Land-based plastic waste, carried to the sea through rivers, is considered a main source of marine plastic pollution. However, most plastics that leak into the environment never make it into the ocean. Only a small fraction of plastics that are found in the terrestrial and aquatic compartments of river systems are emitted, and the vast majority can be retained for years, decades, and potentially centuries. In this perspective paper we introduce the concept of river systems as plastic reservoirs. Under normal conditions, hydrometeorological variables (such as wind, runoff and river discharge) mobilize, transport and deposit plastics within different river compartments (e.g., riverbanks, floodplains, lakes, estuaries). The emptying of these plastic reservoirs primarily occurs under extreme hydrological conditions (e.g., storms, floods). In this paper we specifically focus on the retention mechanisms within different river compartments, and their effect on the fate of the plastics that are accumulated on various timescales. We aim to introduce the concept of rivers as (long-term) sinks for plastic pollution, and provide suggestions for future research directions.

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Rivers as Plastic Reservoirs

A central challenge in microplastics (MP, diameter   250 μm, with drastic improvements in analysis time as compared with the best available technology, such as Fourier transform infrared (FT-IR) and Raman spectroscopy. Primary challenges we identified through the review include improving spatial resolution to detect smaller MP and development of robust models for data analysis. Parameters and practices for reporting quality assurance and quality control measures are summarized and recommendations are made for future research. We conclude that HSI is a promising technology for MP analysis but requires adaptation for this new application.

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Hyperspectral imaging as an emerging tool to analyze microplastics: A systematic review and recommendations for future development

Currently, a significant amount of research is focused on detecting Marine Debris and assessing its spectral behaviour via remote sensing, ultimately aiming at new operational monitoring solutions. Here, we introduce a Marine Debris Archive (MARIDA), as a benchmark dataset for developing and evaluating Machine Learning (ML) algorithms capable of detecting Marine Debris. MARIDA is the first dataset based on the multispectral Sentinel-2 (S2) satellite data, which distinguishes Marine Debris from various marine features that co-exist, including Sargassum macroalgae, Ships, Natural Organic Material, Waves, Wakes, Foam, dissimilar water types (i.e., Clear, Turbid Water, Sediment-Laden Water, Shallow Water), and Clouds. We provide annotations (georeferenced polygons/ pixels) from verified plastic debris events in several geographical regions globally, during different seasons, years and sea state conditions. A detailed spectral and statistical analysis of the MARIDA dataset is presented along with well-established ML baselines for weakly supervised semantic segmentation and multi-label classification tasks. MARIDA is an open-access dataset which enables the research community to explore the spectral behaviour of certain floating materials, sea state features and water types, to develop and evaluate Marine Debris detection solutions based on artificial intelligence and deep learning architectures, as well as satellite pre-processing pipelines.

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MARIDA: A benchmark for Marine Debris detection from Sentinel-2 remote sensing data

Despite recent advances in remote sensing of large accumulations of floating plastic debris, mainly in coastal regions, the quantification of individual macroplastic objects (>50 cm) remains challenging. Here, we have trained an object-detection algorithm by selecting and labeling footage of floating plastic debris recorded offshore with GPS-enabled action cameras aboard vessels of opportunity. Macroplastic numerical concentrations are estimated by combining the object detection solution with bulk processing of the optical data. Our results are consistent with macroplastic densities predicted by global plastic dispersal models, and reveal first insights into how camera recorded offshore macroplastic densities compare to micro- and mesoplastic concentrations collected with neuston trawls.

Quantifying Floating Plastic Debris at Sea Using Vessel-Based Optical Data and Artificial Intelligence

Close-range remote sensing-based detection and identification of macroplastics on water assisted by artificial intelligence: A review.

Airborne and spaceborne remote sensing (RS) collecting hyperspectral imagery provides unprecedented opportunities for the detection and monitoring of floating riverine and marine plastic debris. However, a major challenge in the application of RS techniques is the lack of a fundamental understanding of spectral signatures of water-borne plastic debris. Recent work has emphasised the case for open-access hyperspectral reflectance reference libraries of commonly used polymer items. In this paper, we present and analyse a high-resolution hyperspectral image database of a unique mix of 40 virgin macroplastic items and vegetation. Our double camera setup covered the visible to shortwave infrared (VIS-SWIR) range from 400 to 1700 nm in a darkroom experiment with controlled illumination. The cameras scanned the samples floating in water and captured high-resolution images in 336 spectral bands. Using the resulting reflectance spectra of 1.89 million pixels in linear discriminant analyses (LDA), we determined the importance of each spectral band for discriminating between water and mixed floating debris, and vegetation and plastics. The absorption peaks of plastics (1215 nm, 1410 nm) and vegetation (710 nm, 1450 nm) are associated with high LDA weights. We then compared Sentinel-2 and Worldview-3 satellite bands with these outcomes and identified 12 satellite bands to overlap with important wavelengths for discrimination between the classes. Lastly, the Normalised Vegetation Difference Index (NDVI) and Floating Debris Index (FDI) were calculated to determine why they work, and how they could potentially be improved. These findings could be used to enhance existing efforts in monitoring macroplastic pollution, as well as form a baseline for the design of future multispectral RS systems.

Advancing Floating Macroplastic Detection from Space Using Experimental Hyperspectral Imagery

Plastic pollution in aquatic ecosystems has increased dramatically in the last five decades, with strong impacts on human and aquatic life. Recent studies endorse the need for innovative approaches to monitor the presence, abundance, and types of plastic in these ecosystems. One approach gaining rapid traction is the use of multi‐ and hyperspectral cameras. However, most experiments using this approach were in controlled environments, making findings challenging to apply in natural environments. We present a method linking lab‐ and field‐based identification of macroplastics using hyperspectral data (1,150–1,675 nm). Experiments using riverbank‐harvested macroplastics were set up in a laboratory environment, and on the banks of the Rhine River. Representative pixel selections of eleven lab‐based images (n = 786,264 pixels) and two field‐based images (n = 40,289 pixels) were used to analyze the differences between these environments. Next, classifier algorithms such as support vector machines (SVM), spectral angle mapper (SAM) and spectral information divergence (SID) were applied, because of their robustness to varying light conditions and high accuracies in mapping spectral similarities. Our results showed that SAM classifiers are most robust in separating plastic pixels from background elements. By applying lab‐based data for plastic detection in field‐based images, user accuracies for plastics to up to 93.6% (n = 8,370 plastic pixels) were attained. This study provides key fundamental insights in linking lab‐based data to plastic detection in the field. With this paper we aim to contribute to the development of future spectral missions to detect and monitor plastic pollution in aquatic ecosystems.

Toward Robust River Plastic Detection: Combining Lab and Field‐Based Hyperspectral Imagery

Determining fresh tomato weight using depth images from an AR headset

Soil composition and herbivory are two environmental factors that can affect plant traits including flower traits, thus potentially affecting plant-pollinator interactions. Importantly, soil composition and herbivory may interact in these effects, with consequences for plant fitness. We assessed the main effects of aboveground insect herbivory and soil amendment with exuviae of three different insect species on visual and olfactory traits of Brassica nigra plants, including interactive effects. We combined various methodological approaches including gas chromatography/mass spectrometry, spectroscopy and machine learning to evaluate changes in flower morphology, colour and the emission of volatile organic compounds (VOCs). Soil amended with insect exuviae increased the total number of flowers per plant and VOC emission, whereas herbivory reduced petal area and VOC emission. Soil amendment and herbivory interacted in their effect on the floral reflectance spectrum of the base part of petals and the emission of 10 VOCs. These findings demonstrate the effects of insect exuviae as soil amendment on plant traits involved in reproduction, with a potential for enhanced reproductive success by increasing the strength of signals attracting pollinators and by mitigating the negative effects of herbivory.

Insect exuviae as soil amendment affect flower reflectance and increase flower production and plant volatile emission.

Apple scab is a fungal disease caused by Venturia inaequalis. Disease is of particular concern for growers, as it causes significant damage to fruit and leaves, leading to loss of fruit and yield. This article examines the ability of deep learning and hyperspectral imaging to accurately identify an apple symptom infection in apple trees. In total, 168 image scenes were collected using conventional RGB and Visible to Near-infrared (VIS-NIR) spectral imaging (8 channels) in infected orchards. Spectral data were preprocessed with an Artificial Neural Network (ANN) trained in segmentation to detect scab pixels based on spectral information. Linear Discriminant Analysis (LDA) was used to find the most discriminating channels in spectral data based on the healthy leaf and scab infested leaf spectra. Five combinations of false-colour images were created from the spectral data and the segmentation net results. The images were trained and evaluated with a modified version of the YOLOv5 network. Despite the promising results of deep learning using RGB images (P=0.8, mAP@50=0.73), the detection of apple scab in apple trees using multispectral imaging proved to be a difficult task. The high-light environment of the open field made it difficult to collect a balanced spectrum from the multispectral camera, since the infrared channel and the visible channels needed to be constantly balanced so that they did not overexpose in the images.

Joseph Peller

Papers

Advancing Floating Macroplastic Detection from Space Using Experimental Hyperspectral Imagery

Toward Robust River Plastic Detection: Combining Lab and Field‐Based Hyperspectral Imagery

Determining fresh tomato weight using depth images from an AR headset

Insect exuviae as soil amendment affect flower reflectance and increase flower production and plant volatile emission.

Apple scab detection in orchards using deep learning on colour and multispectral images