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Food Machine learning (Sustainable Cities (Unrelated (FlavorDB (Database…

- - - - Smart agriculture and food systems
        AI-augmented agriculture involves automated data collection, decision-making and corrective actions via robotics to allow early detection of crop diseases and issues, to provide timed nutrition to livestock, and generally to optimise agricultural inputs and returns based on supply and demand. This promises to increase the resource efficiency of the agriculture industry, lowering the use of water, fertilisers and pesticides which cause damage to important ecosystems, and increase resilience to climate extremes.
      - 11 Data intelligence for better food inspection to decrease or eliminate claims that are a major source of food waste and improve early warning systems for post-harvest plant disease and pest outbreaks
        
        Fresh products are inspected to see if they fit specifications at every step of the supply chain. The inspection process is usually fairly manual and therefore time consuming and subjective. Automated food inspection process via machine learning could significantly reduce errors and ultimately reduce food waste as less shipments would be refused and wasted. Different data can be used to feed the machine learning models and include images taken by a mobile phone (AgShift), hyperspectral images (Impact Vision) and sensor data.
        Better data collection and analysis could also significantly improve early warning systems for post-harvest plant disease and pest outbreaks. Data that could be used for such systems include satellites,hyperspectral images and sensors. An example of such solution is Agroshield by Saillog that notifies subscribers after crop diseases and pests were detected on nearby farms
        
        14 Farm to fork digital marketplaces to prevent waste and energy consumption during transports
        
        By directly connecting farmers and end buyers locally, waste happening due to inefficient supply chains can be avoided.
        
        The eat-local movement mostly drives the emergence of such models in richer countries while the potential for more efficient and timely supply chain is the main factor for adoption in emerging countries.
        
        Platforms targeting emerging countries like M-Farm might be therefore more impactful.
        
        Some startups in the space in developed countries include Farmigo that uses a just-in-time model, where producers only harvest food that has already been ordered on Farmigo or La Ruche qui dit Oui that allow users to group themselves to buy directly from their local farmers.
        
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- - - - Is a condition that results from eating a diet in which one or more nutrients are either not enough or are too much
      - ML
        
        https://foodtank.com/news/2017/11/artificial-intelligence-part1/
        
        https://foodtank.com/news/2017/11/artificial-intelligence-part2/
    - - ML to solve food hunger
        
        https://stat.cornell.edu/news/harnessing-machine-learning-and-big-data-fight-hunger
      - https://news.cornell.edu/stories/2018/10/harnessing-machine-learning-and-big-data-fight-hunger
      - The need of enough local food available everywhere
      - food needs to satisfy the dietary needs of every individual
    - - Being without sufficient quantity of food
- - - - Database containing a whole lot of food with their Taste
        
        https://www.researchgate.net/publication/320593078_FlavorDB_A_database_of_flavor_molecules