Abstract
Grapevine varieties are essential for the economies of many wine-producing countries, influencing the production of wine, juice, and the consumption of fruits and leaves. Traditional identification methods, such as ampelography and molecular analysis, have limitations: ampelography depends on expert knowledge and is inherently subjective, while molecular methods are costly and time-intensive. To address these limitations, recent studies have applied deep learning (DL) models to classify grapevine varieties using image data. However, due to the small dataset sizes, these methods often depend on transfer learning from datasets from other domains, e.g., ImageNet1K (IN1K), which can lead to performance degradation due to domain shift and supervision collapse. In this context, self-supervised learning (SSL) methods can be a good tool to avoid this performance degradation, since they can learn directly from data, without external labels. This study presents an evaluation of Masked Autoencoders (MAEs) for identifying grapevine varieties based on field-acquired images. The main contributions of this study include two benchmarks comprising 43 grapevine varieties collected across different seasons, an analysis of MAE's application in the agricultural context, and a performance comparison of trained models across seasons. Our results show that a ViT-B/16 model pre-trained with MAE and the unlabeled dataset achieved an F1 score of 0.7956, outperforming all other models. Additionally, we observed that pre-trained models benefit from long pre-training, perform well under low-data training regime, and that simple data augmentation methods are more effective than complex ones. The study also found that the mask ratio in MAE impacts performance only marginally.
Abstract (translated)
葡萄品种对于许多葡萄酒生产国的经济至关重要，影响着葡萄酒、果汁的生产和水果及叶片的消费。传统的识别方法，如植物学分类（ampelography）和分子分析，存在局限性：植物学分类依赖于专家知识且具有主观性，而分子方法则成本高昂且耗时较长。为解决这些限制，最近的研究开始应用深度学习（DL）模型，利用图像数据对葡萄品种进行分类。然而，由于小规模的数据集，这些方法通常需要从其他领域（例如ImageNet1K (IN1K)）的大型数据集中借用迁移学习，这可能会导致因域转移和监督崩溃而导致性能下降。在这种背景下，自监督学习（SSL）方法可以成为避免这种性能下降的好工具，因为它们可以直接从数据中进行学习，而无需外部标签。 本研究评估了Masked Autoencoders (MAEs) 在基于实地采集图像识别葡萄品种方面的应用。该研究的主要贡献包括两个基准测试集，涵盖了43种不同季节的葡萄品种，对MAE在农业领域的应用进行了分析，并对比了不同季节训练模型的表现情况。我们的结果显示，ViT-B/16模型经过MAE和未标记数据预训练后，在F1得分上达到了0.7956，优于所有其他模型表现。此外，我们还观察到，预训练模型长期预训练效果更佳，在低数据量的训练环境中性能更好，并且简单的数据增强方法比复杂的更为有效。该研究还发现，MAE中的遮罩比率对性能的影响微乎其微。 总的来说，这项工作提供了一种新的自监督学习框架来改善葡萄品种识别任务的有效性，特别关注于小规模数据集和复杂农业环境中的应用挑战。
URL
https://arxiv.org/abs/2506.13335
