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13% of food is lost between harvest and retail across the globe, resulting in unnecessary food shortages as well as wasted water, energy, land, labor, and money.
Improve the precision and reliability of automated vision systems to optimize land use, improve produce grading, and reduce packaging errors.
Small pockets of crop disease in a large field — or blemished berries on a sorting line — are tough to detect by human eye and end up affecting quality and yield. But existing rule-based vision systems are either too inflexible or inconsistent to make a big impact.
Leverage Akridata’s Inspection Studio to build and deploy reliable crop and produce inspection models that are based on state-of-the-art deep learning and work with your existing imaging hardware.
Don’t have an in-house Data Science team for produce defect detection?
Akridata Edge provides ready-to-use agriculture models that have been rigorously tested and refined using millions of plant images, ensuring accurate defect detection.