The escalating global concern over extensive food wastage necessitates innovative solutions to foster a net-zero lifestyle and reduce emissions. The LILA 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.

面临全球对大量食物浪费的日益关注，我们需要创新的解决方案以促进零净排放生活方式并减少排放。LILA家庭堆肥机是将厨余和日常食物废物回收成富含营养、高质量堆肥的方便途径。通过创建和标注了一个包含19种富含营养类别的高分辨率厨房食物废物图像数据集的分割掩模，我们获取了生产堆肥的营养信息。利用这个数据集，我们对食物废物分割进行了四种最先进的语义分割模型的基准测试，以评估氮、磷或钾的堆肥质量。实验证明，使用分割模型来区分我们日常生活中产生的食物废物具有良好的效果。在实验基础上，使用MIT-B5骨干网路的SegFormer模型以67.09的平均交并比（mIoU）取得了最佳性能。还提供基于类别的结果以便进一步分析不同食物废物类别。

厨余食材图像分割与分类用于堆肥养分估算