Artificial intelligence is swiftly changing harvest schedules for the fruit sector, equipping producers with tools to predict ideal picking dates and optimize yields. Still, industry specialists warn substantial obstacles must be overcome before such digital solutions become widespread.
AI-Driven Forecasts Help Tackle Volatile Climates
Increasing climate volatility has led fruit growers to embrace sophisticated AI platforms for predicting when to harvest—a major contributor to sustaining fruit quality and maximizing return. Joel Carter of Okanagan Specialty Fruits recalled that Washington State’s apple harvest last year was interrupted by severe heat peaking at 38C, forcing operations to shut down before midday. Managing 1,250+ acres of orchards, the company supplies pre-sliced apples (which are genetically modified to resist browning) to venues including hotels and schools.
Aligning with the right harvest timeframe involves more than simply waiting for ripeness. By leveraging AI, growers can better plan for labor and anticipate when fruit is at its best, thereby elevating overall efficiency. Carter remarked, “You need to know more than just when your fruit is going to be ripe. How long do you have to pick it? That’s where these models are really helpful.”
Imaging and Analytics Enhance On-Farm Choice
To refine their workflow, Okanagan Specialty Fruits utilizes imaging systems from Vivid Machines, a Canadian technology provider. Cameras fixed to tractors move through the orchards, recording detailed visuals that AI then processes to track flower bud, blossom, and fruit development. Carter points out that the technology’s edge lies in its ability to spot little buds undetectable by humans. “Right now, Vivid is telling us crop estimates and harvest dates,” Carter noted.
Nevertheless, reliability of AI-generated insights is tightly linked to the availability and accuracy of historic data for each specific farm. “This isn’t something where an AI can scrape the internet and figure out what’s the average [yield] for Granny Smith. It’s going to be bespoke to your farm,” Carter continued.
The apple industry benefits from a harvest window that extends around three weeks, granting some operational leeway. For more perishable crops, such as strawberries and blueberries, timing is more critical, with little margin for error. “If a strawberry crop is on, you have to harvest it—otherwise your entire crop gets diseased very, very quickly,” explained Raymond Martin, co-founder of UK-based FruitCast. His company delivers projections for strawberries, raspberries, blackberries, blueberries, and tomatoes, with plans to add grape forecasts next year.
New Sensing and Accuracy Targets are Shaping the Industry
FruitCast employs smartphone, drone, or tractor-mounted cameras to collect farm-wide imagery, which its platform analyzes to forecast both yield and ripeness, including in greenhouses and on extensive plots. Martin stated this strategy outperforms manual assessments, especially for large and complex sites. Recent harvests in the UK have faced high heat and drought, causing thermal dormancy—resulting, Martin said, in slower ripening. FruitCast models account for irrigation and climate, pledging predictions within 10% one week in advance (90% accuracy), within 17% at three weeks (83% accuracy), and always under 20% error.
Major partners like Angus Soft Fruits have observed strong benefits but acknowledge that a comprehensive, all-in-one forecasting system is not yet available. FruitCast technology has also been adopted by some local growers working with Driscoll’s, a prominent California-based fruit supplier with UK ties.
Data Sharing Concerns Meet Next-Gen Monitoring
The evolution of fruit sensing is not limited to imaging alone. Many in the industry rely on brix meters—some utilizing infrared technology for non-destructive sugar content measurement—to track sweetness. At Princeton University, Yasaman Ghasempour and her students have created a millimetre wave-based ripeness sensor capable of deep, non-invasive fruit assessment, which has sparked a range of reactions during use in New Jersey markets.
Other innovations are also emerging. At North Carolina State University, Jing Zhang has developed an automatic blueberry-counting system that works with smartphone images. The University of Florida’s Kevin Wang has engineered a drone-powered crop-counting tool that works with models as inexpensive as $100 (£74) (read more). However, Wang advised that there are hesitations regarding sharing sensitive information—such as details about fertilizers or irrigation—with external AI providers.
AI Advances, but Growers Rely on Their Expertise
Despite recent advancements in predictive harvesting technology, there are considerable challenges remaining. “Adoption is very complicated,” said Zhang of North Carolina State, explaining that winning growers’ confidence is a critical step for broader acceptance. Representing Western US agricultural businesses, Ben Palone of Western Growers views AI tools as effective “optimization tools,” yet stresses, “Growers like to have people in the mix to make some of those very critical decisions—especially when it comes to harvests.”