Microplastics can contaminate water owing to their small size. If aquatic biota consume microplastics, they disrupt their reproductive processes, digestive tracts, and development. This study aimed to identify microplastic waste from silkworms (Tubifex spp.) in the Brantas River. The study was conducted in a descriptive manner by collecting samples of microplastic waste from silkworms and examining the shape, type, amount, and percentage of microplastic abundance in the river. An FTIR test was used to determine the microplastic content. Using a Zeiss Axio Zoom.V16 at 50x magnification, microplastic particles from individual worms and worm samples were visually identified. Then, the 50% hot needle test was used to determine the composition of the plastic. A total of 263 microplastic particles were found in the worm samples. Silkworms (Tubifex spp.) in the Brantas River, Kediri City, were shown to contain four types of microplastics, namely fibers, filaments, fragments, and granules, which were dominated by filament particles with 49% filament content, 45% fiber, 5% fragments, and 1% granules. The microplastic polymers identified via FTIR were polyethylene and ethylene-polypropylene-diene copolymers. These microplastics can originate from plastic bags, used drinking bottles, rope fibers, and pieces of water hose, which are often found around the Brantas River. Silkworms found in the Brantas River contain microplastic waste from various pollution sources.

International audience | This paper presents a new Remote Hyperspectral Imaging System (RHIS) embedded on an Unmanned Aquatic Drone (UAD) for plastic detection and identification in coastal and freshwater environments. This original system, namely the Remotely Operated Vehicle of the University of Littoral Côte d’Opale (ROV-ULCO), works in a near-field of view, where the distance between the hyperspectral camera and the water surface is about 45 cm. In this paper, the new ROV-ULCO system with all its components is firstly presented. Then, a hyperspectral image database of plastic litter acquired with this system is described. This database contains hyperspectral data cubes of different plastic types and polymers corresponding to the most-common plastic litter items found in aquatic environments. An in situ spectral analysis was conducted from this benchmark database to characterize the hyperspectral reflectance of these items in order to identify the absorption feature wavelengths for each type of plastic. Finally, the ability of our original system RHIS to automatically recognize different types of plastic litter was assessed by applying different supervised machine learning methods on a set of representative image patches of marine litter. The obtained results highlighted the plastic litter classification capability with an overall accuracy close to 90%. This paper showed that the newly presented RHIS coupled with the UAD is a promising approach to identify plastic waste in aquatic environments.