Agriculture
❯
Food safety
❯AI to understand adulteration in commonly used food items
Reducing Food Recalls With AI-Dri
For:
Food Companies, Retail Stores, Restaurants
Goal:
Anticipate Risks, Improve Operation Efficiency
Animal Disease Detection
For:
Dairy Farmers, Factory Farmers
Goal:
Anticipate Risks
AI to understand adulteration in commonly used food items
For:
Consumers, farmers, health monitoring agencies
Scope:
Understand the patterns in hyperspectral / near infrared (NIR) or visual imaging specifically for adulteration in milk, bananas and mangoes.
Goal:
Improve Operation Efficiency
AI to understand adulteration in commonly used food items
For:
Consumers, farmers, health monitoring agenciesGoal:
Improve Operation EfficiencyProblem addressed
To devise a simple, cost effective tool to identify adulteration in food items at
point of purchase.
Scope of use case
Understand the patterns in hyperspectral / near infrared (NIR) or visual imaging specifically for adulteration in milk, bananas and mangoes.
Description
Food adulteration is becoming a menace, especially with
adulterants that are either carcinogenic or harmful to body
parts like the kidneys. To give a few examples, milk is
adulterated with soda, urea and detergents, whereas
mangoes and bananas are prematurely ripened using
calcium carbide and so on.
Common man cannot live without these items. There is no
frugal way to identify these type of adulteration.
An experiment of controlled adulteration was conducted and
hyperspectral reflectance reading were taken.
AI helped to find the patterns in the hyperspectral signature
and was able to reliably classify (90 % ++) samples that were
either unadulterated or adulterated.
Sensor Network - IOT
Live Video
AI: Perceive
Machine Learning
Computer Vision
AI: Understand