From Gut Feel to Data-Driven Fresh Produce
Fresh fruits and vegetables have long depended on human intuition: farmers guessing the right seed variety, wholesalers ordering by habit, and retailers eye-balling how much to stock. This approach often swings between empty shelves and bins of spoiled produce, contributing to food waste and volatile prices. Today, AI in agriculture is starting to replace guesswork with measurable insight across the fresh produce supply chain. New software tools can analyse everything from weather and soil conditions to shopper behaviour, turning scattered data into practical recommendations. For consumers in markets like Malaysia, this shift could mean more consistent quality, better freshness, and fewer days when favourite items are out of stock. But it also forces a traditional, relationship-based sector to grapple with data, algorithms, and new skills, reshaping how decisions are made from farm to supermarket.
Experience Data: Satellites, Seeds, and Smarter Fields
On the farm side, Dutch firm Experience Data shows how AI can act as a farm decision engine for growers. Traditionally, choosing a seed variety meant relying on sales reps or neighbouring farmers. Experience Data instead uses software that asks growers just a handful of questions about soil type, climate, and preferences, then recommends the top three varieties that best fit their conditions, even suggesting alternatives if some options are too costly or undesirable. The company also analyses satellite images to detect patterns in storage and crop locations, helping an onion grower identify potential new customers that resemble existing buyers. These tools demonstrate how AI in agriculture can reduce risk, support better yields, and open markets by turning complex data sets into simple, actionable advice that even smaller producers can use without a full data science team.
Ever.Ag’s Everett: Connecting the Whole Supply Chain
Beyond individual farms, AI is now being used to connect data across the wider agricultural supply chain. Ever.Ag’s new Everett platform is described as an "Ag Decision Engine" that lives inside the software agriculture and food processing businesses already use. Initially focused on dairy, Everett combines decades of digitised production, procurement, transport, processing, and market data to optimise decisions such as cheese yield, transportation routes, and sales and operations planning. Because it understands each processor’s products and processes, Everett can orchestrate workflows and continuously improve as more data flows through it. While this early deployment targets dairy, the same concept could apply to fruit and vegetable chains: aligning harvest schedules, cold-chain logistics, and pricing with real-time demand. For consumers, that kind of optimisation could lead to fewer stock-outs, more stable prices, and food waste reduction as products move more efficiently from field to chiller cabinet.
Supermarket Inventory AI: Vallarta’s USD 10 Million Lesson
At the retail end, Vallarta Supermarkets in Southern California offers a striking example of supermarket inventory AI in action. The grocer replaced disconnected systems for produce, bakery, taqueria, and seafood with a unified, AI-powered fresh inventory management platform from Logile. By tying together production planning, recipe and scale management, and yield tracking, Vallarta improved demand forecasting and reduced overproduction, shrink, and spoilage. According to an independent case study, the company achieved a 1,070% return on investment and recovered its full investment within 15 months, with more than USD 10 million (approx. RM46 million) in attributable profit over three years. Importantly, these gains came without cutting store staff, but by better aligning labour with real production needs. For shoppers, the result is fuller counters of the right items, made in the right quantities, and fewer clearance bins filled with wilted or unsold fresh food.
What This Could Mean for Malaysia’s Markets and Shoppers
Applied to Malaysia, these examples point to a future where AI quietly supports both supermarket chains and traditional wet markets. Farm decision engines similar to Experience Data’s tools could help smallholders pick the right seed varieties for local soils and monsoon patterns, stabilising yields and quality. Supply-chain platforms inspired by Everett could coordinate harvest, transport, and cold storage across fragmented producers, reducing delays and spoilage. Supermarket inventory AI, as seen at Vallarta, could help Malaysian retailers better predict demand for fresh produce, cut waste, and keep shelves stocked with fresher items. Still, challenges remain: collecting reliable data, funding technology for small players, closing the skills gap, and building trust with farmers accustomed to gut-based decisions. Looking ahead, expect more predictive demand planning, dynamic pricing to clear stock before it spoils, and tighter links between AI systems and national sustainability goals around food waste reduction.