The small plastic stickers found on fruits and vegetables may seem trivial, but they contribute to microplastic pollution when improperly disposed of and create an unnecessary hassle for consumers. Currently, grocery stores rely on these stickers or manual entry of PLU codes for checkout, which is time-consuming and error-prone. A more efficient and sustainable solution could involve using computer vision to automate produce identification, eliminating the need for physical labels while maintaining fast and accurate checkouts.

How Computer Vision Could Replace Produce Stickers

One way this could work is by integrating cameras (either built into existing scanners or as add-ons) at checkout stations to capture images of fruits and vegetables. Machine learning models could then identify the type, variety, and even quality of each item, automatically applying the correct price. This would function for both staffed and self-checkout lanes. Key advantages include:

• Eliminating sticker waste and associated environmental concerns
• Reducing checkout time by automating identification
• Potentially improving accuracy versus manual PLU code entry

The system could distinguish between similar items (like organic vs. conventional produce) by combining visual data with weight, size, and seasonal availability information. A manual override option would handle cases where recognition fails.

Implementation and Benefits

An MVP might focus on the most common 20-30 produce items, with expansion to 100+ varieties as the system learns. This approach differs from existing solutions:

• It's more targeted (and affordable) than Amazon Go's full-store tracking
• Unlike PLU stickers, it requires no physical labels
• Compared to basic self-checkout recognition, it could identify specific varieties automatically

For retailers, the system could offer faster checkouts and reduced sticker costs. Consumers would benefit from the convenience of not removing labels. Environmental groups might support the reduction in plastic waste. Potential revenue models include SaaS subscriptions for retailers or licensing to POS manufacturers.

The main challenges involve ensuring accurate recognition across diverse produce types and convincing retailers to adopt new technology. However, the combination of operational improvements and environmental benefits could make this an attractive proposition for grocery chains looking to modernize while becoming more sustainable.