SEPARATION OF SOLID WASTE USING CNN TECHNIQUE

Solid waste management is a critical issue in urban areas as it has an impact on public health, environmental sustainability, and economic growth. The segregation of waste is an essential step in the waste management process, as it enables the identification and separation of different types of waste, which can be further recycled or disposed of in an appropriate manner. However, manual segregation of waste is a time-consuming and error-prone task, and can result in inefficient waste management practices. To overcome this challenge, we use computer vision techniques, such as Convolutional Neural Networks (CNN), has emerged as a promising solution. CNNs are a type of deep learning algorithm that can automatically identify and classify objects in images, making them suitable for the classification of waste. This process of segregating waste using CNNs is performed by capturing images of waste using a camera. These images are then preprocessed to enhance their quality and normalize their size and orientation. The preprocessed images are then fed into a CNN model that has been trained on a dataset of waste images. This model identifies the different types of waste in the image and assigns them to specific categories, such as biodegradable and non-biodegradable waste.