AI-Powered Avocado Ripeness Detection: A Tasty Solution to Food Waste

Finally, AI That Does Something Truly Valuable: Find Ripe Avocados

Macy has been working for CNET for coming on 2 years. Prior to CNET, Macy received a North Carolina College Media Association award in sports writing. Avocados are delicious, good with a variety of meals and packed full of nutrients. But I don't buy them anymore. Without a doubt, I've pitched whole avocados more than any other produce. Why? Because there's seemingly only a 15-second window between when the avocado is rock hard and complete mush. And I always miss it. But those days may be numbered and it's shockingly thanks to AI. Researchers at Oregon State University and Florida State University have developed a smartphone-powered AI that can tell exactly when an avocado is perfectly ripe. Using more than 1,400 iPhone photos of Hass avocados, the team trained a deep-learning model that can predict firmness, the key sign of ripeness, with over 90% accuracy. The AI analyzes subtle cues in texture, color and shape that most humans would miss. "Avocados are among the most wasted fruits globally due to overripeness," Luyao Ma, an assistant professor at OSU, said. "Our goal was to create a tool that helps consumers and retailers make smarter decisions about when to use or sell avocados." Food waste is one of the most critical global issues. Nearly a third of everything grown, harvested and produced for human consumption never makes it to the table and goes to waste. In the US, federal agencies have pledged to cut that loss in half by 2030, a goal that calls for innovation across the food system, such as an app that can tell you when it's time to eat a fruit. "Avocados are just the beginning," Ma said. "This technology could be applied much more broadly, helping consumers, retailers and distributors make smarter decisions and reduce waste." Don't miss any of our unbiased tech content and lab-based reviews. Add CNET as a preferred Google source on Chrome. The app is not yet available to consumers, but that is the long-term goal. Read more: No Knife, No Problem: How to Safely Remove an Avocado Pit In the meantime, if you're looking for the best avocados at your local market, here are some app-free tips to try:

AI may help you pick the perfect avocado

The days of buying a rock-tough avocado in the hopes of avoiding mushy food waste may soon be over. Machine learning researchers at Oregon State University (OSU) recently designed an artificial intelligence program that visually assesses avocado quality and ripeness. Its creators don't intend to only offer their tool for large farming facilities, however. Instead, the team hopes individuals will wield the AI software in their own kitchen and at the grocery store with some help from the smartphone in their pocket. Avocados are packed with vitamins, minerals, and unsaturated fats. They're also one of the agricultural industry's biggest ecological drains. The water and fertilizer requirements coupled with comparatively small crop yields means that a single avocado possesses about double the carbon footprint as a banana and five times that of an apple. Then, there's the mountain of food waste to consider. Think of all the squishy superfoods you pass over while searching for the perfect produce. The vast majority of those discards wind up in the trash, further exacerbating the avocado's environmental impact. "Avocados are among the most wasted fruits globally due to overripeness. Our goal was to create a tool that helps consumers and retailers make smarter decisions about when to use or sell avocados," explained Luyao Ma, a researcher at OSU and co-author of a study describing their project published in the journal Current Research in Food Science. To help curb the ongoing issue, Ma recently collaborated with fellow researchers at OSU and Florida State University on an AI model that predicts the firmness and internal quality of Hass avocados. Importantly, their program's assessments aren't destructive to the fruit. Instead, they're based entirely on visual details captured in over 1,400 iPhone photos of avocados. Although prior approaches also harnessed images and machine learning for determining food quality, they generally required manual feature selections and traditional algorithmic designs that reduced their accuracy. Other technology like scanners currently being trialed in the UK also focus on individual shoppers. "To overcome these limitations, we used deep learning approaches that automatically capture a broader range of information, including shape, texture, and spatial patterns to enhance the accuracy and robustness of avocado quality predictions," added study co-author In-Hwan Lee. The result is an AI program that currently predicts an avocado's firmness and freshness with respective accuracies of around 92 and 84 percent. Ma, Lee, and their colleagues also believe those numbers will easily improve as more avocado portraits are included into the model. Aside from individual shoppers using an avocado app, the study's authors hope that their technology will also deploy to processing facilities for sorting and grading the fruit at scale. Hypothetically, riper avocados could be selected for shipment to closer retailers. Meanwhile, grocery store employees could themselves use a version of technology to determine which fruit should be displayed first. The implications don't end there, either. "Avocados are just the beginning," said Ma. "This technology could be applied much more broadly, helping consumers, retailers, and distributors make smarter decisions and reduce waste." But while Ma said she first chose to examine avocados due to their high market value and food waste numbers, the decision was also somewhat personal: she confessed to being an avid fan of avocado toast.

Smartphone-powered AI predicts avocado ripeness

Researchers have developed a smartphone-based artificial intelligence system that accurately predicts the ripeness and internal quality of avocados. "Avocados are among the most wasted fruits globally due to overripeness," said Luyao Ma, an assistant professor at Oregon State University. "Our goal was to create a tool that helps consumers and retailers make smarter decisions about when to use or sell avocados." The research team, comprising scientists from Oregon State and Florida State University, trained AI models using more than 1,400 iPhone images of Hass avocados. The system predicted firmness, a key indicator of ripeness, with nearly 92% accuracy, and internal quality (fresh vs. rotten) with over 84% accuracy. The findings were published in the journal Current Research in Food Science. The researchers believe these accuracy rates can be improved as more images are added to the model. They also note that the technology has the potential to assess the ripeness and quality of other types of food. They hope to further develop the technology so that consumers can use it at home to determine the optimal time to eat an avocado, avoiding the disappointment of cutting into one only to find dreaded brown spots. The team also sees potential applications in avocado processing facilities, where the technology could be used to better sort and grade the fruit. For example, if the system detects that a batch is more ripe, it could be shipped to a nearby retailer instead of one further away. Retailers could similarly use the technology to determine which avocados should be sold first based on ripeness. These findings build on previous research that used images and machine learning techniques to assess food quality. However, earlier studies relied on manual feature selection [SN1] and traditional machine learning algorithms, which limited prediction performance, said In-Hwan Lee, a doctoral student working with Ma on the project. "To overcome these limitations, we used deep learning approaches that automatically capture a broader range of information, including shape, texture, and spatial patterns to enhance the accuracy and robustness of avocado quality predictions," Lee said. Ma chose to focus on avocados due to their high market value and high waste rate. She also noted a personal motivation: as a frequent consumer of avocado toast, she was often frustrated by not knowing when avocados were perfectly ripe and cutting into overripe ones. The research addresses a major global challenge: food waste. About 30% of the world's food production is wasted. In response to this challenge, the U.S. Department of Agriculture and the Environmental Protection Agency have set a national goal to reduce food waste by 50% by 2030. "Avocados are just the beginning," Ma said. "This technology could be applied much more broadly, helping consumers, retailers, and distributors make smarter decisions and reduce waste." Zhengao Lee of Florida State University is also a co-author of the paper. Ma and Lee are in the Department of Food Science and Technology in Oregon State's College of Agricultural Sciences. Ma is also affiliated with the Department of Biological and Ecological Engineering.