Manuscript received June 19, 2024; revised July 5, 2024; accepted August 6, 2024; published March 21, 2025.

Abstract—The escalating global concern over extensive food wastage necessitates innovative solutions to foster a net-zero lifestyle and reduce emissions. An effective home composter presents a convenient means of recycling kitchen scraps and daily food waste into nutrient-rich, high-quality compost. To capture the nutritional information of the produced compost, we have created and annotated a large high-resolution image dataset of kitchen food waste with segmentation masks of 19 nutrition-rich categories. Leveraging this dataset, we benchmarked four state-of-the-art semantic segmentation models on food waste segmentation, contributing to the assessment of compost quality of Nitrogen, Phosphorus, or Potassium. The experiments demonstrate promising results of using segmentation models to discern food waste produced in our daily lives. Based on the experiments, SegFormer, utilizing MIT-B5 backbone, yields the best performance with a mean Intersection over Union (mIoU) of 67.09. Class-based results are also provided to facilitate further analysis of different food waste classes.

Keywords—semantic segmentation, deep learning, food waste, compost, nutrients

Cite: Raiyan Rahman, Mohsena Chowdhury, Yueyang Tang, Huayi Gao, George Yin, and Guanghui Wang, "Kitchen Food Waste Image Segmentation and Classification f or Compost Nutrients Estimation," Journal of Image and Graphics, Vol. 13, No. 2, pp. 158-163, 2025.

Copyright © 2025 by the authors. This is an open access article distributed under the Creative Commons Attribution License (CC-BY-4.0), which permits use, distribution and reproduction in any medium, provided that the article is properly cited, the use is non-commercial and no modifications or adaptations are made.