A fruit processor needs a classifier to categorize apples into three varieties. They have 150 labeled images per class and applied transfer learning using an ImageNet-pretrained network. After tuning, the best results were: 68% training accuracy and 67% validation accuracy. What action should the specialist take to improve accuracy?

The similar training (68%) and validation (67%) accuracy, both relatively low, indicates high bias (underfitting). This means the model is not complex enough or hasn't learned sufficient patterns from the data. Collecting more labeled training images is the most effective way to reduce bias and improve the model's ability to generalize, especially with only 150 images per class, which is a relatively small dataset for image classification. Automated hyperparameter optimization (HPO) might offer minor improvements but won't address the fundamental issue of high bias. Switching to a deeper network could increase variance (overfitting) if the dataset remains small. Retraining with the same architecture and dataset will likely yield similar results.