AI tool for predicting apple shelf life based on transport and packaging
Predictive AI platform simulating packaging and transport conditions to optimize apple shelf life by minimizing bruising, weight loss, and ethylene emissions. Enables growers and shippers to reduce testing cycles and waste, extending produce shelf life.
Overview
This AI tool leverages advanced machine learning models to predict critical apple shelf-life parameters such as bruising, weight loss, and ethylene emissions. By simulating transport vibration and packaging conditions, it evaluates damage risks and recommends optimal packaging designs. This predictive platform aims to help growers and shippers reduce testing cycles, minimize waste, and extend the shelf life of their produce, leading to more efficient supply chain management.
Technical specifications
Key features include:
- Utilizes validated ML models, including XGBoost and neural networks, to simulate packaging and transport scenarios.
- Integrates extensive datasets from Michigan Apple Committee and Project GREEEN studies, covering vibration factors and packaging configurations.
- Provides recommendations on packaging designs using corrugated boxes, reusable plastic containers (RPCs), trays, and cushioning materials.
- Collaborates with major industry partners like Riveridge Produce and North Bay Produce to refine and validate the model with real-world data.
Technology readiness level
Currently, the technology stands at TRL 4, indicating that it has been validated in a lab environment. Future development will focus on retraining ML models with expanded datasets, integrating field data from industry partners, and optimizing the tool for commercial applications in collaboration with Halo.
About Michigan State University
Michigan State University is a major public land‑grant research university with a comprehensive academic portfolio and a large research enterprise. Industry partners engage through an on‑campus U.S. Department of Energy national user facility and shared core laboratories with user access. The university provides a chemical process scale‑up pilot plant on Michigan’s lakeshore, a research and technology park, and a Grand Rapids health innovation campus linking researchers with clinical partners. A statewide extension network supports field deployment and workforce training across Michigan’s manufacturing corridor. Research is backed by competitive federal funding from NSF, NIH, DOE, USDA, and DoD, while dedicated tech transfer and corporate engagement teams—supported by an affiliated research foundation—accelerate IP, licensing, startups, and sponsored research.
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