# Advancing Image-Based Grapevine Variety Classification with a New Benchmark and Evaluation of Masked Autoencoders

arXiv: https://arxiv.org/abs/2506.13335 · HTML: https://mlsanity.com/paper/2506.13335/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 35 of its 89 references that are in the map.
- First posted on arXiv 16 Jun 2025.

## Builds on (most cited first, 30 of 35)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
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- [Swin Transformer: Hierarchical Vision Transformer using Shifted Windows](https://mlsanity.com/paper/2103.14030/) (2021)
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- [Momentum Contrast for Unsupervised Visual Representation Learning](https://mlsanity.com/paper/1911.05722/) (2019)
- [Masked Autoencoders Are Scalable Vision Learners](https://mlsanity.com/paper/2111.06377/) (2021)
- [EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks](https://mlsanity.com/paper/1905.11946/) (2019)
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- [MobileNetV2: Inverted Residuals and Linear Bottlenecks](https://mlsanity.com/paper/1801.04381/) (2018)
- [Bootstrap your own latent: A new approach to self-supervised Learning](https://mlsanity.com/paper/2006.07733/) (2020)
- [Training data-efficient image transformers & distillation through attention](https://mlsanity.com/paper/2012.12877/) (2020)
- [Going Deeper with Convolutions](https://mlsanity.com/paper/1409.4842/) (2014)
- [Exploring Simple Siamese Representation Learning](https://mlsanity.com/paper/2011.10566/) (2020)
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- [Similarity of Neural Network Representations Revisited](https://mlsanity.com/paper/1905.00414/) (2019)
- [Inception-v4, Inception-ResNet and the Impact of Residual Connections on Learning](https://mlsanity.com/paper/1602.07261/) (2016)
- [VICReg: Variance-Invariance-Covariance Regularization for Self-Supervised Learning](https://mlsanity.com/paper/2105.04906/) (2021)
- [Dense Contrastive Learning for Self-Supervised Visual Pre-Training](https://mlsanity.com/paper/2011.09157/) (2020)
- [A Cookbook of Self-Supervised Learning](https://mlsanity.com/paper/2304.12210/) (2023)
- [CrossTransformers: spatially-aware few-shot transfer](https://mlsanity.com/paper/2007.11498/) (2020)
- [Benchmarking Representation Learning for Natural World Image Collections](https://mlsanity.com/paper/2103.16483/) (2021)
- [An open access repository of images on plant health to enable the development of mobile disease diagnostics](https://mlsanity.com/paper/1511.08060/) (2015)
- [TransFG: A Transformer Architecture for Fine-grained Recognition](https://mlsanity.com/paper/2103.07976/) (2021)
- [Are Large-scale Datasets Necessary for Self-Supervised Pre-training?](https://mlsanity.com/paper/2112.10740/) (2021)
- [Learning from Few Examples: A Summary of Approaches to Few-Shot Learning](https://mlsanity.com/paper/2203.04291/) (2022)
- [The Hidden Uniform Cluster Prior in Self-Supervised Learning](https://mlsanity.com/paper/2210.07277/) (2022)
- [A survey on Semi-, Self- and Unsupervised Learning for Image Classification](https://mlsanity.com/paper/2002.08721/) (2020)

## Abstract (arXiv)

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.