论文标题
促进基于图像的水果质量评估的机器学习
Facilitated machine learning for image-based fruit quality assessment
论文作者
论文摘要
基于图像的机器学习模型可用于使农产品的分类和分级更加有效。在许多地区，由于缺乏集中化和自动化后供应链的自动化，实施此类系统可能很困难。利益相关者通常太小，无法专注于机器学习，并且不可用的大型培训数据集。我们为基于预训练的视觉变压器的图像提出了一个机器学习程序。与当前培训卷积神经网络（CNN）的标准标准方法相比，实施更容易实施，因为我们没有（重新）训练深层神经网络。我们根据两个数据集评估了我们的方法，用于苹果缺陷检测和香蕉成熟度估计。我们的模型达到的竞争分类精度等于或低于表现最佳的CNN。同时，它需要减少三倍的训练样本才能达到90％的精度。
Image-based machine learning models can be used to make the sorting and grading of agricultural products more efficient. In many regions, implementing such systems can be difficult due to the lack of centralization and automation of postharvest supply chains. Stakeholders are often too small to specialize in machine learning, and large training data sets are unavailable. We propose a machine learning procedure for images based on pre-trained Vision Transformers. It is easier to implement than the current standard approach of training Convolutional Neural Networks (CNNs) as we do not (re-)train deep neural networks. We evaluate our approach based on two data sets for apple defect detection and banana ripeness estimation. Our model achieves a competitive classification accuracy equal to or less than one percent below the best-performing CNN. At the same time, it requires three times fewer training samples to achieve a 90% accuracy.
