Case Study: LIAT achieves 95% AI model accuracy with V7 Darwin

LIAT improves strawberry yield prediction accuracy to 95% with V7

LIAT, a technology developer focused on the horticulture sector, faced the critical challenge of accurately predicting strawberry yield. With over 40% of production costs spent on labor and significant losses from incorrect forecasts, they needed a better solution. They turned to V7 and its Darwin platform to leverage computer vision for monitoring crop responses in real-time.

Using V7, LIAT implemented a solution involving semi-automated video annotation to label, identify, and track strawberries from collected imagery. This data was used to train AI-powered yield forecasting systems. The results were significant: V7 helped improve model accuracy from 85% to 95% and extended yield prediction time from 3 to 5 weeks ahead of existing systems, leading to higher profits for farmers and reduced waste.

LIAT