 30 set fps. at Once30 fps. recording, Once recording, the device the wasdevice tracked, was tracked, left to right,left to acrossright, aacross scene a atscene 1 mm/s at 1 mm/sec intervals intervals across the across target. the Whentarget.

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recordingWhen recording scenes withinscenes awithin laboratory a laboratory setting, thesetting, target the object target is placedobject is within placed a darkwithin box a todark minimise box to minimise the interference the interference of ambient of am straybient light stray and light is illuminatedand is illuminated using ausing 20 Watt a 20 LEDWatt lampLED lamp with awith diffuser a diffuser to minimise to minimise bright bright spots spots within within the scene. the scene. Figure Figure2 shows 2 shows the Hyperspectralthe Hyperspectral Smartphone Smartphone ready ready for datafor data collection collection within within a laboratory a laboratory environment. environment. As mentionedAs mentioned above, above, the instrumentthe instrument is versatile is versat andile can,and therefore,can, therefore, be used be withused a with wide a range wide ofrange smartphones, of smartphones, however, however, for the for purposes the purpos ofes this of this article, article, the the smartphone smartphone used used was was a Samsunga Samsung Galaxy Galaxy A12. A12. The The presence presence of of an an auto-focus auto-focus feature feature within within the the smartphone smartphone camera cam- softwareera software is of significantis of significant benefit tobenefit this instrument, to this instrument, minimising minimising the specialist the knowledge specialist requiredknowledge to required operate theto operate Hyperspectral the Hyperspect Smartphone,ral Smartphone, and as such, and providing as such, providing a versatile, a accessible,versatile, accessible, user-friendly user-friendly system. Asystem. full list A of full camera list of specificationscamera specifications for the Galaxyfor the A12Gal- canaxy beA12 found can be within found Table within1. TheTable instantaneous 1. The instantaneous field of field view of (IFOV) view (IFOV) [ 20] for [20] each for pixel each withinpixel within the spectrometer the spectrometer image image was measured was measured to be approximately to be approximately 5 mm × 5 5mm mm × for5 mm a 95% for energya 95% energy enclosure enclosure at a working at a working distance distance of 300 mm. of 300 Whilst mm. theWhilst total the field total of viewfield (TFOV)of view is(TFOV) determined is determined by several by factors.several factors. At short At working short working distances distances (on the (on order the of order the travelof the distance)travel distance) the vertical the vertical field of fi vieweld of (VFOV) view (VFOV) is determined is determined by the slit by height the slit and height the distance and the betweendistance thebetween slit and the the slit grating, and the and grating, the horizontal and the horizontal field of view field (HFOV) of view is determined(HFOV) is de- by thetermined travel by distance the travel of the distance phone. of At the greater phone. working At greater distances, working the distances, acceptance the angle acceptance of the slitangle (which of the is slit determined (which is determined by the slit width,by the sl height,it width, and height, distance and to di thestance grating) to the makes grating) a greatermakes a contribution greater contribution to both the to HFOV both the and HFOV the VFOV. and Nothe additionalVFOV. No coupling additional optics coupling were incorporatedoptics were incorporated before the spectrometer before the spectrometer slit. slit.

Figure 2. TheThe Hyperspectral Hyperspectral Smartphone Smartphone mounted mounted on on the the translation translation stage stage ready ready to image to image an ob- an obsidiansidian flow ﬂow banded banded ash ash tuff tuff rock rock within within a laboratory a laboratory setting. setting.

TableTable 1.1. CameraCamera speciﬁcationsspecifications forfor thethe SamsungSamsung GalaxyGalaxy A12A12 smartphone.smartphone.

SamsungSamsung GalaxyGalaxy A12 A12 ResolutionResolution 48 48 MP MP (1920 ×× 1080 forfor video) video) FF Number 2.0 FocalFocal Length (mm)(mm) 26 Fps 30 Fps 30 Once capture was complete, individual frames were extracted from the video ﬁle. Dark and whiteOnce referencecapture was frames complete, were alsoindividual captured frames at this were stage. extracted To obtain from a white the video reference file. aDark piece and of mattwhite white reference card frames was positioned were also in captured place of at the this target stage. object To obtain and illuminated a white refer- in theence same a piece manner. of matt To white correct card the was image positioned frames in for place illumination of the target and object sensor and biases illuminated whilst preservingin the same the manner. colour To data correct of the the output, image the frames following for illumination analysis was and implemented sensor biases in Matlab whilst forpreserving each colour the regioncolour withindata of each the frame. output, the following analysis was implemented in Matlab for each colour region within each frame. (target − dark ) (whiteλ − darkλ ) target = raw λ λ · 1 1 (1) λ ( − ) ( − ) whiteλ darkλ targetraw λ1 darkλ1

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where target represents the object to be imaged, and dark and white represent the dark and white references, respectively. The subscript raw λ represents an uncorrected spectrum. In order to calibrate the instrument and to enable samples from different sources to be accurately compared, a radiometric assessment of the optical power represented by each pixel within the image was performed by measuring the power reflected by the white reference target. This was performed using a photodiode-based radiometer, as described by Zhu et al. [21], but with its RG850 long-pass filter replaced by a narrow bandpass filter centred on 550 nm with a full width half maximum of 10 nm (Thorlabs Stock #FB550-10, Ely, UK). By comparing the photocurrent measured by the radiometer with and without the filter in place, the reflected optical power collected by the radiometer could be calculated. Given that the FOV of the radiometer represented an area upon the target of approximately 14 mm in diameter at its 1 m operating distance, the reflected power per unit area, without the filter in place, was calculated from this to be approximately 46.55 uW/m2. Therefore, the optical power reflected from the white reference target was estimated to be 7.17 nW. For a comparison, the radiometer was sighted at the LED lamp directly, resulting in an optical power measurement of 108.62 nW. These values show that the optical throughput (related to etendue [22]), is approximately 6.6%. Given that each pixel within the image of the spectrometer represents an area of 5 mm × 5 mm, the power collected in total per pixel of the spectrometer is approximately 1.16 nW for the white reference target. In the author’s experience, a typical “rule of thumb” for the development of optical instrumentation is that a good signal to noise ratio can be achieved using a silicon-based detector measuring 1 nW in 1 µm. In order to calibrate the instrument spectrally, and to extract a spectral response curve from each corrected image frame, we utilised the calibration process within the Spectral Workbench software. This software is also publicly available and accessible from Public Lab [23], further demonstrating the accessible nature of this instrument. To complete the calibration accurately, a separate calibration image needed to be captured of a fluorescent lamp. Compact fluorescent lamps contain mercury vapour, which, when energised, emits a consistent, characteristic spectrum. Accurate wavelength ranges can then be applied to the image frames by aligning the peaks present at 436 nm and 546 nm within the software with their respective peaks in the calibration image. The resulting spectral curves were then averaged to provide one comparable spectrum for each target, allowing for direct comparisons to be made between different days and/or different targets. In its current format, the presence of a Bayer colour filter within the smartphone optics will reduce the sensitivity of the measurements captured with this instrument, however, as demonstrated below, we have been able to produce accurate, high quality, qualitative datasets, and as such we highlight the substantial potential available within this area of research. Finally, spatial datasets were created in Origin Pro (2020b) by extracting the pixel values from a specific wavelength/column within each image frame from the chosen scene. These columns were then combined to create a spatial dataset of the target object that displayed the spectral response captured from a specific wavelength.

3. Results and Discussion 3.1. Applications The laboratory-based environmental applications discussed in Sections 3.1.1 and 3.1.2 replicate the experimental work conducted in Stuart et al. [1]. We decided to replicate these experiments as they can be simply implemented and produce rapid results that easily demonstrate the capabilities of the hyperspectral sensor when the resulting datasets are compared to the existing literature. Furthermore, accurate, low-cost hyperspectral alternatives remain somewhat absent in these contexts at present, therefore, we aim to provide a foundation for the development of low-cost hyperspectral data collection in these application areas. As such, a full explanation of these experiments and their importance to their relevant ﬁelds can be found in Stuart et al. [1]. We then go on to compare the Hyperspectral Smartphone to a pre-existing, laboratory-based hyperspectral imager in J. Imaging 2021, 7, x FOR PEER REVIEW 6 of 14

are compared to the existing literature. Furthermore, accurate, low-cost hyperspectral alternatives remain somewhat absent in these contexts at present, therefore, we aim to provide a foundation for the development of low-cost hyperspectral data collection in these application areas. As such, a full explanation of these experiments and their importance to their relevant fields can be found in Stuart et al. [1]. We then go on to compare the J. Imaging 2021, 7, 136 Hyperspectral Smartphone to a pre-existing, laboratory-based hyperspectral imager6 ofin 13 Section 3.2 before highlighting the capabilities of the Hyperspectral Smartphone as a field- portable device in Section 3.3, where the instrument is utilised in a field setting for the collection of both vegetation and wider landscape datasets at different working distances. Section 3.2 before highlighting the capabilities of the Hyperspectral Smartphone as a field- 3.1.1.portable Fruit deviceQuality in Control Section 3.3, where the instrument is utilised in a field setting for the collection of both vegetation and wider landscape datasets at different working distances. Hyperspectral images of a healthy apple were captured every 24 h over the course of five3.1.1. days Fruit in Qualityorder to Controlobserve any potential changes in spectral response associated with the breakdownHyperspectral of pigments images during of a healthy the fruit apple ripening were captured process [1,24,25]. every 24 Figure h over the3 shows course the of observedfive days spectral in order response to observe curve any of potential the frui changest over the in five-day spectral responsemeasurement associated period. with A clearthe breakdownincrease in reflectance of pigments over during time the can fruit be observed, ripening process which is [1 to,24 be,25 expected]. Figure3 during shows the the ripeningobserved process spectral [26,27], response emphasising curve of the the Hy fruitperspectral over the Smartphone’s five-day measurement capability period.to detect A accurateclear increase spectral in data. reflectance Furthermore, over time absorpti can beon observed, features whichassociated is to with be expected the pigmentation during the ofripening the fruit process are evident [26,27 within], emphasising the dataset; the most Hyperspectral notably a Smartphone’s‘shoulder’ starting capability at ca. to550 detect nm associatedaccurate spectral with anthocyanin data. Furthermore, absorption absorption and a slight features ‘shoulder’ associated at ca. with 650 nm the highlighting pigmentation theof absorption the fruit are of evident chlorophyll within b the[24,28]. dataset; The mostliterature notably also a highlights ‘shoulder’ an starting absorption at ca. feature 550 nm forassociated chlorophyll with a, anthocyanin which is represented absorption by and a distinct a slight loss ‘shoulder’ in reflectance at ca. 650 at nmca. 675 highlighting nm fol- lowedthe absorption by a rapid of increase chlorophyll in reflectance b [24,28]. Thetowards literature the infrared also highlights [24,26–28]. an absorption Whilst a loss feature in reflectancefor chlorophyll is observed a, which in our is dataset represented in this by region, a distinct the subsequent loss in reflectance increase is at not ca. present. 675 nm Thisfollowed is believed by a rapid to be increasedue to the in reflectancespectral ra towardsnge of the the Hyperspectral infrared [24,26 Smartphone,–28]. Whilst aas loss this in absorptionreflectance feature is observed is present in our close dataset to inthe this upper region, boundary the subsequent of the instrument’s increase is not spectral present. range.This isWe believed therefore to be infer due that to the the spectral reduced range sensitivity of the Hyperspectralassociated with Smartphone, the edges of as the this spectralabsorption range feature of the is diffraction present close grating to the may upper be boundaryresulting ofin thesome instrument’s data loss in spectral this region. range. DespiteWe therefore these losses, infer thatit is theclear reduced that the sensitivity Hyperspectral associated Smartphone with the is capable edges of of the capturing spectral accuraterange of spectral the diffraction datasets grating that are may comparable be resulting with in the some existing data lossliterature in this in region. this field. Despite these losses, it is clear that the Hyperspectral Smartphone is capable of capturing accurate spectral datasets that are comparable with the existing literature in this field.

Figure 3. The observed spectral response curves of a healthy apple over the five-day measurement Figureperiod. 3. NoteThe observed the general spectral increase response in reflectance curves of over a healthy the measurement apple over the period five-day and measurement the absorption period.features Note present the general at ca. 550 increase nm and in ca. reflectance 650 nm. over the measurement period and the absorption features present at ca. 550 nm and ca. 650 nm. The abilities of the Hyperspectral Smartphone are further illustrated in Figure4, whichThe demonstrates abilities of the clear Hyperspectral spatial data collectionSmartphone that are clearly further highlights illustrated the in variations Figure 4, in whichpigmentation demonstrates across clear the apple’sspatial data surface collection and correlates that clearly well highlights with the observedthe variations spectral in pigmentationresponse curve. across The the clarity apple’s of surface the datasets and co obtainedrrelates well using with the the automated observed translation spectral stage highlights the significant potential of this device, and indeed smartphone-based hyperspectral devices as a whole.

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J. Imaging 2021, 7, 136 response curve. The clarity of the datasets obtained using the automated translation 7stage of 13 highlights the significant potential of this device, and indeed smartphone-based hyperspectral devices as a whole. response curve. The clarity of the datasets obtained using the automated translation stage highlights the significant potential of this device, and indeed smartphone-based hyperspectral devices as a whole.

Figure 4. Spatial datasets of the apple across different wavelengths. Note the variations in pigments

acrossFigure the4. Spatial spectrum. datasets of the apple across different wavelengths. Note the variations in pigments acrossFigure 4.the Spatial spectrum. datasets of the apple across different wavelengths. Note the variations in pigments across the spectrum. 3.1.2. Volcanic Rock Mineralogy 3.1.2. VolcanicVolcanic Volcanic Rock rock Rock Mineralogy images Mineralogy were also captured with the Hyperspectral Smartphone. These imagesVolcanicVolcanic were rock acquired rock images images as were a means were also captured also of demonstrating captured with the with Hyperspectral the the device’s Hyperspectral Smartphone. ability to Smartphone. identifyThese variations These andimagesimages feature were were acquired changes acquired as acrossa as means a means a of target demonstrating of object’sdemonstrating the surface. device’s the Figure ability device’s 5to shows identify ability the varia- to hyperspectralidentify varia- datationstions and capturedand feature feature changes of changes an obsidian across across a target flow a target object banded’s object surface. ash’s Figuresurface. tuff. As5 shows Figure Figure the 55 hyperspectral shows shows, the this hyperspectral target has cleardata captured captured variations of ofan present anobsidian obsidian acrossflow flowbanded its banded surface. ash tuff. ash As These tuff.Figure variationsAs 5 shows, Figure this are5 shows, target clearly has this clear replicated target has in clear the variations present across its surface. These variations are clearly replicated in the hyper- hyperspectralvariationsspectral data, present adding data, furtheracross adding support its further surface. to the support Thesecapabilities tovariations the of this capabilities low-cost are clearly design. of this replicated The low-cost band- in design. the hyper- The bandingspectraling across data, acrossthe rock adding the is easily rock further recognisable is easily support recognisable within to the the cahyperspectral withinpabilities the ofhyperspectral image, this low-costallowing image, for design. the allowing The band- for theingstraightforward straightforwardacross the rockidentification is identification easily of recognisable the individual of the individual withinflow bands the. flowhyperspectral bands. image, allowing for the straightforward identification of the individual flow bands.

Figure 5. Spatial dataset obtained of an obsidian flow banded ash tuff, clearly highlighting the indi- Figure 5. Spatial dataset obtained of an obsidian flow banded ash tuff, clearly highlighting the vidual flow bands. Hyperspectral dataset taken from ca. 600 nm. individual flow bands. Hyperspectral dataset taken from ca. 600 nm. To further demonstrate the spectral capabilities of this instrument, a sulphur rock Figure 5. Spatial dataset obtained of an obsidian flow banded ash tuff, clearly highlighting the indi- was imaged.To further Sulphur demonstrate was chosen due the to spectral its distinctive capabilities spectral ofresponse this instrument, where a distinct a sulphur rock vidual flow bands. Hyperspectral dataset taken from ca. 600 nm. wasincrease imaged. in reflectance Sulphur is evident was chosen from ca. due 500 nm to its[29]. distinctive Figure 6 shows spectral the spectral response response where a distinct increaseof Sulphur in collected reflectance by the is Hyperspectral evident from Smar ca. 500tphone. nm [This29]. figure Figure clearly6 shows shows the the spectral ex- response To further demonstrate the spectral capabilities of this instrument, a sulphur rock ofpected Sulphur increase collected in reflectance by thefrom Hyperspectral ca. 500 nm, emphasising Smartphone. the capabilities This figureof this instru- clearly shows the wasment. imaged. The additional Sulphur variations was chosen across the due spectr to umits distare likelyinctive to bespectral the result response of variations where a distinct expected increase in reflectance from ca. 500 nm, emphasising the capabilities of this present across the surface of the sulphur target used due to the presence of colour varia- instrument.increase in reflectance The additional is evident variations from ca. across 500 nm the [29]. spectrum Figure 6 are shows likely the to spectral be the response result of oftions Sulphur across thecollected rock’s surface, by the as Hyperspectral highlighted in FigureSmar tphone.7. However, This the figure key reflectance clearly shows the ex- J. Imaging 2021, 7, x FOR PEER REVIEWvariations present across the surface of the sulphur target used due to the8 of presence14 of feature at ca. 500 nm remains prominent within the captured dataset. colourpected variationsincrease in acrossreflectance the rock’s from surface,ca. 500 nm, as highlightedemphasising in the Figure capabilities7. However, of this the instru- key reflectancement. The additional feature at ca.variations 500 nm across remains the prominent spectrum are within likely the to captured be the result dataset. of variations present across the surface of the sulphur target used due to the presence of colour variations across the rock’s surface, as highlighted in Figure 7. However, the key reflectance feature at ca. 500 nm remains prominent within the captured dataset.

Figure 6. Observed spectral curve for a sulphur target. Note the clear increase in reﬂectance Figure 6. Observed spectral curve for a sulphur target. Note the clear increase in reflectance from ca.from 500 ca.nm. 500 nm.

Figure 7. Spatial datasets for the Sulphur target highlighting the variations in reflectance across the different wavelengths.

3.2. Comparisons to A Pre-Existing Low-Cost Imager To examine the capabilities of the Hyperspectral Smartphone in more detail, we compared them directly to the low-cost hyperspectral device presented in Stuart et al. [1]. Ta- ble 2 provides a direct comparison between the Hyperspectral Smartphone and the laboratory-based hyperspectral imager. As Table 2 highlights, both instruments represent valuable, low-cost hyperspectral sensors that can be deployed with relative ease and without the need for extensive additional set-up time. Both devices are capable of a range of image capture scenarios and allow the operator to vary the image dimensions to fit the target scene, either through manually editing the dimensions prior to scene capture (Laboratory- based Hyperspectral Imager) or through the extension of scene capture sweeps (Hyper- spectral Smartphone). Whilst the Hyperspectral Smartphone is constrained to the settings available within the built-in smartphone software, it represents a significantly cheaper and therefore more accessible, hyperspectral imaging sensor, when compared to the laboratory-based imager. Furthermore, the presence of an auto-focus feature within the smartphone software allows the Hyperspectral Smartphone to be especially user friendly. However, its spectral range is limited to the visible spectrum, whereas the laboratory- based imager is capable of a broader spectral range and can be converted to cover different regions of the spectrum, such as infrared, with relative ease [1]. The prominent differences present between both instruments are largely a result of the cost of the components involved and their intended research area. The laboratory-based hyperspectral imager represents a more specialised instrument in this comparison because it is typically suited to bench-top laboratory analysis, whereas the Hyperspectral Smartphone represents a more accessible design that can be implemented in a range of settings outside the laboratory. As such, these differences are clearly represented in the set-up costs associated with each device.

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J. Imaging 2021, 7, 136 8 of 13 Figure 6. Observed spectral curve for a sulphur target. Note the clear increase in reflectance from ca. 500 nm.

Figure 7. SpatialFigure datasets 7. Spatial for datasetsthe Sulphur for thetarget Sulphur highlighting target the highlighting variations thein reflectance variations across in reflectance the different across wavelengths. the different wavelengths. 3.2. Comparisons to A Pre-Existing Low-Cost Imager 3.2. Comparisons to a Pre-Existing Low-Cost Imager To examine the capabilities of the Hyperspectral Smartphone in more detail, we com- To examine the capabilities of the Hyperspectral Smartphone in more detail, we pared them directly to the low-cost hyperspectral device presented in Stuart et al. [1]. Ta- compared them directly to the low-cost hyperspectral device presented in Stuart et al. [1]. ble 2 provides a direct comparison between the Hyperspectral Smartphone and the labor- Table2 provides a direct comparison between the Hyperspectral Smartphone and the atory-based hyperspectral imager. As Table 2 highlights, both instruments represent val- laboratory-based hyperspectral imager. As Table2 highlights, both instruments represent uable, low-cost hyperspectral sensors that can be deployed with relative ease and without valuable, low-costthe need hyperspectral for extensive sensors additional that set-up can be time. deployed Both devices with are relative capable ease of a and range of image without the needcapture for extensive scenarios additional and allow set-up the operator time. Both to vary devices the image are capable dimensions of a range to fit the target of image capturescene, scenarios either through and allow manually the operator editing the to varydimensions the image prior dimensions to scene capture to fit (Laboratory- the target scene,based either Hyperspectral through manually Imager) editingor through the dimensionsthe extension prior of scene to scene capture capture sweeps (Hyper- (Laboratory-basedspectral Hyperspectral Smartphone). Imager) Whilst or the through Hyperspectral the extension Smartphone of scene is capture constrained sweeps to the settings (Hyperspectralavailable Smartphone). within Whilst the built-in the Hyperspectral smartphone software, Smartphone it represents is constrained a significantly to the cheaper settings availableand withintherefore the more built-in accessible, smartphone hyperspectral software, imaging it represents sensor, awhen significantly compared to the la- cheaper and thereforeboratory-based more accessible, imager. Furthermore, hyperspectral the imaging presence sensor, of an when auto-focus compared feature to within the the laboratory-basedsmartphone imager. software Furthermore, allows the Hyperspectral presence of an Smartphone auto-focus feature to be especially within the user friendly. smartphone softwareHowever, allows its spectral the Hyperspectral range is limited Smartphone to the visible to be especiallyspectrum, userwhereas friendly. the laboratory- However, its spectralbased imager range isis limitedcapable to of thea broader visible spectral spectrum, range whereas and can the be laboratory-based converted to cover different imager is capableregions of a broader of the spectrum, spectral rangesuch as and infrared, can be with converted relative to ease cover [1]. different The prominent regions differences of the spectrum,present such asbetween infrared, both with instruments relative ease are [ 1largely]. The prominenta result of differencesthe cost of the present components in- between both instrumentsvolved and their are largelyintended a resultresearch of thearea. cost The of laboratory-based the components hyperspectral involved and imager rep- their intendedresents research a more area. specialised The laboratory-based instrument in hyperspectralthis comparison imager because represents it is typically a suited to more specialisedbench-top instrument laboratory in this analysis, comparison whereas because the itHy isperspectral typically suited Smartphone to bench-top represents a more laboratory analysis,accessible whereas design the that Hyperspectral can be implemented Smartphone in a representsrange of settings a more outside accessible the laboratory. design that canAs be such, implemented these differences in a range are of clearly settings represent outsideed the in laboratory. the set-up Ascosts such, associated these with each differences aredevice. clearly represented in the set-up costs associated with each device. Figure8 provides a side-by-side comparison of apple targets captured by both instruments. As this figure demonstrates, both the Hyperspectral Smartphone and the laboratory-based hyperspectral imager are capable of detecting pigment variations across this type of target. When comparing the two spatial datasets, the Hyperspectral Smart- phone appears to be capable of greater image resolution, however, it should be noted that the optical system present within the laboratory-based instrument represents a basic set-up [1], and is, therefore, capable of improved image clarity, subject to the inclusion of an upgraded lens system. The Hyperspectral Smartphone benefits from the built-in optical system present within the chosen smartphone. As most smartphones are now capable of high quality image capture, it makes sense that the Hyperspectral Smartphone would be superior in the context of this comparison. J. Imaging 2021, 7, 136 9 of 13

Table 2. Direct comparison between the Hyperspectral Smartphone and the laboratory-based hyperspectral imager.

Hyperspectral Smartphone Laboratory-Based Hyperspectral Imager Imaging Mode Push Broom Whiskbroom Approximate Cost of Instrument 1 ~£100 <£6000 Image Capture Dimensions Variable—can be modified by the operator. Variable—can be modified by the operator. Spectral Range (nm) 400–700 340–850 Spectral Resolution (FWHM nm) 14 12 Limited. Exposure settings can only be Variable. Exposure settings and image Operator Input Options modified within the constraints of the parameters can be modified by the built-in smartphone software. operator. Highly portable. Can be deployed Limited portability within a laboratory Potential Portability anywhere with sufficient lighting. Capable setting. of both indoor and outdoor data capture. Additional illumination required for indoor Additional LED illumination required. Additional Equipment Required for data collection, e.g., an LED lamp. A Target object placed in a low-cost Successful Data Capture fluorescent light is required to complete the integrating sphere during data capture. data calibration process. J. Imaging 2021, 7, x FOR PEER REVIEW 10 of 14

1 These values represent the cost associated with the spectral set-ups and do not include potential additions such as LED lamps. However, these additions are often commonplace within laboratory environments or can be easily purchased for minimal additional cost.

Figure 8. Side-by-sideFigure 8. Side-by-side comparison comparisonof apple targets of apple captured targets across captured different across wavelengths different wavelengths by both the by Hyperspectral both Smartphone the(top Hyperspectral dataset) and the Smartphone laboratory-based (top dataset) hyperspect and theral laboratory-based imager (bottom hyperspectral dataset). Note, imager that the (bottom apple target is placed withindataset). a low-cost Note, integrating that the sphere apple during target isdata placed capture within with a the low-cost laboratory-based integrating instrument. sphere during data capture with the laboratory-based instrument. 3.3. A Field-Portable Low-Cost Hyperspectral Imager 3.3. A Field-PortableThe Low-Cost above sections Hyperspectral have Imagerdemonstrated the Hyperspectral Smartphone to be a valua- The aboveble sections low-cost have instrument demonstrated capable the Hyperspectralof high quality, Smartphone accurate hyperspectral to be a valuable data collection low-cost instrumentwithin a capable laboratory of high setting. quality, We accuratenow aimed hyperspectral to further test data its collection capabilities within as a robust, field- a laboratory setting.portable We instrument. now aimed To to do further this, the test Hyperspectral its capabilities Smartphone, as a robust, ﬁeld-portable with the translation stage, instrument. Towas do mounted this, the onto Hyperspectral a tripod to Smartphone,allow for stable with data the capture translation and the stage, translation was stage was mounted ontoconverted a tripod to to battery allow forpower. stable Figure data capture9 shows andthe Hyperspectral the translation Smartphone stage was in the field converted toready battery for power. data collection. Figure9 shows the Hyperspectral Smartphone in the ﬁeld ready for data collection.

Figure 9. The Hyperspectral Smartphone ready for data collection within a field setting.

In the field, a variety of targets were imaged in order to determine the Hyperspectral Smartphone’s abilities in a field setting at a range of different working distances. For this data collection, measurements were completed within Weston Park, Sheffield. This site was chosen due to the wide range of potential targets available without the need for ex- tended travel, allowing for initial field tests to be completed under COVID-19 travel restrictions. Datasets were captured at both short ca. 1 m and longer ca. 20 m working distances. Figure 10 shows the results obtained over a short working distance for a distinct target, in this case a sign within the park. It is clear from this figure that the Hyperspectral Smartphone is capable of clearly defining target object features, in this instance replicating the writing clearly within the spatial data.

J. Imaging 2021, 7, x FOR PEER REVIEW 10 of 14

Figure 8. Side-by-side comparison of apple targets captured across different wavelengths by both the Hyperspectral Smartphone (top dataset) and the laboratory-based hyperspectral imager (bottom dataset). Note, that the apple target is placed within a low-cost integrating sphere during data capture with the laboratory-based instrument.

3.3. A Field-Portable Low-Cost Hyperspectral Imager The above sections have demonstrated the Hyperspectral Smartphone to be a valuable low-cost instrument capable of high quality, accurate hyperspectral data collection within a laboratory setting. We now aimed to further test its capabilities as a robust, field- portable instrument. To do this, the Hyperspectral Smartphone, with the translation stage, J. Imaging 2021, 7, 136 was mounted onto a tripod to allow for stable data capture and the translation stage10 ofwas 13 converted to battery power. Figure 9 shows the Hyperspectral Smartphone in the field ready for data collection.

Figure 9.9. The Hyperspectral Smartphone readyready forfor datadata collectioncollection withinwithin aa ﬁeldfield setting.setting.

InIn thethe field,field, aa variety ofof targets werewere imagedimaged inin orderorder toto determinedetermine thethe HyperspectralHyperspectral Smartphone’s abilities in in a a field field setting setting at at a arange range of ofdifferent different working working distances. distances. For Forthis thisdata data collection, collection, measurements measurements were were completed completed within within Weston Weston Park, Park, Sheffield. Sheffield. This This site sitewas waschosen chosen due dueto the to thewide wide range range of potentia of potentiall targets targets available available without without the theneed need for forex- extendedtended travel, travel, allowing allowing for for initial initial field field test testss to tobe becompleted completed under under COVID-19 COVID-19 travel travel re- restrictions.strictions. Datasets Datasets were were captured captured at both at both short short ca. ca.1 m 1 and m and longer longer ca. 20 ca. m 20 working m working dis- distances.tances. FigureFigure 10 10showsshows the the results results obtained obtained over over a ashort short working working distance distance for a distinct target,target, inin thisthis casecase aa signsign withinwithin thethe park.park. It is clear from this figurefigure thatthat thethe HyperspectralHyperspectral J. Imaging 2021, 7, x FOR PEER REVIEW 11 of 14 Smartphone isis capable of clearly definingdefining target objectobject features,features, inin thisthis instance replicating thethe writingwriting clearlyclearly withinwithin thethe spatialspatial data.data.

Figure 10.10. SpatialSpatial datasetdataset captured captured of of a signa sign within within Weston Weston Park Park over over a short a short working working distance. distance. Note theNote clarity the clarity of the of writing. the writing.

More intricateintricate targetstargets werewere alsoalso imagedimaged atat thisthis workingworking distance.distance. FigureFigure 1111 showsshows thethe spatial andand spectralspectral data data collected collected from from a sectiona section of of a ﬂowera flower bed. bed. Whilst Whilst some some of theof the details de- oftails the of target the target are lost arewithin lost within the spatial the spatial datasets, datasets, the spectral the spectral response response variations variations remain re- clearmain followingclear following the expected the expected response response from this from particular this particular target. These target. datasets These show datasets the Hyperspectralshow the Hyperspectral Smartphone Smartphone to be a valuable to be a short-range valuable short-range ﬁeld instrument. field instrument.

Figure 11. Spatial datasets across different wavelengths for a section of flower bed acquired over a short working distance.

Finally, the Hyperspectral Smartphone was used to capture landscape-style datasets over a longer working distance. Figure 12 shows a spatial dataset captured over this range. Whilst this dataset is poor in comparison to the other data provided within this article, it is evident that there remains potential for the acquisition of these landscape-style datasets with this style of device. From these datasets it is clear that, in its current format, the Hy- perspectral Smartphone is more suited to shorter range data capture, however, the preliminary data captured over longer distances shows promise, suggesting that with some minor modifications to the existing instrument landscape scenes may prove possible to accurately capture with this style of low-cost hyperspectral system.

J. Imaging 2021, 7, x FOR PEER REVIEW 11 of 14

Figure 10. Spatial dataset captured of a sign within Weston Park over a short working distance. Note the clarity of the writing.

More intricate targets were also imaged at this working distance. Figure 11 shows the spatial and spectral data collected from a section of a flower bed. Whilst some of the de- J. Imaging 2021, 7, 136 tails of the target are lost within the spatial datasets, the spectral response variations11 of re- 13 main clear following the expected response from this particular target. These datasets show the Hyperspectral Smartphone to be a valuable short-range field instrument.

Figure 11.11. Spatial datasets across different wavelengthswavelengths forfor aa sectionsection ofof ﬂowerflower bedbed acquiredacquired overover aa shortshort workingworking distance.distance.

Finally, thethe HyperspectralHyperspectral SmartphoneSmartphone waswas usedused toto capturecapture landscape-stylelandscape-style datasetsdatasets over a longer working distance. Figure 12 showshowss a spatial dataset captured over this range. Whilst this dataset is poor in comparison to the other data provided within this article, it is Whilst this dataset is poor in comparison to the other data provided within this article, it evident that there remains potential for the acquisition of these landscape-style datasets is evident that there remains potential for the acquisition of these landscape-style datasets with this style of device. From these datasets it is clear that, in its current format, the with this style of device. From these datasets it is clear that, in its current format, the Hy- Hyperspectral Smartphone is more suited to shorter range data capture, however, the perspectral Smartphone is more suited to shorter range data capture, however, the pre- preliminary data captured over longer distances shows promise, suggesting that with some liminary data captured over longer distances shows promise, suggesting that with some J. Imaging 2021, 7, x FOR PEER REVIEWminor modiﬁcations to the existing instrument landscape scenes may prove possible12 of to14 minor modifications to the existing instrument landscape scenes may prove possible to accurately capture with this style of low-cost hyperspectral system. accurately capture with this style of low-cost hyperspectral system.

FigureFigure 12. Spatial dataset dataset captured captured of of a astatue statue over over a alonger longer working working distance distance (ca. (ca. 20 m). 20 m).Whilst Whilst this thisdataset dataset is of ispoorer of poorer quality quality than thanothers others captured captured with withthis instrument, this instrument, the target the target object object can be can iden- be tified highlighting the potential for the capture of landscape-style scenes in the future. identiﬁed highlighting the potential for the capture of landscape-style scenes in the future.

4.4. Conclusions InIn thisthis article, article, we we have have documented, documented, to the bestto the of ourbest knowledge, of our knowledge, the ﬁrst smartphone the first capablesmartphone of hyperspectral capable of hyperspectral data collection data without collection the need without for extensive the need post for processing. extensive post We haveprocessing. discussed We thehave design discussed and buildthe design process and required build process to convert required a standard to convert smartphone a stand- intoard smartphone a visible spectrum into a visible hyperspectral spectrum imager hyperspectral for ca. £100.imager Its for abilities ca. £100. have Its abilities been tested have inbeen a range tested of in imaging a range scenariosof imaging and scenarios we have and provided we have aprovided robust metrology a robust metrology as well as as a thoroughwell as a thorough comparison comparison against the against existing the literature existing and literature a pre-existing and a pre-existing low-cost hyperspec- low-cost tralhyperspectral imager. The imager. spectral The bandwidth spectral bandwidth and spectral and resolution spectral resolution of the smartphone of the smartphone imaging systemimaging is system comparable is comparable to that of to the that other of th low-coste other imager.low-cost Both imager. have Both variable have imagingvariable resolutionsimaging resolutions afforded byafforded the scanning by the nature scanning of the nature systems, of the and systems, although and the although smartphone the systemsmartphone has a system ﬁnite vertical has a finite image vertical resolution image due resolution to the number due to the of pixelsnumber on of the pixels sensor, on the sensor, all these pixels scan the scene simultaneously, significantly decreasing imaging time in comparison to the lab setup. Portability is paramount in agricultural and environmental monitoring applications. It is clear that the Hyperspectral Smartphone is capable of accurate spectral and spatial data collection and has the potential for future deployment within a range of environmental monitoring applications, with the additional benefits of its portability and user-friendly setup, making it a valuable instrument for more remote field campaigns. The results obtained by the Hyperspectral Smartphone demonstrate the significant potential available in the field of smartphone-based hyperspectral imaging and, as such, represents a valuable addition to currently available field-portable hyperspectral technologies, providing a solid foundation for future development and improvements in this area of research.

Author Contributions: This paper was written by M.B.S., A.J.S.M., M.D., M.J.H., N.A.B., L.R.S., C.Z., T.D.P. and J.R.W. The correction software was developed by L.R.S. and N.A.B. Experimental investigation and imager testing were completed by M.B.S., M.J.H. and M.D. Project supervision was provided by J.R.W. and A.J.S.M. All authors have read and agreed to the published version of the manuscript. Funding: This work was supported by the Engineering and Physical Sciences Research Council (EPSRC) fellowship EP/M009106/1 and doctoral training grant scholarship EP/R513313/1. Data Availability Statement: All relevant data are shown in the paper or could be recreated by following the methodology in the paper. Acknowledgments: Andrew J. S. McGonigle acknowledges support from the Rolex Institute. Conflicts of Interest: The authors declare no conflicts of interest.

J. Imaging 2021, 7, 136 12 of 13

all these pixels scan the scene simultaneously, significantly decreasing imaging time in comparison to the lab setup. Portability is paramount in agricultural and environmental monitoring applications. It is clear that the Hyperspectral Smartphone is capable of accurate spectral and spatial data collection and has the potential for future deployment within a range of environmental monitoring applications, with the additional benefits of its portability and user-friendly setup, making it a valuable instrument for more remote field campaigns. The results obtained by the Hyperspectral Smartphone demonstrate the significant potential available in the field of smartphone-based hyperspectral imaging and, as such, represents a valuable addition to currently available field-portable hyperspectral technologies, providing a solid foundation for future development and improvements in this area of research.

Author Contributions: This paper was written by M.B.S., A.J.S.M., M.D., M.J.H., N.A.B., L.R.S., C.Z., T.D.P. and J.R.W. The correction software was developed by L.R.S. and N.A.B. Experimental investigation and imager testing were completed by M.B.S., M.J.H. and M.D. Project supervision was provided by J.R.W. and A.J.S.M. All authors have read and agreed to the published version of the manuscript. Funding: This work was supported by the Engineering and Physical Sciences Research Council (EPSRC) fellowship EP/M009106/1 and doctoral training grant scholarship EP/R513313/1. Data Availability Statement: All relevant data are shown in the paper or could be recreated by following the methodology in the paper. Acknowledgments: Andrew J. S. McGonigle acknowledges support from the Rolex Institute. Conﬂicts of Interest: The authors declare no conﬂict of interest.

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