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Using AI to Reduce Food Waste

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Using AI to Reduce Food Waste

Suffice to say, it's not a technology that's in widespread usage at the moment, but it will be interesting to follow its progress.

· AI Zone ·
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I've written before about novel IoT-related technologies that are designed to give us detailed information on the freshness of produce. The hope is that better information will reduce food waste.

Indeed, initial applications of the technology have allowed retailers to save roughly half of the 18% of produce that goes to waste, thus representing a significant boost to their bottom line.

The technology itself monitors the produce at the pallet level and provides real-time feedback to enable accurate and prompt decision-making to be done on the spot. So, for instance, if produce is aging too quickly, it can be rerouted to a closer store to ensure it retains a decent shelf-life.

The data generated also allows the company to provide prescriptive, corrective actions that take into account a range of variables, including the state of the produce and its volume, together with daily demand and the freshness standards required for shipment.

Smart Pricing

Suffice to say, such insights into the freshness of produce can not only help to reduce food waste. A system developed by RapidMathematix aims to combine information on the freshness of produce with smart pricing that reduces the cost of goods as they deteriorate in freshness.

The system utilizes deep learning and machine vision to automate the pricing of produce depending on a range of factors, including the freshness of the produce, market conditions, and the competition.

The idea is that customers get a better idea of what they're consuming, and stores can more effectively reduce food waste by having smarter pricing strategies.

The system collects data from a range of sources to determine the freshness, location, and demand for the produce and determines the most accurate prices for these unique conditions. It's also connected up to electronic shelf labels that allow it to recommend prices in real-time.

The technology also coordinates with IoT devices to gather information from inside the store. The team believes that the data can also be used to improve negotiations between vendor and producer.

Suffice to say, it's not a technology that's in widespread usage at the moment, but it will be interesting to follow its progress.

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Topics:
machine learning ,machine vision ,deep learning ,ai

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