Although potatoes are a carbohydrate, they are botanically a vegetable, and subject to all the same vegetables quality control methods and challenges of any other vegetable. That means that for growers, marketing companies, wholesalers, and retailers – optimizing potato harvesting and potato quality control is super important. A high quality potato, and that usually means firm potatoes, large potatoes and those without visible or internal potato defects, will boost profits, and help you to keep good relationships with your buyers, no matter where you land on the value chain. However, today’s potato quality control methods are largely manual, non-standardized and limited to what can be seen with the human eye.
Potatoes Quality Control has evolved significantly in recent years, with new technologies enabling more precise evaluation of potato quality across the entire supply chain. Advanced sensing equipment can now detect internal defects before they become visible, allowing for early intervention and better inventory management. For growers and distributors, maintaining consistent potato quality requires careful monitoring of multiple factors including size uniformity, skin condition, internal composition, and freedom from disease. Modern potato quality control systems can automatically assess these parameters, providing real-time data that helps stakeholders make informed decisions about storage conditions, shipping timing, and market distribution. This systematic approach to quality control ensures that only the highest quality potatoes reach consumers, reducing waste and maximizing profitability throughout the supply chain.
That’s where Clarifresh can support your business, using AI and computer vision technology to provide a consistent and accurate fruit & vegetables quality control process for stakeholders working with potato crops, whether that’s at the farming and growing stages, all the way through to retailers looking to delight the end customer.
The Clarifresh platform also integrates with 3rd-party technology to evaluate external tomato attributes. Learn more here.