Solid Waste Management System based on CNN
This project automatically identify and classify different types of waste materials which can help to improve the efficiency and accuracy of waste sorting and recylcing.
Created on 19th February 2023
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Solid Waste Management System based on CNN
This project automatically identify and classify different types of waste materials which can help to improve the efficiency and accuracy of waste sorting and recylcing.
The problem Solid Waste Management System based on CNN solves
Environmental Pollution: oor solid waste management can lead to environmental pollution, which can have adverse effects on human health and the environment. A well-designed solid waste management system can help to reduce pollution and improve the overall environmental quality.
Health Risks: Improper disposal of solid waste can lead to the spread of diseases and pose health risks to people and animals. An effective solid waste management system can help to prevent the spread of disease and protect public health.
Resource Depletion: Solid waste contains valuable resources such as metals, plastics, and organic matter. A well-designed solid waste management system can help to recover these resources and reduce the demand for new raw materials.
Space Constraints: With increasing urbanization, space for landfill sites is becoming scarce. A solid waste management system can help to reduce the amount of waste sent to landfills, thus reducing the need for new landfill sites.
Challenges I ran into
Data Collection: In order to train a CNN for waste management, you need to collect a large amount of labeled data. This can be a time-consuming and expensive process. Additionally, collecting a representative sample of waste can be challenging, especially if waste varies significantly from one region to another. So finding a dataset that contains various categories of waste was a difficult task for me.
Data Quality: The quality of the data used to train the CNN is crucial to its accuracy. If the labeled data is of poor quality, it can lead to inaccurate results. It's important to ensure that the labeled data is consistent and accurate.
During the implementation of the idea in python, I got various syntax errors. Also due to inappropriate epochs and overfitting initially I cannot achieve enough accuracy. But after rectifying all those mistakes, now this model can accurately predict the categogy of the waste.
Technologies used