With advanced AI, while every industry is growing at rocket speed, the smart
home industry has not reached the next generation. There is still a huge leap
of innovation that needs to happen before we call a home a Smart home. A Smart
home should predict residents' needs and fulfill them in a timely manner. One
of the important tasks of maintaining a home is timely grocery tracking and
supply maintenance. Grocery tracking models are very famous in the retail
industry but they are nonexistent in the common household. Groceries detection
in household refrigerators or storage closets is very complicated compared to
retail shelving data. In this paper, home grocery tracking problem is resolved
by combining retail shelving data and fruits dataset with real-time 360 view
data points collected from home groceries storage. By integrating this
vision-based object detection system along with supply chain and user food
interest prediction systems, complete automation of groceries ordering can be
achieved.

通过将零售货架数据与水果数据集和家庭食品库存的实时视图数据点结合，该研究解决了家庭杂货追踪问题，实现了杂货订购的完全自动化。