When the first day of the apple harvest rolled around in Washington State last year, the fruit was ripe and the pickers were ready – but the weather had other ideas. "It was like 38C… it's not safe for people to work in that heat," recalls Joel Carter at Okanagan Specialty Fruits. "We had to stop at 10 o'clock in the morning." He says that artificial intelligence (AI) models that forecast ideal harvest dates, taking into account the weather, would be useful.
"You need to know more than just when your fruit is going to be ripe. How long do you have to pick it?" he says. "That's where these models are really helpful." Carter's company has more than 1,250 acres of apple orchards in Washington, and the fruit is grown for sliced apple portions – often sold to hotels and schools, for example.
The firm is investing in technology with the hope of maximising productivity. Even the apples are genetically engineered so that they don't brown easily once cut. But planning a harvest is tricky.
New tools that count and analyse fruit on the tree or vine, and predict when crops will ripen, are emerging. It matters because prices for fruit, especially high-value berries such as strawberries or blueberries can fluctuate wildly. Getting the harvest date wrong means you book seasonal workers when you don't actually need them, and risk missing out on the biggest profits.
Okanagan Specialty Fruits is already experimenting with cameras from a Canadian firm called Vivid Machines. The cameras are mounted atop tractors and they scoop up imagery of the apple trees as the tractor trundles by. AI identifies buds, flowers or fruit in that footage.
"Right now, Vivid is telling us crop estimates and harvest dates," says Carter. He notes that the system is good at picking out very tiny flower buds, which are hard to see at a glance with the naked eye. But the accuracy of forecasts is noticeably dependent on the quality of historical information fed in to the system, adds Carter.
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