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.

翻译：基于图像的深度学习为马铃薯贮藏期间的质量监测提供了一种非侵入性、可扩展的解决方案，可应对发芽检测、重量损失估计和货架期预测等关键挑战。本研究在受控温湿度条件下，历时200天采集了图像及对应的重量数据。借助ResNet、VGG、DenseNet和Vision Transformer（ViT）等强大的预训练架构，我们设计了两类专用模型：（1）用于发芽检测的高精度二分类器；（2）用于估计重量损失并高精度预测剩余货架期的先进多分类预测器。DenseNet表现出卓越性能，其发芽检测准确率达98.03%。货架期预测模型在粗粒度类别划分（2-5类）下表现最佳，准确率超过89.83%；而在细粒度划分（6-8类）时，由于类别间视觉差异细微且每类数据有限，准确率有所下降。这些发现证明了将基于图像的模型集成到自动化分拣与库存系统的可行性，可实现早期识别发芽马铃薯，并依据贮藏阶段进行动态分类。实际应用价值包括改进库存管理、实施差异化定价策略以及减少供应链中的食物浪费。尽管预测精确的货架期区间仍具挑战，但聚焦于更宽泛的类别划分可确保模型性能的稳健性。未来研究应致力于开发基于多样化马铃薯品种和贮藏条件的通用模型，以提升其适应性与可扩展性。总体而言，该方法为质量评估提供了一种经济高效、非破坏性的技术手段，有助于提升马铃薯贮藏与流通环节的效率和可持续性。