Discover how image processing technology can enhance compost management in residential settings. This paper presents a novel dataset and the application of state-of-the-art semantic segmentation models to assess the nutrient quality of compost derived from kitchen waste. Highlights include:

• High-resolution segmentation of food waste categories,
• Benchmarking of models to determine the optimal approach for nutrient estimation,
• Detailed analysis of nutrient levels in different types of food waste.

This innovative technology could revolutionize home composting solutions, providing users with real-time feedback on the nutritional value of their compost, driving more informed composting practices. Further development could enhance nutrient recovery, linking dietary patterns to waste management.