Banana Shelf-Life Prediction Models and Algorithms

The banana shelf-life prediction is very crucial in banana supply chains. The high accuracy of banana shelf-life prediction not only can reduce the banana waste but also can improve profitability and customer satisfaction. By precisely predicting the banana shelf-life, farmers, retailers and consumers in the banana supply chain can make informed decisions about the storage, transportation and consumption of bananas. Accurate shelf-life prediction can optimize inventory management, reduce spoilage-related losses, and enhance profitability across the banana supply chain. This proposed research aims to develop new models and algorithms to accurately predict banana shelf-life, based on image and spectral data, in addition to the shelf-life historical data of bananas.

Our Objective

  • Develop new models and algorithms to accurately predict banana shelf-life, based on image and spectral data, as well as the shelf-life historical data, by incorporating AI/ML models with time series methods and optimization techniques.

Our Deliverables

  • New shelf-life prediction models and algorithms.
  • Implementable framework for shelf-life prediction.

Discover Our Team

Liu Peng

Adjunct Research Fellow

Yessie Widya Sari

Associate Professor of Biophysics
IPB University

Dase Hunaefi

Researcher
IPB University

Utami Dyah Safitri

Director of Education Administration
IPB University

Yuan Xue Ming

Senior Research Fellow

Nur Hasanah

Secretary of Business School
IPB University

Koh Chaik Ming

Senior Research Fellow

Wulan Tri Wahyuni

Analyst
IPB University

Fauzan

Analyst
IPB University