| _version_ | 1866901554877956096 |
|---|---|
| author | Vinay. B Sandeep. B Bhargav Sai. Ch Kiran Kumar. N Devi. G Dr. Venkataramana. B |
| author_facet | Vinay. B Sandeep. B Bhargav Sai. Ch Kiran Kumar. N Devi. G Dr. Venkataramana. B |
| contents | The intensive urbanization has caused the solid waste production to grow dramatically and thus, manual segregation of waste becomes inefficient and prone to error. Misplaced segregation minimizes the effectiveness of recycling, and it elevates the pollution in the environment. The paper describes waste segregation and monitoring system with a machine learning that is able to recognize waste as biodegradable, recyclable, and non-recyclable. Classification of images is done through supervised learning methods that are trained on labeled waste samples. The proposed system will combine a real-time monitoring system to monitor the level of waste and segregation accuracy. Automated segregation causes less human involvement and enhances the efficiency of operations in waste management. Standard metrics are used to assess the performance index, as accuracy, precision, recall, and F1-score. The results of the experiment show that there is a higher classification reliability than when using the sorting method manually. It is a scalable system that can be implemented in the context of the smart cities. This strategy is a part of the sustainable waste management and resource optimization. |
| format | Recurso digital |
| id | zenodo_https___doi_org_10_5281_zenodo_18681730 |
| institution | Zenodo |
| language | |
| publishDate | 2026 |
| publisher | Zenodo |
| record_format | zenodo |
| spellingShingle | Machine Learning Based Waste Segregation and Monitoring System Vinay. B Sandeep. B Bhargav Sai. Ch Kiran Kumar. N Devi. G Dr. Venkataramana. B AI Machine Learning Waste Segregation Image Classification Smart Waste Management Solid Waste Monitoring Recycling Automation Environmental Sustainability Convolutional Neural Networks (CNN) Deep Learning Computer Vision Automated Waste Sorting Real time- monitoring The intensive urbanization has caused the solid waste production to grow dramatically and thus, manual segregation of waste becomes inefficient and prone to error. Misplaced segregation minimizes the effectiveness of recycling, and it elevates the pollution in the environment. The paper describes waste segregation and monitoring system with a machine learning that is able to recognize waste as biodegradable, recyclable, and non-recyclable. Classification of images is done through supervised learning methods that are trained on labeled waste samples. The proposed system will combine a real-time monitoring system to monitor the level of waste and segregation accuracy. Automated segregation causes less human involvement and enhances the efficiency of operations in waste management. Standard metrics are used to assess the performance index, as accuracy, precision, recall, and F1-score. The results of the experiment show that there is a higher classification reliability than when using the sorting method manually. It is a scalable system that can be implemented in the context of the smart cities. This strategy is a part of the sustainable waste management and resource optimization. |
| title | Machine Learning Based Waste Segregation and Monitoring System |
| topic | AI Machine Learning Waste Segregation Image Classification Smart Waste Management Solid Waste Monitoring Recycling Automation Environmental Sustainability Convolutional Neural Networks (CNN) Deep Learning Computer Vision Automated Waste Sorting Real time- monitoring |
| url | https://doi.org/10.5281/zenodo.18681730 |