The Monitoring of Macroplastic Waste in Selected Environment with UAV and Multispectral Imaging

Plastic pollution is becoming an increasingly serious threat to the natural environment. Macroplastics, primarily polyethylene films, pose significant ecological and economic risks, particularly in the agricultural sector. Effective monitoring of their presence is necessary to evaluate the effectiveness of mitigation measures. Conventional techniques for identifying environmental contaminants, based on field studies, are often time-consuming and limited in scope. In response to these challenges, a study was conducted with the primary aim of utilizing unmanned aerial vehicles (UAVs), multispectral cameras, and classification tools to monitor macroplastic pollution. The model object for the study was an industrial compost pile. The performance of four object-oriented classifiers—Random Forest, k-Nearest Neighbor (k-NN), Maximum Likelihood, and Minimum Distance—was evaluated to effectively identify waste contamination. The best results were achieved with the k-NN classifier, which recorded a Matthews Correlation Coefficient (MCC) of 0.641 and an accuracy (ACC) of 0.891. The applied classifier identified a total 37.35% of the studied compost pile’s surface as contamination of plastic. The results of the study show that UAV technology, combined with multispectral imaging, can serve as an effective and relatively cost-efficient tool for monitoring macroplastic pollution in the environment.