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Article: Experts offer tips on AI in food and agriculture at IFT meeting

29 July 2020

From helping farmers track animal diseases to predicting the likelihood of pathogens in fields and water, artificial intelligence (AI) has a vast potential for application throughout the food industry.

If applied correctly, technologies such as AI, machine learning and predictive analytics can help the food industry boost efficiency, reduce food waste, improve food safety and support sustainability, said top-level food industry experts who discussed the applications of AI in the food supply chain at the Institute of Food Technologists' (IFT) virtual conference SHIFT20. .

However, there are still many challenges the food system has to overcome before expanding the use of the novel technology, such as creating basic infrastructure, improving the quality of data and bridging a gap in the availability of technical experts who can help food firms integrate AI into their operations.

Food companies should also be aware that AI is not a silver bullet, and it may not always be the right technology for the job, said experts.

"In some cases, it's a great tool to have, in other cases there are better, more appropriate tools out there to solve some of those issues," said Martin Wiedmann, a leading food science expert and the Gellert Family professor in food safety at Cornell University.

During a "virtual fireside chat" event, Wiedmann and Simon Liu, associate administrator at USDA's Agricultural Research Service (ARS), mapped out the current and future uses of novel technologies such as AI, predictive analytics and machine learning throughout the food supply chain.

They also provided practical tips for companies who want to test ways to optimize their operations using AI and machine learning.

Experts outline steps for application of AI, machine learning

Since data is key for AI or machine learning to work, companies should first ensure they are collecting the right, quality data and establishing a good basic data infrastructure, Wiedmann said. That is very important, especially in food industry sectors such as produce, which has only recently started converting from paper to digital records.

In such a conversion, small issues can make a big difference and having a database in which the word "cantaloupe" is spelled five different ways is not going to be useful, Wiedmann noted.

Companies should also make sure they are asking the right question, to ensure that the machine learning or AI technology can provide an analysis that is useful and practical for the company. Companies should always complete a pilot first to ensure not only the technology works in the way they want, but also that AI or machine learning is the right technology for the goals they are trying to achieve, Wiedmann advised.

"In some problems, you can use mechanistic models, models that use known mechanisms to predict and they tend to be much better at predictions," he noted. "I am continuously amazed by the underutilization of very standardized tools such as what we call simulations in the food industry. They are not difficult to build and are very powerful."

Therefore, companies should first do a pilot focusing on "something you are willing to change," Wiedmann advised.

"And if your AI predictions aren't right - and often they will not be - then figure out why and learn from that and continue to improve."

USDA's ARS explores uses for AI application

When applied correctly, AI can have tremendous potential to improve the food system, and scientists at educational institutions and USDA are pushing to expand the areas where the new technology can improve the efficiency of the food system.

According to Liu, USDA already has a number of initiatives underway that are exploring the uses of AI in the food and agriculture sectors.

"At ARS we have initiated a project we call the 'big data' project," he said. "This is the sixth year of this big data project, so right now we have many different projects around the country, we have many uses around the country and we are continuing to expand this now."

Currently, the most common uses of AI and machine learning focus on issues related to food production, such as improving crop rotation, streamlining crop planning, and monitoring crop and soil health or deficiencies, Liu said.

For instance, researchers at USDA's Beltsville Agricultural Research Center in Maryland are piloting a low-cost automated detection system, which uses computer vision coupled with machine algorithm to document conditions and problems in corn and soybean crops, Liu said.

With the help of Microsoft, the project allows researchers to gather data from multiple sources, including sensors, drones, tractors and satellites, feed it into cloud-based artificial intelligence models, resulting in a detailed picture of conditions on the experimental farms. While still at an early stage, researchers want to use the program to develop web-based tools to help farmers allocate resources more efficiently.

In addition, ARS is exploring the use of AI in disease detection - a major threat to the economy and food security, Liu said.

"AI-based image recognition systems could recognize specific plant diseases with a high degree of accuracy and potentially pave the way for field-based crop disease identification using mobile devices, such as smartphones," he said.

ARS scientists in New Mexico, for example, have developed AI-based tools to provide early warning of pest and disease outbreaks.

Researchers are also looking to use predictive analytics to forecast effective crop production and yield and determine how much of the crop will be harvested under specific conditions. Scientists are testing that approach in Iowa by using the new technology to determine potential strategies to increase profitability of each field based on varied uses of nutrients and pesticides, Liu said.

Novel technologies applied further down the supply chain can improve quality and boost food safety, suggested Wiedmann.

Image recognition, for example, can ensure product quality and track defects in food production facilities, Wiedmann said. Facial recognition, on the other hand, can determine whether food facility staff is following safety procedures, such as washing hands, he noted.

Visual imaging with AI has a potential for application in animal slaughter and meat processing, particularly when it comes to less homogenous cuts of meat, such as pork halves. To use a more automated system to cut such products, you can take images, let AI analyze them and then guide knives as they cut the product, Wiedmann said.

It is important to remember that AI "really shines" in areas where large data sets are already available - whether those are GIS, meteorological or supply chain data, he stressed.

Future AI applications in food regulation, distribution, marketing

AI can also have useful applications for regulators, who can use data on companies' history of inspection to determine frequency and priority of inspections, Wiedmann noted.

Future applications of AI could help significantly improve sustainability and reduce waste in the food industry. For example, it could predict when people are going to buy certain products and prevent an oversupply or undersupply of food.

AI has the power to improve shelf-life labeling, and scientists at Cornell are already testing ways to use the technology to make "sell by" or "use by" dates, Wiedmann noted.

"Just more precision to some of these 'fudge factors' I am going to call them that [industry uses] right now will reduce food waste and have a huge impact on sustainability," he said.

As AI and machine learning technologies continue to expand, there are also areas at the end of the supply chain that can benefit - including the food distribution and online shopping sectors.

Using an AI ordering algorithm, for instance, can help online grocery stores - which are becoming increasingly popular - increase efficiency and reduce food waste, Liu noted. The new technology could help consumers improve what they eat, as imaging recognition applications could estimate the nutrition content of various foods, he added.

"Maybe now we are helping folks in the front of the food supply chain, but in the future, we will be moving to cover the back end - the distribution, the food marketing and waste management," Liu said.

Another frontier for the future of AI would be taking the technology from "predictive" to "prescriptive," and using it not just to predict when issues may arise but also how to address them, Liu noted.

Challenges for expansion

Yet, a number of challenges still exist before the food industry can harness the full power of technologies such as AI, machine learning and predictive analytics, experts stressed.

One big issue for food companies now is the gap in skilled experts who can help companies adopt AI and machine learning technologies into their operations. That is a significant challenge and it can only be addressed using a multi-prong approach that requires the public, private and academic sectors to work to address collaboratively, Liu noted.

"We need to have a talent pool, to make sure we have the people, the staff that have enough management skill or business skill to advance the AI application," he said.

Building appropriate technical and data infrastructure can also be a challenge for companies as not all food firms can afford to divert resources to such initiatives, the experts noted.

USDA is also trying to figure out how to bring together the AI resources from different locations around the country, Liu noted.

"How can we structure that expertise so that we can accumulate the AI know-how and then be able to leverage that expertise properly," he stressed.

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