Intelligent Waste Segregation Technique Using CNN

One of the main issues facing recycling systems in our country’s big cities is waste segregation. 62 million tons of trash are produced annually in India. Plastic garbage makes for 5.6 million tons of this total. Every year, this is recycled to a degree of around 60%. There are also 11.9 million tonnes of 43 million tonnes of solid garbage were recycled. Despite the figures sound wonderful, but the segregation of recyclable materials is a major issue in the recycling sector. prior to recycling or other trash management methods. In India, Currently, when garbage is collected from residences, it is not separated. So to sort this garbage, a large crew and much effort are required.Additionally, because to the presence of harmful compounds in the trash, those employed in this sector are vulnerable to a number of illnesses. Therefore, the goal is to increase productivity while reducing human interaction in the waste sorting process. The proposed study aims to develop a convolutional neural network-based image classifier that can recognize objects and determine the sort of garbage they contain. In this study, the model VGG16 was used to extract characteristics from photos, input them into a classifier, and generate predictions about how to tell one sort of garbage from another. In India, pollution from municipal solid waste has long been an issue. Every minute, garbage is produced by people. Solid waste management is made more challenging by ineffective waste segregation. Waste segregation may be facilitated and improved with computer vision. In order to categorize the waste categories of 8069 photos of municipal solid trash, the model uses CNN-based waste-type classifiers (VGG-16). The model will investigate how well four CNN architectures categorize wastes.