Amazon taps AI, ML to keep bad apples from its shelves

In an interview, Rajeev Rastogi, vice-president of ML at Amazon India, said the company has developed computer vision programs that recognize defects such as cuts and scratches on tomatoes and onions to figure out when they have gone bad. The system uses a mix of convolutional neural networks (CNNs) and visual transformer (ViTs) algorithms. CNNs are deep learning algorithms that can take image input and assign importance to various aspects of that image, while ViTs are specialized versions of transformer algorithms, which can weigh the significance of each part of data it gets. "In our grocery business, produce quality is the single-most important customer input and the number one driver of repeat purchase," Rastogi said. "Currently, quality is processed manually, which doesn't really scale. It's also very error-prone, is costly and doesn't have high repeatability. So, we developed a computer vision system for grading fresh produce quality by analysing images of produce," he said.