Searching for the perfect avocado at the grocery store is an acquired skill. You might think you picked the perfect avocado, only to bring it home and find out it is not close to ripe, or, even worse, that it is rotten to the core. This blunder is more than just one’s own personal produce tragedy. Unripe avocados contribute to food waste, with overripe avocados accounting for ~40% of global avocado production. Researchers at Oregon State University and Florida State University posed a valuable question in the face of this dilemma: What if machine vision could predict the ripeness and internal qualities of avocados? With more than 1400 iPhone images of Hass avocados, the team used an AI model to determine the ripeness and quality of the fruit. The avocados were placed in a photo box with strategically positioned lights. For each fruit, 10 images were taken, rotating the avocado between each shot to capture surface variation within the same sample and to evaluate whether deep learning models could accurately predict ripeness based on the images. Spotlight illustration courtesy of iStock.com/erhui1979. Avocado illustration courtesy of iStock.com/Baluchis. The system predicted firmness, which contributes to ripeness, with 92% accuracy, and internal quality with >84% accuracy. These rates improve when more images are added to the model. Using established data on shelf life at room temperature, the calculated firmness translated into more accurate estimates of remaining shelf life. Beyond benefitting the guacamole cornerstone, this model has the potential to assess the ripeness and quality of other foods as well. Once food industries and consumers have access to predicted firmness, remaining shelf life, and internal quality, they can make data-driven, evidence-based decisions for smarter consumption and distribution planning. This, in turn, can help to reduce food waste and loss. Avocado processing facilities could use this technology to sort and grade the fruit. With the newly gathered knowledge, they can send riper batches to nearby retailers and the freshest fruit to grocers farther away. This would lower the chances of the avocados being tossed in the compost pile. Retailers could similarly use the technology to determine which of their newly purchased avocados to move to the front of the shelves. This technology doesn’t have to be limited to manufacturers and retailers; the researchers plan to develop it to be consumer-friendly, allowing average Joes to use the tech to increase confidence in selection and prevent their consistent avocado-induced panic in the middle of the produce aisle. This research was published in Current Research in Food Science (www.doi.org/10.1016/j.crfs.2025.101196).