How the city can improve diet and the data can help?

Standard intake of healthy food is necessary for keeping a balanced diet to avoid obesity in the human body.

Machine learning that automatically performs accurate classification of food images and estimates food attributes.

Experiment with a variety of food categories, each containing thousands of images, and through machine learning training to achieve higher classification accuracy.

Machine learning of FOOD RECOGNITION and  NUTRITION ESTIMATION

A very powerful tool that holds the potential to revolutionize the way things work that also helpful for urban food system.

• Understand food consumer trends and behavior patterns > 

             ((( Machine Learning can review large volumes of data and discover trends and patterns that would not be apparent to humans.)))

• Updating & Continuous Improvement > ((( Using new dataset and the model can update and  ML algorithms gain experience, they keep improving in accuracy and efficiency. This lets them make better decisions that’s very useful for food industry as it requires flexibility )))
• Interpretation of Results > ((( another major challenge is the ability to accurately interpret results generated by the algorithms and the model can transfer learning to new area or new tasks for prediction )))

Although, it’s also has the disadvantage of using ML such as data acquisition, require lot of  time and Resources.       

For accuracy of nutrition estimation as the big amount of data, it’s impossible to make it happens by human!
MACHINE LEARNING FOR FOOD RECOGNITION and  NUTRITION ESTIMATION is a project of IAAC, Institute for Advanced Architecture of Catalonia developed at AI in urbanism course of Master in City and Technology program in 2019-2021 by:
Students: Pawitra Bureerak
Faculty: Angelos Chronis, Serjoscha Duering, Nariddh Khean’s.