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.
Abstract (translated)
基于图像的深度学习为监测储存期间土豆质量提供了一种非侵入性且可扩展的解决方案，解决了诸如发芽检测、重量损失估计和保质期预测等关键挑战。在这项研究中，在受控温度和湿度条件下收集了持续200天的图像和相应的重量数据。我们利用ResNet、VGG、DenseNet和Vision Transformer（ViT）这些强大的预训练架构，设计了两个专门化的模型：一是用于发芽检测的高精度二元分类器；二是用于估计重量损失并预测剩余保质期的先进多类预测器，并且它们都表现出非常高的准确性。 实验结果表明，DenseNet在发芽检测方面取得了优异的表现，准确率达到98.03%。对于保质期预测模型，在粗略类别划分（2-5个类别）时表现最佳，准确率超过89.83%，但在更细的类别划分（6-8个类别）中，由于细微的视觉差异和每个类别的数据有限，其准确性有所下降。 这些发现证明了将基于图像的模型整合到自动化分拣和库存系统中的可行性，能够实现对发芽土豆的早期识别，并根据储存阶段进行动态分类。实际应用包括改进库存管理、差异化定价策略以及减少供应链中的食品浪费。虽然预测精确的保质期间隔仍然具有挑战性，但专注于更广泛的类别划分可以确保模型性能稳定。 未来的研究应该致力于开发适用于各种土豆品种和存储条件的一般化模型，以增强适应性和可扩展性。总体而言，这种方法提供了一种低成本且非破坏性的质量评估方法，支持了土豆储存与分销的效率和可持续性。
URL
https://arxiv.org/abs/2601.00645
