用图像模型检测土豆发芽和预测保质期，准确率超98%。
Quality Detection of Stored Potatoes via Transfer Learning: A CNN and Vision Transformer Approach
- 用预训练的CNN和ViT模型，设计二分类器识别发芽土豆。
- 发芽检测准确率达98.03%，保质期粗分类准确超89.83%。
- 适合农业供应链用于自动分拣与动态定价，减少浪费。
基于图像的深度学习为存储期间土豆质量监测提供了非侵入、可扩展的解决方案，解决了发芽检测、重量损失估算和保质期预测等关键挑战。本研究在受控温湿度条件下，持续200天采集图像与对应重量数据。利用ResNet、VGG、DenseNet及视觉变换器（ViT）等强大预训练架构，构建了两个专用模型：（1）高精度二分类器用于发芽检测；（2）多分类预测器用于估计重量损失并预测剩余保质期。DenseNet在发芽检测中表现优异，准确率达98.03%。保质期预测在粗分类（2-5类）下效果最佳，准确率超过89.83%，而细分类（6-8类）因视觉差异微小且每类数据有限，准确率下降。结果表明，图像模型可集成至自动化分拣与库存系统，实现发芽土豆的早期识别和按存储阶段动态分类。实际应用包括优化库存管理、差异化定价策略，降低全链条食物浪费。尽管精确保质期预测仍具挑战，聚焦粗分类可保证稳健性能。未来研究应构建在多样品种与存储条件下训练的通用模型，提升适应性与可扩展性。总体而言，该方法提供了一种低成本、无损的质量评估方式，助力土豆存储与配送的效率与可持续性。
原文摘要 · Abstract (English)
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.
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