Quality Detection of Stored Potatoes via Transfer Learning: A CNN and Vision Transformer Approach

TL;DR

This study develops transfer learning-based CNN and Vision Transformer models for non-invasive, image-based detection of potato sprouting, weight loss, and shelf-life prediction, demonstrating high accuracy and practical utility in storage management.

Contribution

It introduces specialized deep learning models for potato quality assessment, achieving high accuracy and showcasing the integration of transfer learning with image analysis for storage monitoring.

Findings

DenseNet achieved 98.03% sprout detection accuracy.

Shelf-life prediction accuracy exceeded 89.83% for broader classes.

Finer class divisions reduced accuracy due to subtle visual differences.

Abstract

Image-based deep learning provides a non-invasive, scalable solution for monitoring potato quality during storage, addressing key challenges such as sprout detection, weight loss estimation, and shelf-life prediction. In this study, images and corresponding weight data were collected over a 200-day period under controlled temperature and humidity conditions. Leveraging powerful pre-trained architectures of ResNet, VGG, DenseNet, and Vision Transformer (ViT), we designed two specialized models: (1) a high-precision binary classifier for sprout detection, and (2) an advanced multi-class predictor to estimate weight loss and forecast remaining shelf-life with remarkable accuracy. DenseNet achieved exceptional performance, with 98.03% accuracy in sprout detection. Shelf-life prediction models performed best with coarse class divisions (2-5 classes), achieving over 89.83% accuracy, while accuracy declined for finer divisions (6-8 classes) due to subtle visual differences and limited data per class. These findings demonstrate the feasibility of integrating image-based models into automated sorting and inventory systems, enabling early identification of sprouted potatoes and dynamic categorization based on storage stage. Practical implications include improved inventory management, differential pricing strategies, and reduced food waste across supply chains. While predicting exact shelf-life intervals remains challenging, focusing on broader class divisions ensures robust performance. Future research should aim to develop generalized models trained on diverse potato varieties and storage conditions to enhance adaptability and scalability. Overall, this approach offers a cost-effective, non-destructive method for quality assessment, supporting efficiency and sustainability in potato storage and distribution.