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Manual berry picking and packing processes are prone to human error, lack real-time tracking capabilities, and struggle with scalability. The solution must operate reliably in extreme weather conditions (sunlight, rain), accommodate glove-based user interactions, and handle high-volume barcode scanning (100,000+ scans/day) across large-scale operations.
A leading US-based berry grower with a 100-year history, specializing in sustainable farming practices and year-round supply of strawberries, blueberries, blackberries, and raspberries.
Streamlined harvesting workflows through digital tracking, reduced operational errors by 70-90%, enabled real-time data-driven decision-making, and improved employee onboarding efficiency via simulation training. The solution will maintain productivity in extreme conditions while supporting Wish Farms' growth as a sustainable agriculture leader.