As part of efforts to manage food waste sustainably, researchers provide a solution for estimating compost nutrients through AI-driven image segmentation and classification. A LILA home composter initiative where the quality of compost is assessed using AI to analyze nutrients from kitchen food waste images is presented.

• Develops high-resolution image dataset of kitchen food waste
• Benchmarks advanced semantic segmentation models for food waste categorization
• Enhances the compost nutrient assessment process, focusing on nitrogen, phosphorus, and potassium
• SegFormer with MIT-B5 backbone deemed most effective - mIoU of 67.09
• Delivers class-based results for comprehensive food waste analysis

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Incorporating AI into compost management symbolizes how cutting-edge technology can support environmental sustainability. This research may lead to more meticulous recycling practices that not only manage waste efficiently but also provide valuable insights into the nutritional value of compost, contributing to a tighter loop in the circular economy.