High Quality Compost

High-quality compost production is increasingly being studied through the lens of data-driven optimization. Current research focuses on using computer vision techniques, including semantic segmentation models like SegFormer, to analyze food waste composition for predicting compost nutrient content and automating processes like turning. This work aims to improve compost quality and efficiency, impacting both sustainable waste management practices and precision agriculture. The development of robust algorithms capable of handling challenging real-world conditions, such as mud and shadows in image analysis, is a key area of ongoing investigation.