Automatic Waste Segregator Based on IoT & ML Using Keras model and Streamlit
Keywords:
Automatic waste Segregator, Keras Model, Streamlit, Waste Management, Image Classification, Smart City Technology, Real-time Monitoring, Waste SortingAbstract
The creation of creative approaches to effective waste management has been motivated by the rising global concern for environmental sustainability. With the help of Streamlit, this project provides an Internet of Things (IoT) Waste Segregator system that combines real-time data collecting, machine learning, and approachable visualization. The system's main goal is to automate garbage segregation, which will increase recycling rates and ease the burden on landfills. The sys consists of a sys of sensors and cameras putted in locations used for waste collection, allowing for the monitoring the level and classification of waste materials in real time. A deep learning model created with the Keras framework processes the data gathered by these sensors. The model has been trained to distinguish between several types of garbage, including organic, recyclable, and non-recyclable materials. With the use of a neural network for convolutional (CNN) architecture ResNet-101, the system is able to successfully learn and distinguish between different types of garbage based on visual signals. The RPA model trained sends mail to the respective authorities.
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