Use of AI and analytics in the produce supply chain
Welcome to the June 1, 2025 edition of SFTW.
MDL & AA Happenings
- Metal Dog Labs released part 2 of the GenAI in Ag report. The second part focused on Value Creation Frameworks, and provides a playbook on how to think about and execute on GenAI based projects. Both the reports are available for free to SFTW members. You can get your free copy here by Signing In (for existing subscribers) or by Signing Up (for new subscribers).
- AgTech Alchemy will host two events on the same day in California and in Fargo on June 12th. Do not forget to register, if you plan to attend one of the events.
- SFTW Convos will return on Wednesday with a conversation with Guy Coleman, open source advocate and creator of the OpenWeedLocator for precision spraying of weeds.
The explosion of AI awareness in the popular domain due to ChatGPT has even Grandma asking to get her own ChatGPT account, though she might not know how to flip to the back camera while being on a video call with her grandchild.
But contrary to popular opinion, AI has been around for at least a few decades and the adoption of AI has accelerated over the last few years.
Over the next few weeks, I will bring you more and more examples of where AI is being used within the food and agriculture value chains, all the way from the consumer to farm, and everything in between.
If you are an enterprise in any industry, you cannot be sitting on the sidelines, when it comes to the use of AI within your business.
Scaling Regen Ag Starts with Better Data
Farmers Edge is building the digital backbone for regenerative agriculture—designed to simplify Scope 3 reporting and deliver credible results, fast. With Managed Technology Services built for agribusiness, Farmers Edge connects real-time field data to your sustainability goals across every acre and every grower in your network. From in-season tracking to end-of-year proof, Farmers Edge makes regen ag measurable and manageable. If you're serious about scaling impact and reporting, this is the infrastructure that makes it possible.
AI for Food Inspections and Food Waste Management
The United States Government Accountability Office estimates that the government loses up to $521 billion annually due to fraud, based on an analysis of fiscal years 2018 to 2022. Tax fraud, check fraud and improper payments to contractors, in addition to improper payments under the Social Security and Medicare programs have become a massive drag on the government’s finances.
AI-powered fraud detection solutions provide higher detection accuracy by looking at the whole picture instead of individual transactions, catching fraud patterns that traditional methods might overlook. AI can also help reduce false positives, tapping into quality data to provide context about what constitutes a legitimate transaction.
The U.S. Treasury Department began using machine learning in late 2022 to analyze its trove of data and mitigate check fraud. The department estimated that AI helped officials prevent or recover more than $4 billion in fraud in fiscal year 2024.
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In February, the FDA failed to share the findings of its investigation into an E.coli outbreak that sickened nearly 90 people last fall with the public, according to the Washington Post.
The United States imported $ 213 billion worth of agricultural products in 2024. The FDA oversees all food products except those regulated by the USDA. The USDA primarily regulates meat, poultry, and egg products, while the FDA handles everything else, including food products that contain animal protein as an ingredient.
Given the sheer volume of food imports, the FDA only inspects a small percentage—often less than 1%—of shipments entering the U.S. To maximize efficiency, the FDA targets high-risk food categories, such as seafood, ready-to-eat foods, and products with a history of contamination.
FDA has not met its domestic and foreign inspection targets since fiscal year 2018.
The FDA Food Safety Modernization Act (FSMA) directs FDA to inspect each high-risk domestic food facility at least once every 3 years and each non-high-risk facility at least once every 5 years.
For foreign facility inspections, FDA conducted far fewer than the annual target of 19,200 inspections identified by FSMA, according to FDA data. For example, the highest annual number FDA completed was in fiscal year 2019 when FDA inspected 1,727 foreign facilities—about 9 percent of the annual target.
It is no surprise that the FDA is now using artificial intelligence to help oversee the safety of the nation’s food supply. The FDA is using AI to identify where they should target their limited supply of food inspectors to go and inspect.
The FDA is focusing on seafood to help identify areas of high risk to be targeted for a closer look from a food safety and quality standpoint. The AI models are designed to improve the agency’s ability to quickly identify imported seafood products that may be contaminated by illness-causing pathogens, decomposition, the presence of unapproved antibiotic residues, or other hazards.
Machine learning gives the agency the ability to analyze data from various sources to help inform FDA decisions and target our resources at the borders. In a related shrimp pilot, we have begun to focus on areas of increased risk, such as shrimp contaminated by aquaculture drugs, for foreign inspections.
But what happens to certain food products before they are shipped post harvest, and once they are within the supply chain?