Introduction
Artificial intelligence
(AI), or machine learning/machine vision, is playing a predominant role in the
world of food safety and quality assurance. According to Mordor
Intelligence, AI in the food and beverages
market is expected to register a CAGR of 28.64 percent, during the forecast
period 2018-2023. AI makes it possible for computers to learn from experience,
analyze data from both inputs and outputs, and perform most human tasks with an
enhanced degree of precision and efficiency.
In computer science, artificial intelligence (AI),
sometimes called machine intelligence, is intelligence demonstrated by machines,
in contrast to the natural intelligence displayed by humans and animals(Viejoet
al., 2018).
Food processing is one of the major manufacturing
sectors. According to the United States
Department of Agriculture, 16 percent of the value of shipments from
all U.S. manufacturing plants comes from food processing plants. These plants
employ around 1.5 million. For the most part, the sector is a very high volume,
low margin industry. Finding new ways to gain even modest increases in
efficiency can make the difference between a facility turning a profit or a
loss. This is why some of the largest food processing companies are turning to
artificial intelligence technology in attempts to improve numerous aspects of
the process. Offer many possibilities
to optimize and automate processes, save money, and reduce human error for many
industries. AI and ML can benefit restaurants, bars, and cafe businesses as
well as in food manufacturing. These two segments have common use cases where
AI in the food industry can be applied (Sharma, A.K).
Using AI in Food Industry
1.
Supply Chain
Optimization: less waste and more transparency: As long as food manufacturers are concerned with food safety
regulations, they need to appear more transparent about the path of food in the
supply chain. Here, AI in food manufacturing helps to monitor every stage of
this process — it makes price and inventory management predictions and tracks
the path of goods from where they are grown to the place where consumers
receive it, ensuring transparency.
2.
Sorting Food:
Optical Sorting Solutions: Instead of manually
sorting large amounts of food by size and shape the AI-based solutions to
easily recognize which plants should be potato chips and which are better to
use for French fries.Vegetables of an inappropriate colour will also be sorted
out by the same system, decreasing the chance that they are discarded by
buyers. Food Sorters and Peelers developed by TORMA show better processing
capacity and availability, which increased food quality and safety. This is
achieved by using core sensor technologies and a camera that recognizes
material based on colour, biological characteristics, and shape (length, width,
and diameter); the camera has an adaptive spectrum that is well suited for
optical food sorting.
3. Ensuring Personal Hygiene: AI is also helping to improve personal hygiene in a food plant, which is
just as important as hygiene in a kitchen, and helps to ensure that a facility
is compliant with regulations. The system, which can also be used in restaurants, uses
cameras to monitor workers, and it uses facial-recognition and
object-recognition software to determine if workers are wearing hats and masks
as required by food safety laws. If it discovers a violation, the software
extracts screen images for review.
4.
Predictive
Maintenance, Remote Monitoring, and Condition Monitoring: It is obvious that manufacturing a lot of goods
demands large, complicated, and intricately constructed mechanisms. The
maintenance of such machines can be rather costly without predictive
maintenance – figuring out the time-to-repair and cost-to-repair indicators
through categorizing issues and making predictive alerts. Timely repairs can
save up to 50% maintenance time and reduce the costs needed for it by almost
10%. To perform remote monitoring on complicated mechanisms, you can make a
Digital Twin of a machine that will show you the performance data on parameters
and manufacturing processes and boost the throughput. Machine Learning also
allows the identifications of factors that affect the quality of the
manufacturing process with Root Cause Analysis (eliminating the problem at its
very source). With condition monitoring, you are able to monitor the
equipment’s health in real-time to reach high overall equipment effectiveness
(OEE).
The Benefits of AI in the Food Industry
1.
Recently, more and more companies are trusting
Artificial Intelligence to improve supply chain management thorough logistics
and predictive analytics as well as to add transparency.
2.
Digitization of the supply chain ultimately
drives revenue and provides a better understanding of the situation. AI can
analyze enormous amounts of data that are beyond human capability.
3.
Artificial Intelligence helps businesses to
reduce time to market and better deal with uncertainties.
4.
Automated sorting will definitely reduce labour
costs, increase the speed of the process, and improve the quality of yields (Masood and Hashmi 2019).
Artificial Intelligence in Food Waste
The humans currently don’t
use their resources wisely and mono-cropping, the blanket application of
synthetic chemical fertilizers and intensive land use, can be replaced with
“smarter” methods. Information gathered from sensors, drones, and satellites,
as well as other equipment, could help farmers make better decisions faster (Beheraet
al., 2015) Here are some ways to reduce food waste with AI:
·
While some solutions analyze the ripeness of the
fruits, others figure out what microbes could increase crop growth without the
involvement of synthetic fertilizers.
·
Farmers could get rid of field trials,
benefiting from advantages of the AI, which will save significant amounts of
money.
·
If farm-based food supply chains use visual
imagery technology, the food inspection process will be much easier.
·
AI food tracking will enable us to sell food
before it becomes waste, through more efficiently connecting farmers with
restaurants or people buying food.
The main challenge to make these ideas a reality can not be
delivered by one company. The whole industry needs to be changed. An entire
network of partners is required to help these changes make a significant impact
on the world.
Conclusion
The implementation of AI and
ML in food manufacturing and restaurant businesses is already moving the
industry to a new level, enabling fewer human errors and less waste of abundant
products; lowering costs for storage/delivery and transportation; and creating
happier customers, quicker service, voice searching, and more personalized
orders.
References
Viejo, C.G.,
Fuentes, S., Howell, K., Torrico, D. and Dunshea, F.R., 2018. Robotics and
computer vision techniques combined with non-invasive consumer biometrics to
assess quality traits from beer foamability using machine learning: A potential
for artificial intelligence applications. Food control, 92.72-79.
Sharma, A.K., Artificial Intelligence and Machine Learning
Application to Functional Food Science.
Masood, A. and
Hashmi, A., 2019. AI Use Cases in the Industry. In Cognitive Computing
Recipes (383-396). Apress, Berkeley, CA.
Behera, S.K., Meher, S.K. and Park, H.S., 2015. Artificial neural network
model for predicting methane percentage in biogas recovered from a landfill
upon injection of liquid organic waste. Clean Technologies and Environmental
Policy, 17(2).443-453.
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