AI
Food processing is a critical and indispensable role in ensuring a supply to safe, convenient and affordable food in a modern context. It is a process that is involved in many stages from farms and to supermarkets. However the food processing industry still faces many challenges including: labor shortages, inefficiency, waste, and the growing need for sustainable food. This industry plans to overcome these problems through the use of Artificial Intelligence (AI). AI is sparking a change in food processing by automation, perfecting quality control, fixing complex machineries, innovation, while offering sustainable options. In doing so, they aim for a system that better responds to the changing customers needs.
One of the palpable impacts of AI in food processing is the effects of automation. Traditional methods of using manual labour in food factories, processes such as cutting or packaging, lead to inconsistent results and slower speeds. However, AI powered machines or cameras can complete these tasks much faster and produce higher quality products. In fact, advanced computer vision systems allow robotic arms to sort foods with an accuracy rate up to ±5%, surpassing us humans. In addition to this, interestingly AI powered robotics could be more adaptable than traditional methods. For example, robots can operate in unsafe or hygiene sensitive places. This reduces risks through contamination but also allows workers to focus on other tasks that AI robots may struggle with, such as creative tasks.
Some may argue that one of the most important applications of AI and food processing is in quality control and food safety. Contamination can have devastating effects on both consumers and the company. AI aims to solve this by integrating machine learning and computer vision into monitoring systems, which support food inspection for contamination, defects, or other errors that have been missed by the human eye. In fact, Nestlé has already applied AI systems that inspect processes such as Packaging, which have resulted in a reduced need for manual checkups to 80%. Moreover hyperspectral imaging paired with machine learning has proved to detect contaminants and toxins in grains or nuts. These have traditionally been tasks that require time manual labor, and laboratory Testing. AI’s problem solving skills have assisted companies and consumers to prevent unsafe products from being sold and consumed.
AI doesn’t only help with monitoring food, but it helps to also fix and maintain the machines in factories. Like many other factories, the current food production system relies on a range of different complex equipment. If these equipment fail or go through a malfunction it can lead to a lot of time, product, and money. By analyzing sensors in the machine AIs can predict when equipment is likely to malfunction and schedule maintenance in advance.
Apart from increasing efficiency in food processing, it also pushes for innovation. Until recently food product development could take years due to the trials and consumer tests. However AI speeds this process up by predicting ingredient interactions and consumer preferences. Kraft Heinz, for example, uses AI to create a plant-based cheese in just eight to ten months although it normally takes two years. In addition to speeding up product testing it also allows for discovering new ingredients, especially when looking for sustainable sources of protein such as plant-based or insect alternatives. This ultimately helps companies to deliver safe eco friendly products to the table faster. On the other hand, personalisation is increasing in demand among consumers, due to the large range of dietary needs, allergies, and healthier products. AI helps break down data of the needs and consumer preferences to suggest nutrition plans, ultimately facilitating and promoting a healthier diet.
As food processing poses a threat to global food production and sustainability grows in demand, AI aims to help address it. By better predicting consumer’s demand, it reduces overproduction and food waste, which are a large part that contributes to environmental problems. Studies have shown that through optimising logistics and storage, AI can cut spoilage during transportation as much as 50%, therefore reducing food waste. Apart from helping the earth directly through aspects of food processing, AI also rationalises energy efficiency as monitoring systems decrease energy use by 15-25%. Furthermore AI assists designers to manufacture eco friendly packaging.
Despite all of these advantages of AI in food processing, there are many challenges and ethical considerations. A large problem associated with AI is the cost, as it requires a notable amount of funds, which may not be accessible for small businesses. Another challenge posed is the lack of structured data sets in food companies. These data sets are essential to train AI effectively, and there are only a handful of experts in both the fields of AI and food science.
Ethical concerns are issues that often come along with the use of AI. Automation threatens repetitive and manual jobs to lead to job displacements, many companies may feel unsafe due to their data being exposed, and regulatory compliance remains tangled because food safety standards differ across the world.
To ensure ethical, safe, and responsible integration of AI, the industry must address these issues.
In conclusion, artificial intelligence is being integrated into the food processing system through multiple profound approaches, and ultimately aims to create a meaningful impact. However it is also important to recognise that, with these benefits come with disadvantages such as job displacement, cost, and lack of privacy.
It is key to implement AI into the system ethically and thoughtfully, to ensure that both the companies and consumers maximise the advantages. Only after this responsible implementation, AI has the potential to build a new system that is more sustainable, healthier, and adaptable.
By: Maru Kusawa
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