Automated Sorting: Mapping of selective collection disposal objects using artificial intelligence

Introdução: A inteligência artificial, especialmente na área de visão computacional, tem se destacado como uma ferramenta poderosa para diversas aplicações, incluindo a classificação de objetos. Neste estudo, desenvolvemos uma pesquisa aplicada que utiliza inteligência artificial para detectar e classificar objetos descartados como lixo em duas categorias principais: papel e metal. Método: A pesquisa se baseou em uma base de dados contendo cerca de 897 imagens de objetos descartados, sendo 448 imagens de papel e 449 imagens de metal. Utilizamos o modelo YOLOv5 (you only look once) para treinar e testar a detecção e classificação dos objetos. O YOLOv5 é conhecido por apresentar resultados promissores nesse tipo de tarefa. Resultados: Os resultados obtidos demonstraram que o modelo YOLOv5 apresentou um desempenho satisfatório na detecção e classificação dos objetos descartados. A precisão média alcançada foi de 0,88. Conclusão: O estudo mostra que o uso da inteligência artificial, por meio do modelo YOLOv5, é eficaz para detectar e classificar objetos descartados em categorias de reciclagem, como papel e metal. Essa abordagem pode contribuir significativamente para aprimorar o processo de coleta seletiva e a gestão de resíduos, promovendo práticas mais sustentáveis e conscientes em relação ao meio ambiente.Introduction:Artificial intelligence, especially in the field of computer vision, has emerged as a powerful tool for various applications, including object classification. In this study, we developed an applied research that utilizes artificial intelligence to detect and classify discarded objects as waste into two main categories: paper and metal. Method:The research was based on a database containing approximately 897 images of discarded objects, with 448 images of paper and 449 images of metal. We used the YOLOv5 (you only look once) model to train and test the object detection and classification. YOLOv5 is known for providing promising results in this type of task. Results:The obtained results demonstrated that the YOLOv5 model exhibited satisfactory performance in detecting and classifying the discarded objects. The achieved mean average precision was 0.88. Conclusion:The study shows that the use of artificial intelligence, through the YOLOv5 model, is effective in detecting and classifying discarded objects into recycling categories, such as paper and metal. This approach can significantly contribute to improving the selective collection process and waste management, promoting more sustainable and environmentally-conscious practices

12,500+ and counting: biodiversity of the Brazilian Pampa

Knowledge on biodiversity is fundamental for conservation strategies. The Brazilian Pampa region, located in subtropical southern Brazil, is neglected in terms of conservation, and knowledge of its biodiversity is fragmented. We aim to answer the question: how many, and which, species occur in the Brazilian Pampa? In a collaborative effort, we built species lists for plants, animals, bacteria, and fungi that occur in the Brazilian Pampa. We included information on distribution patterns, main habitat types, and conservation status. Our study resulted in referenced lists totaling 12,503 species (12,854 taxa, when considering infraspecific taxonomic categories [or units]). Vascular plants amount to 3,642 species (including 165 Pteridophytes), while algae have 2,046 species (2,378 taxa) and bryophytes 316 species (318 taxa). Fungi (incl. lichenized fungi) contains 1,141 species (1,144 taxa). Animals total 5,358 species (5,372 taxa). Among the latter, vertebrates comprise 1,136 species, while invertebrates are represented by 4,222 species. Our data indicate that, according to current knowledge, the Pampa holds approximately 9% of the Brazilian biodiversity in an area of little more than 2% of Brazil’s total land. The proportion of species restricted to the Brazilian Pampa is low (with few groups as exceptions), as it is part of a larger grassland ecoregion and in a transitional climatic setting. Our study yielded considerably higher species numbers than previously known for many species groups; for some, it provides the first published compilation. Further efforts are needed to increase knowledge in the Pampa and other regions of Brazil. Considering the strategic importance of biodiversity and its conservation, appropriate government policies are needed to fund studies on biodiversity, create accessible and constantly updated biodiversity databases, and consider biodiversity in school curricula and other outreach activities