A team of engineers at the US Department of Agriculture has developed a new tool for automating the production of food.
The researchers, from Cornell University, developed the tool after finding that the supply chain for some foods in the US was so complicated that it was impossible to fully automate.
The team says the tool could be used to quickly automate production, reduce the number of people required to process food, and reduce costs.
“It’s the simplest way to automate food processing that we’ve found so far,” said lead researcher Dr Adam Jorgensen.
The tool uses algorithms to identify, label and classify products to provide consumers with information on how the product is made.
The algorithm then looks at the ingredients in each food and the characteristics of the product to determine how much labour and materials are needed to produce it.
The system can be used with any food item, from bread to burgers to chips, to identify where each ingredient is sourced.
The new tool is not currently being tested in the field.
“Our goal is to use this tool in the food supply chain to reduce food waste,” said Dr Jorgenson.
The research, published in the journal Food Science and Technology, is the first of its kind in the world.
Dr Jurgensen said he hoped to eventually build a similar tool for the global food chain, including packaging and packaging materials.
He also said the team was trying to design a tool that could be easily integrated into existing products.
“We thought this could be a great way to look at these problems from the start.” “
The researchers were inspired by a previous work by the Cornell team, which used the same algorithm to identify food. “
We thought this could be a great way to look at these problems from the start.”
The researchers were inspired by a previous work by the Cornell team, which used the same algorithm to identify food.
They tested their new tool with some food products and found that it could process about 25% of the food in a bag without needing any additional equipment.
The software then identified which ingredients were the most commonly used, and how many of each ingredient the machine was able to process.
The process could be simplified to use a single machine and could save a significant amount of labour.
The work is supported by the US Agriculture Department’s Center for Innovative Manufacturing Technology.