1 Introduction

Plastic has become one of the most commonly used materials in everyday life in recent generations. Pollution due to plastics in natural environments is harmful to habitats, wildlife, and human health. With plastic waste washing up on coastlines and building up in the sand, plastic pollution on beaches in particular may be a serious issue. Plastic products are responsible for up to 80% of marine trash and are expected to enter the aquatic environment at a rate of 19–23 million tons per year, with this proportion expected to rise dramatically in the coming years (Derraik 2002; Borrelle et al. 2020; Gallo et al. 2018). These findings are based on structural model estimations due to reliable observational data, prompting several investigations into various monitoring strategies to improve the massive identification and quantification of aquatic litter loads and plastics accumulating on banks of rivers and shorelines.

Visual surveys and manual collection, which take a lot of time and labor, are currently used methods for finding plastic garbage on beaches. Several distinct strategies, such as aerial or satellite imagery, have been investigated in recent years as potential instrumental methods for marine remote sensing applications such as oil spill detection, habitat mapping, and marine litter detection (Kavanaugh et al. 2021; Silveira et al. 2021; Papakonstantinou et al. 2021; Topouzelis et al. 2020). With the advent of Artificial Intelligence algorithms, RGB imagery has proven sufficient to identify specific application types of plastic waste, further enhancing the efficiency of detection (Sami et al. 2020; Gnann et al. 2022; Tamin 2022). While these advancements hold promise, they are not without limitations, including constraints related to spectral resolution and comprehensive polymer differentiation (Houhou and Bocklitz 2021; Holzinger et al. 2023). These techniques can't differentiate between different kinds of debris and are constrained by their resolution (Themistocleous et al. 2020; Schwarz et al. 2019; Hibbitts et al. 2019). As a result, there is a need for more effective and precise techniques for locating plastic waste accumulating in the world's oceans and on its beaches. Plastics are renowned for their unique spectral characteristics, which play a pivotal role in their detectability within the realm of remote sensing applications (Martínez-Vicente et al. 2019). These characteristics are primarily attributed to their inherent optical properties. Plastics often exhibit distinct absorption features and high reflectance in specific spectral regions, which can be precisely identified through HSI systems (Tasseron et al. 2021). Hyperspectral imaging has established itself as a creative observational tool for studies conducted in imaging applications and research (Fearns et al. 2011; Ødegård et al. 2018; Attia et al. 2023). This technique creates a map of the spectrum variance, making it a useful resource for numerous applications. Plastic retrieval from HSI could be achieved through the use of spectral indices, which exploit the unique spectral characteristics of plastics. These indices are designed to highlight certain spectral features indicative of plastic materials, such as reflectance patterns at specific wavelengths (Prata et al. 2020; Kremezi et al. 2021). The primary goal of the HS mechanism is to enable categorization and grouping. Finding objects in a database that precisely meet a set of criteria is a necessary step in the classification process. Without the use of established patterns or any prior knowledge, clustering automatically finds things that are related. These processes are possible because different materials reflect different wavelengths of light based on how photons interact physically with different samples' chemical compositions. The quantity of reflected photons, known as reflectance (Moshtaghi et al. 2021; Knaeps et al. 2021), is a particular to a specific material. Therefore, the reflectance is also referred to as a material’s spectral optical signature (Kleynhans et al. 2020). HS spectrometers mounted on platforms like drones and aircraft are used to detect optical signals from the coastlines and distinguish between different elements using optical signatures. Utilizing non-destructive HSI offers several advantages over traditional methods, most notably the capability to simultaneously identify multiple constituents within a single sample (Garaba and Dierssen 2018; Corbari et al. 2020; Shu et al. 2021; An et al. 2022; He et al. 2021; Mei et al. 2022; Abdallah et al. 2022). In accordance with the advantages of HSI, this method is currently conquering many obstacles to be acknowledged as the frequently-used tool for identifying optical signatures of surface material properties and validating them (Grahn and Geladi 2007; Lindon et al. 2016). Many advanced studies on high-resolution aerial detection for aquatic plastics (PE, polypropylene, and polystyrene) use the diffuse reflectance approach, as in Balsi et al. (2021), Zhu et al. (2020), Balsi et al. (2018), Veenstra and Churnside (2012), Lechthaler et al. (2020). However, these studies have predominantly focused on the analysis of marine floating polymers using conventional broadband light sources within controlled laboratory settings or natural sunlight in open field conditions, without comprehensive investigations into potential environmental interference factors, such as the influence of beach sand. Other studies have discussed the ability to detect polymers using shortwave infrared (SWIR) imaging spanning the range of 900 to 1700 nm and identify them within this IR spectral band (Zhang et al. 2019; Shan et al. 2019). Reflectance features specific to plastic debris have been recorded at approximately 1215 and 1732 nm (Moshtaghi et al. 2021). Applying SWIR HS cameras are generally more expensive than the visible-near infrared (VIS–NIR) HS cameras, which may limit their accessibility for some research applications. SWIR sensors often use advanced detector materials like Indium Gallium Arsenide (InGaAs) to cover the SWIR spectrum. Unlike the silicon-based detectors commonly found in VIS–NIR cameras, these materials come at a higher cost (Togeiro de Alckmin et al. 2020; Mahmoud et al. 2016). Our choice to focus on the VIS–NIR spectral bands is influenced by both the research objectives and the practicality of using more accessible equipment. This decision allows us to explore cost-effective solutions while still addressing the critical challenges of plastic waste detection on beaches. Microplastics were discernible in HS images captured within the visible wavelength range from soil samples without necessitating an examination within the NIR or SWIR spectral bands, as discovered in Shan et al. (2018), which shows that when the wavelength was below 675 nm, white PE fragments exhibited the highest reflectance. Using our proposed LIF technology, it is feasible to overcome these numerous difficulties, including the interference from other beach materials and the requirement for precise identification of the type of litter. Laser-induced fluorescence is a non-destructive, non-linear optical approach that may identify compounds' fluorescence emission spectra (Mahmoud and El-Sharkawy 2023a, 2023b; Mahmoud et al. 2023; Elbasuney et al. 2024). A molecule can get excited and produce light at a longer wavelength after it absorbs light at a certain wavelength, creating a fluorescence emission spectrum that is unique to that molecule. The decision to opt for LIF as the methodology for our study is grounded in several compelling advantages that it offers over the traditional linear technique, which is based on diffuse reflection measurements. First and foremost, LIF is a non-linear technique that relies on the molecular material's emission properties, which are inherently unique for each material. This uniqueness allows LIF to discriminate between different materials with a level of precision that is often unattainable with linear techniques (Hassoun et al. 2020; Zeng et al. 2020; Taylor and Lai 2021). LIF can differentiate between plastic trash and other beach items by measuring the fluorescence emission spectra of several types of litters.

Our pilot study for this research was to integrate various types of beach marine debris materials, including polymers and wood, and then study the possibility of identifying them using LIF spectroscopy in the 400–1000 nm spectral band. This methodology has significantly advanced our ability to predict the appearance of an object within diverse and challenging environments. It has enabled successful recognition of various materials within complex sandy backgrounds. And lastly, beach trash and its spectral characteristics distinguish these targets, especially polymers, from the mixed natural minerals represented in quartz sand.

So, we have taken the first steps in our experimental work toward a physical laboratory setup for images acquired with a HS imager in this paper. We modeled the lamp as a point source positioned behind the HS camera, emitting light with a Gaussian angular distribution. Using our proposed setup, we successfully characterized the light's field of view. The proposed experimental setup consists of a reflectance model for various materials (plastic and wood) placed in a container full with beach sand. By initially lighting the samples with a broad-spectrum halogen lamp, scanning them with a HS camera, and detecting the spectral diffuse reflection bands of the studied litters using HS Analysis Software, we were able to determine the optimal laser wavelength suited for causing the plastic and wood samples to fluoresce. Subsequently, the fluorescence of waste plastics was stimulated more effectively by employing a laser source with a wavelength closely aligned with the diffuse reflection characteristics of the samples. This step was used to stimulate a unique fluorescence signature of the examined materials using LIF spectroscopy. A HS camera that individually display target brightness and irradiance data at a confined spectral bandwidth of about 5 nm was used to carefully measure the emission spectra of the studied items in the interim. The appropriately informative HS imagery extracts all of the optical features of the examined litters and records fluorescence signals. Our proposed approach could determine instantly the unique wavelength that could discriminate waste plastics.

Our goals were to create a high-resolution fluorescence emission library for plastics and wood in order to determine which wavelengths are most effective at distinguishing between litters around beach sand.

Our imaging method based on LIF support current efforts to categorize and assess the likelihood of these materials being found in various sandy marine environments that have an impact on marine pollution using remote sensing techniques in the 400–1000 nm spectral band, as well as provide a novel commercial approach for the development of future airborne and drone remote sensing systems for waste plastics detection on coastlines.

2 Materials and methods

In our characterization, most common litter materials found abundantly on beaches were considered. We selected both a plastic film and a plastic bag as LD-PE materials because the polymers represented in LD-PE were proportionally prevalent in all aquatic environments (Barboza et al. 2019; Li et al. 2016). We intentionally selected plastic waste samples without coloration. By using plastic waste without coloration, we aimed to create a standardized and consistent baseline for our study. This choice allows us to focus on the inherent fluorescence properties of plastics rather than the influence of varying colorants or additives, which can introduce spectral variations. Our pilot study shifts the focus from color-based differentiation to a more robust method based on LIF approach. We sought to develop a methodology that can be applied broadly to detect and identify plastics in various environmental conditions and scenarios. Colorless plastic waste serves as an excellent representation of plastics commonly encountered in both terrestrial and aquatic environments. Furthermore, transparent plastics pose a greater challenge for visual detection systems as they lack the distinct coloration that can aid in the visual identification of colored plastics. The absence of coloration in transparent plastics makes them particularly difficult to discern from surrounding materials, such as sand, wood, or other beach debris. The other investigated sample included dry and wet wood pieces. Based on gathering from public lab supplies, the three materials that were chosen were reachable with about 5 cm in length for the plastic film, plastic piece bag, or wood piece. A layer of quartz-rich sand from San Stifanu Beach (Alexandria, Egypt) that was put in a container with an 8 cm depth was used to achieve the sandy background for the tested samples, as shown in Fig. 1. This material setup enables us to study the sand signature effect on tested items and features our study contribution and its ability to classify different waste materials based on their fluorescence signatures using our customized LIF approach for the captured cube images.

Fig. 1
figure 1

The used samples for our waste martial classification study based on their LIF

Full size image

The laboratory studies on the litters were divided into two different stages. The first step involved using HS scanning and broad-spectrum lighting from a reasonable level of about 60 cm to determine the common absorption wavelengths of the three studied samples (a plastic film, a plastic bag piece, and a wood piece) over the researched VIS/NIR range. This range was chosen to encompass the visible and NIR regions of the electromagnetic spectrum, which are relevant for mimicking the solar radiation typically encountered in beach and coastal environments. The selection of this broadband light source was deliberate, as it allowed us to approximate the spectral characteristics of natural sunlight, which contains a broad range of wavelengths. By doing so, we aimed to capture the diverse optical properties of the tested materials, including plastics, wood, and sand, under conditions that closely resemble those encountered in the field. The specimens were irradiated with a laser beam whose wavelength falls within the optimal wavelength for discriminating between different materials, including wood, metal, and sand in the second phase, and optical signature measurements based on LIF were then detected using an HS imager at the same distance. Our objective in using this approach was to obtain a unique spectral signature for each material that would be instrumental in the discrimination process of litter on shores. The diversity of spectral responses captured under these conditions provides valuable data for subsequent analysis and discrimination algorithms, enabling us to distinguish between different materials more effectively.

3 Experimental results and analysis

3.1 Identification of the waste samples' distinctive diffuse reflection

To obtain HS cube image information for the two LD-PE polymers in addition to wood, we employed a HS (0.4–1.0 microns) line-scanning camera with 520 pixels per line and, on average, 696 lines per cube. Figure 2 depicts the imaging system for the studied litter diffuse reflectance computations. For our VIS/NIR analysis, we employed the SOC710 camera (Surface Optics Manufacturer, USA) with a spectral resolution of around 5 nm and 128 spectral channels, each representing a different wavelength. The optical lens employed had a field of view of 10° (Schneider Xenoplan, F/1.9, 35 mm focal length), which is adequate for a sharp image. To simulate a sandy beach environment, a quartz sand layer was put into a black polymer tank until the surface was about 40 cm under the lenses of the HS camera. The black container used to keep both litter samples and sand had very low reflectance values and had no effect on the reflectance values of the objects studied across the whole spectrum. Formerly, a broad-spectrum light source with a 0.4–1 micron wavelength range was employed. This kind of light is suitable for our tested HS camera due to its homogeneity. The light was focused on the samples at a distance of around 0.6 m from the optical bench and at an angle of 45 degrees from the SOC710 HS imager.

Fig. 2
figure 2

The exact configuration of the optical imaging system in use for diffuse reflected characteristics

Full size image

We calibrated the HS imager to produce the highest signal-to-noise ratio (S/N) output before beginning our experimental work. A spectral image obtained from a white reference sheet with a high reflectivity standard is used to determine the background response. To achieve the black effect, a non-reflective dark lid is completely placed over the camera lens. The formula below is then used to calculate the relative reflectance for the captured images using these two acquired reference images (Mahmoud and El-Sharkawy 2023; Aref et al. 2023).

$${{\text{I}}}_{fc}=\frac{{{\text{I}}}_{{\text{oc}}}-{{\text{I}}}_{{\text{Dc}}}}{{{\text{I}}}_{{\text{Bc}}}{-{\text{I}}}_{{\text{Dc}}}}$$
(1)

where Ifc is the corrected spectral response captured image, Ioc is the raw spectral response captured image, IDc is the dark captured image, and IBc is the white captured image. A device (laptop) that runs software (HS-Analysis TM Data Analysis) managed the linear scanner's motors, adjusted exposure, and gathered the diffuse reflectance characteristics data. This calibration process is integral to the success of our pilot study, where the accurate measurement of spectral signatures is fundamental to our approach for discriminating between various trash materials, including plastics, wood, and sand, within beach sand environments. By optimizing the S/N ratio through calibration, we enhance our ability to capture subtle spectral differences that may be indicative of specific materials, even in complex and variable field conditions. The distributed reflection spectra patterns for the tested litter materials (two different polymers with wood) mixed with sand in a container using the SOC710 HS camera are depicted in Fig. 3a. Figure 3b presents a comparison between our diffuse reflection measurements for the examined materials, inclusive of both dry and wet wood spectra, and the spectral responses obtained from the white reference measurements. This visual comparison highlights the distinct spectral characteristics exhibited by the materials within our study.

Fig. 3
figure 3

a The average dispersed reflectance spectral response characteristics of the tested materials (Plastics, Wood, and Sand) using the HS optical imager with a range of a wavelength of 400–1000 nm [ordinary units relative to the white reference (O.U.)]. b The average dispersed reflectance spectral response characteristics of the tested materials including both wet and dry wood with respect to the white reference measurements

Full size image

The inspection of the resulting cube picture, as seen in Fig. 3a, demonstrates high dispersed reflectance of the tested materials in the spectral band between 510 and 580 nm as well as in the range from after 400 nm to 460 nm. We could also notice that the value of intensity at a wavelength of 450 nm, which is considered the unique common classified frequency for plastics, is about 148 (O.U.) which is the highest value, while it is about 80 (O.U.) in the case of wood or sand. Each of the plastics, wood, and sand signatures could be characterized at a wavelength of about 580 nm. Both the wood and sand signatures have a higher intensity than the plastic signature at about 750 nm. The wavelengths, especially around 450 nm, offer optimal discrimination capabilities between plastics and wood or the sandy background, primarily due to significant differences in intensity. In Fig. 3b, a notable contrast becomes apparent when comparing the diffuse reflection curves of the investigated materials to the spectral response of the white reference standard. This contrast is indicative of a pronounced difference in the material properties within this spectral band interval. Specifically, the observed spectral curves suggest a significant degree of absorbance exhibited by these materials in this range. Notably, there is a striking similarity in the responses of both dry and wet wood, signifying a nearly matched spectral signature between these two conditions. This observation may underscore the robustness and reliability of our experimental procedures and spectral measurements, which demonstrated a relatively strong match in spectral characteristics regardless of the moisture content of the wood samples. Figure 4 shows the acquired images at these selected wavelengths for the investigated litter materials taken by the SOC710 HS imager at 450 nm, 580 nm, and 750 nm respectively.

Fig.4
figure 4

The acquired images for the investigated litter materials taken by the SOC710 HS imager for diffuse reflected characteristics study; a The picture taken at 450 nm by the HS imager; b The picture taken at 580 nm by the HS imager; c The picture taken at 750 nm by the HS imager

Full size image

As shown in Fig. 3 and by comparing the three pictures in Fig. 4, we could clearly detect plastic waste with respect to wood or sand in the blue spectrum of the studied diffuse reflected characteristics. The diffuse reflection data served as a crucial step in our study to select the appropriate wavelength that would enable effective discrimination between the litter materials. Due to this result, we made the decision to move on with LIF testing employing a laser source whose wavelength is about 400 nm (UV light), which is considered to be in the distinctive diffuse reflection spectrum of the plastic samples under study. It has been realized that stimulating at shorter wavelengths (400 nm) stimulates more bands, which enhances the process of identifying different litters in a sandy background environment.

3.2 Identification of the litter materials distinctive flourscence signatures

The wide-ranging light source was substituted in our LIF study by a 50-mW commercial UV, as shown in Fig. 5. The UV laser source used to excite the samples was roughly 60 cm away from the optical bench. This adjusted distance with the selected commercial laser ensures that the induced light completely covers the studied areas with the same optical path distances.

Fig. 5
figure 5

a The HS high-resolution imaging system configuration for marine litters analysis based on their fluorescence, b Litter investigation using proposed LIF approach

Full size image

As depicted in Fig. 5, the litter specimens react in response to the 400 nm UV source of excitation light. The HS camera computes both the light re-emitted and scattered spectra in order to select the appropriate wavelength and apply our algorithm to the immediate detection of plastic waste. Fluorescence occurs when molecules are excited by a steady source of light; the intensity of the fluorescence is proportional to the wavelength. Utilizing our LIF approach with a wavelength of about 400 nm, a significantly greater state was induced in the investigated litter samples. The re-emission of the fluorescence signature might allow the stimulated matter to return to its baseline state. All potential energy transitions in stimulated material may be represented by the photons that are emitted. The SOC710 HS imager can precisely measure the fluorescence signature. This signal can be used to identify a molecular structure. The fluorescent data was managed and collected by the running software (HS-Analysis TM Data Analysis). A comparison of the emission spectra pattern for all the studied litter materials (plastic film, wood piece, and plastic bag piece) is shown in Fig. 6.

Fig. 6
figure 6

The average discrepancy in nonlinear impact, fluorescence for all analyzed litter specimens; a using relative normal units; b using a logarithmic scale

Full size image

The typical responses of the plastics, wood, and sand for all examined litter specimens revealed their own fluorescence fingerprint across the range of 390–1050 nm. As illustrated in Fig. 6, the fluorescence fingerprint that results from stimulating the specimen with a UV laser source of about 400 nm differs depending on the litter item being examined. There is a distinct fluorescence signal for plastics at 450 nm. Wood demonstrated a unique fluorescence emission at 750 nm. We assessed our computations by taking the common re-emitted signal at 550 nm for the three investigated materials. The optimal re-emitted wavelengths (450 nm and 750 nm) chosen for plastics and wood characterization and selected from the SOC710 cube HS image to validate the relevant data are shown in Fig. 7.

Fig. 7
figure 7

Optical contrast characterization of examined litter samples surrounded with beach sand using LIF technique a the picture taken at 450 nm by the HS camera; b the picture taken at 750 nm by the HS camera; c the picture taken at 550 nm by the HS camera with no fluorescence emission for the three studied items (plastic, sand, and wood); d The image captured at 750 nm, showing no discernible emission difference between dry and wet wood

Full size image

Applying our proposed LIF methodology for plastic litter detection on shorelines, as illustrated in our experimental setup shown in Fig. 5 and their related findings shown in Figs. 6 and 7, it may be concluded that LD-PE plastics have their own distinct optical marker (Fig. 7a) and can be discriminated from wood or sand at 450 nm. We might also take advantage of the distinct wood fluorescence output relative to sand or plastic at 750 nm.

4 Discussion

Marine litter, especially plastic debris, is a critical environmental issue on a worldwide scale, threatening aquatic wildlife and ecology, habitats, livelihoods, aquaculture, maritime transit, tourism, and commerce (Kühn et al. 2015; Sa et al. 2018). Plastic materials have a significant effect on the environment since they take many years to disintegrate. Furthermore, poisonous compounds are absorbed by the soil when polymers decompose in light from the sun, and when it burned, a dangerous chemical is released into the atmosphere, generating atmospheric carbon emissions. Novel low cost-effective remote sensing ways to identify and describe plastic items are still being developed, though. Finding plastic trash on beaches can assist in tracking the success of mitigation measures and provide information for upcoming initiatives to minimize plastic waste and enhance waste management procedures (Hirai et al. 2011; Lebreton et al. 2018; Suaria et al. 2016; Wagner et al. 2014). Plastics exhibit distinctive spectral characteristics that set them apart from natural materials, forming the basis of their detectability in remote sensing applications. It often features high reflectance in specific spectral regions due to their optical properties, which can vary depending on factors such as the polymer type, surface texture, and pigmentation. These reflective properties result in unique spectral signatures that can be captured by HSI systems. It also demonstrates the fascinating property of fluorescence when exposed to specific excitation wavelengths, further enriching their spectral identity. Different types of plastics exhibit varied fluorescence responses, creating a spectral fingerprint that can be harnessed for precise identification. This combination of high reflectance and fluorescence underpins the potential for plastics to be distinguished from natural materials like wood and sand in remote sensing imagery. By exploiting these spectral attributes, we aim to develop an effective methodology for discriminating plastics amidst diverse waste materials in beach sand environments, contributing to the larger endeavor of mitigating plastic pollution in coastal ecosystems. In order to solve this worldwide issue and safeguard our natural resources, it is crucial to develop new technology and ways to identify the plastic waste on beaches. The interference from other beach materials and the necessity for accurate identification of the kind of litter can make it relatively difficult to identify discarded plastics on sand-surrounded beaches. The segmented image from the SOC710 HS imager for the three litter materials (plastic film, plastic bag piece, and wood piece) surrounded by beach sand is shown in Fig. 8.

Fig. 8
figure 8

The segmented picture taken by the HS imager on the investigated litters without applying proposed LIF approach

Full size image

As shown in Fig. 8, HSI and diffuse reflection measurements provide valuable insights into the spectral characteristics of materials, enabling researchers to discern various constituents on the beach. However, these methods alone may have limitations in their ability to accurately identify and differentiate polymers, especially in scenarios where multiple materials and environmental factors come into play. Our innovative LIF technique complements HSI by adding a critical dimension to the analysis-fluorescence signatures. The proposed LIF approach is superior to other spectroscopic methods in a number of ways. One benefit is its high sensitivity, which enables the identification of substances at low concentrations. Another benefit is its great selectivity, which makes it possible to identify specific substances because the fluorescence emission spectra are particular to each atom or molecule. LIF is also suited for real-time monitoring applications because of its quick reaction time. Exciting the investigated items with a laser source emitting UV light at a wavelength of about 400 nm enables us to detect the studied LD-PE polymers surrounded by sand with high-resolution images. Figure 9 shows the significant photoluminescence signature that was easily seen with the visual inspection of the plastic waste at 450 nm.

Fig. 9
figure 9

a The segmented pictures taken by the HS imager on the investigated litters after applying our proposed LIF approach, b Immediate plastic litter detection through blue light emission against a sandy background, c The blue fluorescence effect of the added yellow-colored plastic bag piece with the studied litters

Full size image

According to the results shown in Fig. 9, our novel waste plastic detection strategy based on LIF with a UV laser source could be implemented in marine remote sensing applications with a simplified and inexpensive imaging setup by replacing the HSI camera setup with a conventional RGB camera and a blue filter working at 450 nm wavelength, as illustrated in Fig. 10.

Fig. 10
figure 10

A novel simplified imaging setup for plastic waste detection using a conventional RGB camera and a blue filter

Full size image

5 Conclusion

There are still no viable, low-cost ways of determining the number and dispersion of polymers, which is necessary to comprehend their impact on ecosystems. Here, we efficaciously presented a new HS database for the target theater of operations (LD-PE polymers, wood, and sand) on a simulated beach environment, which can be investigated even more to enhance the thorough understanding of diffused spectral reflectance-based plastic and wood classification. It has been demonstrated that an effective and promising system for the rapid, remote, non-invasive, and exceptionally sensitive identification of the majority of marine debris, such as plastic and wood, may be created by combining the novel LIF approach with the HS camera. The first stage entailed determining the optimal wavelength for discriminating between the three investigated samples over the studied VIS/NIR range using HS imaging and broad-spectrum illumination. At wavelengths between 400 and 460 nm, imaging shows a strong sample absorbance difference. Following that, a customized LIF technique was developed to categorize the three litter samples investigated. UV laser excitation is used to do fluorescence experiments on a plastic film, a plastic bag piece, and a wood piece. Our HSI setup, in conjunction with the LIF methodology, demonstrated that methods for fluorescence spectra analysis allowed for accurate distinguishing between them based on the molecular structure differences between plastics, wood, and beach sand for UV laser-stimulated samples. While existing HS reflectance databases provide valuable reference information, our study introduces an innovative method to enhance the accuracy material identification in real-world, dynamic beach environments. We found that the fluorescence emission for waste plastic differentiation is 450 nm. This makes it possible to make even more progress toward the creation of sophisticated plastic or wood debris monitoring and categorization missions in the world's oceans and on its beaches. Our imaging LIF technique, which successfully detects plastic waste instantly, may be used to build airborne systems with a new low-cost detection mechanism using a modified conventional camera that only functions at a blue wavelength and an optical telescope for distance adaptation for marine plastic detecting applications.