Ai-powered shelf life and ripeness assessment technology for fruits and vegetables

Overview

This technology addresses a major source of food waste by providing accurate, automated assessment of fruit and vegetable ripeness and remaining shelf life. Current industry grading relies primarily on color, which is an incomplete indicator of freshness. By incorporating texture information alongside color and capturing images under standardized lighting conditions, the AI model achieves dramatically higher accuracy with far less training data than conventional AI vision systems. The solution is delivered through a patent-pending imaging platform called Technogym and has been validated on ten common produce types, with accuracy ranging from 86% to 100%.

Technical specifications

• Combined color and texture analysis captures ripeness indicators that color alone misses, improving grading accuracy for both single-color and multi-colored produce
• Uniform lighting environment reduces image variability, enabling effective AI training with as few as 80 images per item
• Phone-based AI application built on the patent-pending Technogym platform, making the system portable and accessible
• Validated accuracy across ten produce types: Avocado 88%, Banana 99%, Broccoli 100%, Carrot 93%, Eggplant 93%, Lemon 97%, Pear 86%, Potato 86%, Tomato 86%, Zucchini 96%
• Planned digital twin model to predict how temperature, humidity, CO2, and ethylene levels affect shelf life, enabling logistics-condition optimization
• Daily imaging validation protocol using Go Micro capture devices under room-temperature conditions to track produce deterioration over time

Technology readiness level

The core AI ripeness assessment has been demonstrated and validated through controlled experiments on ten fruit and vegetable varieties, achieving 86-100% accuracy with minimal training datasets. The next development phase, estimated at three months, focuses on building a computational digital twin of produce behavior to model the effects of storage variables such as temperature, humidity, CO2, and ethylene on shelf life, creating a predictive tool for supply chain and logistics planning. The technology is ready for pilot deployment and further co-development with partners in the fresh produce, retail, and logistics sectors.