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Artificial Intelligence in Food Processing

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.



Writer :: Gouthami,Jagadeesh, Shivaswamy and Patil      Published on :: 25-Aug-2020


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