Food Waste Classification Using Artificial Intelligence

Yap, Jing Zong (2023) Food Waste Classification Using Artificial Intelligence. Final Year Project (Bachelor), Tunku Abdul Rahman University of Management and Technology.

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Abstract

Food waste classification is a challenging task due to the diverse nature of food waste, which comes in various shapes and forms. This project aims to develop a food waste classifier model that can accurately classify food waste into different categories. The objective is to determine appropriate approaches to the waste classification problem, and then build a food waste differentiating model. The ssd_mobilenet_v2_fpnlite_640x640_coco17_tpu-8 pre-trained model is used for the experiments because this model is specifically designed for object detection tasks, which involves identifying the presence and location of objects within an image or video, with a dataset consisting of four categories of food waste. The results show that the model achieved an accuracy of 74.1%, with room for further improvement through future work such as increasing the dataset size, fine-tuning the model, experimenting with different architectures, and incorporating user feedback. This project provides a foundation for future research into food waste classification and has practical implications for reducing food waste in various settings.