AI-Based Smart Waste Segregation and Recycling Assistant

Artificial Intelligence (AI) and Machine Learning

Correctly separating waste at the point where it is discarded remains a practical difficulty in recycling. When paper, glass, metals, plastics and organic or electronic waste are placed together, downstream recovery can become harder and more costly. Sorting by hand also requires time and labour, and people may be uncertain about the right disposal route for an unfamiliar item. This review paper presents an AI-Based Smart Waste Segregation and Recycling Assistant intended to help users identify waste and understand how to handle it. The proposed workflow accepts a photograph captured or uploaded by a user, applies an image-classification model, and returns a predicted waste category. Depending on the labels available in the training dataset, categories may include plastic, paper, glass, metal, organic material and electronic waste. The result is connected to practical segregation, recycling and disposal advice through a straightforward interface. Transfer learning with a pretrained deep-learning network is a possible implementation approach. Model quality should be assessed with accuracy, precision, recall, F1-score and a confusion matrix. Potential extensions include a recycling-facility locator, waste statistics, bin-level monitoring and a camera-assisted smart-bin prototype. The system is proposed as a way to encourage better waste-sorting decisions; its effectiveness must be established through implementation and testing.

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