Facilitated machine learning for image-based fruit quality assessment
This work addresses the problem of small-scale stakeholders in agriculture who lack machine learning expertise and large datasets, offering a more accessible solution, though it is incremental as it builds on existing pre-trained models.
The paper tackles the challenge of implementing image-based machine learning for fruit quality assessment in decentralized agricultural supply chains by proposing a method using pre-trained Vision Transformers, which achieves competitive classification accuracy (within 1% of CNNs) and requires three times fewer training samples to reach 90% accuracy.
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.