Two recent new product launches continue to demonstrate how Artificial Intelligence (AI) is changing food processing. In this article, we tell how AI is adding value to this industry and why its importance is growing.
Lately we have heard a lot about Artificial Intelligence (AI). The ability of computers to imitate human thinking and decision-making, and to automate tasks that traditionally required human intelligence, has sparked all kinds of news. Along these lines, and to continue confirming the growing importance of this technology, Tomra Food has just launched two new classification and calibration solutions based on AI.
One of those solutions is the new Tomra Neon, which pre-sorts mechanically harvested blueberries destined for the fresh market. On the other hand, the new generation Spectrim These machines are just the beginning of a revolution that will make the production of fresh and processed foods more efficient and profitable.
Without a doubt, the most extensive public discussions about AI have focused on the latest version of the ChatGPT chatbot. This language processing tool, powered by artificial intelligence, can answer questions, perform tasks such as writing emails and computer codes, as well as writing essays to pass exams that, without this help, would be very difficult for students. Not long ago, none of these AI capabilities existed.
AI also made headlines when the godfather of the technology, cognitive and computer psychologist Geoffrey Hinton, quit his part-time job at Google so he could speak more freely about his concerns about AI’s emergence into the market. labor.
In this way, today it cannot be denied that AI-driven automation will be implemented in tasks that are currently handled manually, including some routine occupations in food processors and packaging plants. As we will see in a moment, this will be a positive development for employers, employees and food consumers.
Since 2019, Tomra Food has been using Artificial Intelligence to make sorting and grading solutions more accurate than traditional techniques, and the deployment of AI-driven technology in the food production industry will continue to accelerate in the future. Next, we will look at some of the areas it will impact:
Make no mistake: AI is causing a major technological change to which companies will have to adapt to remain competitive.
When talking about AI, the terms ‘machine learning’ and ‘deep learning’. Machine Learning is a collection of techniques that allows software systems to recognize patterns in data to provide measurements and insights. Deep learning on the other hand is a subset of machine learning that uses artificial neural networks to solve complex problems. These technologies are well suited to food production because many tasks involve data and decision making.
AI is also relevant to food production due to the high level of variability in the industry, from weather and climate effects to natural variations in products. These factors can result in traditional systems struggling to make accurate predictions. It is not enough to have data: its quality is also an important factor in AI performance. As with traditional systems, the better the data, the better the decisions, which is why it is so important to have the best inspection systems and sensors, as they can collect higher quality data that powers the AI system. This leads to more accurate and consistent decisions that result in less food waste and more salable products, as well as maximizing product value.
Thus, Artificial Intelligence can improve classification and calibration machines in several ways; can help make more accurate “accept or reject” decisions; recover more good products from compromised raw materials, through greater precision; and more accurately sort products on the line into different grades to enable hands-free production. For example, heavy rain or frost has damaged so much fruit that only 40% of it can be packaged. Older technologies wouldn’t be accurate enough to recover this, as they would mistakenly include too much damage, but AI makes it possible: in addition to recovering a potentially disastrous harvest, this helps keep customers satisfied at times where they would otherwise , the product would not reach the appropriate quality.
Deep Learning is a method of Artificial Intelligence that uses pre-trained models to teach computers how to process data, such as complex patterns in photographs. The recently launched Spectrim X series grading platform is a great example of this. Developed by a team of industry-leading scientists, engineers, researchers and experts, Spectrim X integrates the latest development of Tomra’s LUCAi deep learning technology.
The Spectrim X platform is equipped with LUCAi software, computing hardware and pre-trained models that achieve unprecedented calibration accuracy. More packaging operations can now be carried out without manual intervention, focusing more on thresholds while minimizing fruit loss.
Spectrim X evaluates thousands of high-resolution multi-channel fruit images every second. It then cross-references what it sees with artificial intelligence networks that have been trained on tens of thousands of fruits to make sorting decisions that meet market demands. This data has been captured from Tomra machines around the world and accurately manually labeled by Tomra’s data science team. During 18 months of real-world testing in the US and New Zealand, sorting and sizing apples, Spectrim
The new Tomra Neon solution is also powered by AI, to pre-sort blueberries. While automated blueberry picking is faster and less expensive compared to manual harvesting, it poses challenges to fresh fruit processing and packaging lines in the form of unwanted products and fruit bunches. To address these challenges, Tomra Neon accepts or rejects fruit before transferring it to Tomra’s KATO260 optical sorter. By using AI, Tomra Neon can identify and remove those unwanted clusters, undersized fruits or unripe fruits. By eliminating more than 95% of the bunches and more than 90% of the unwanted green and red cranberries, the efficiency of the optical sorter is optimized.
The valuable capabilities of AI in food processing will become even more important in the near future due to the growing challenges presented by global food shortages and unusual weather events, and because there is commercial pressure to provide ingredients and products of the highest quality. high quality, even if there are deficiencies from its entry.
AI can also help processors and packaging companies face other challenges, such as boosting the offering of products and services at the lowest cost; meet customer product specifications fairly and accurately; and reduce or eliminate problems associated with recruiting, training and retaining skilled labor.
Difficulties arising from finding seasonal workers and properly managing the workforce can be addressed by automating tasks that were previously performed manually. It’s no secret that sorters, graders and balers are faster, more accurate, consistent, reliable and ultimately more profitable than humans.
This brings us back to the controversial issue of job replacement. It’s worth remembering that many processing plants, especially those not near large population centers, struggle to employ all the people they need, so instead of performing tasks that were previously done manually, automation often It does what could not otherwise be done. Automation often takes care of tasks that people don’t want to do because they are boring, repetitive or exhausting. This is certainly true of manual sorting and sizing, and if workers are displaced from these roles, they are often reassigned to other tasks on the line, which are less monotonous and have more value.
However, as we’ve seen here, reducing labor dependency is just one of the many ways AI-powered automation benefits processors and packers. AI will play an increasingly important role in meeting the needs and wants of food consumers as the world’s growing middle classes increase demand for healthy foods. And most important of all, AI will help meet the challenge of feeding the world’s huge and growing population, which will require more food production and less food waste.