  Data augmentation is a powerful tool for improving deep learning-based image
classifiers for plant stress identification and classification. However,
selecting an effective set of augmentations from a large pool of candidates
remains a key challenge, particularly in imbalanced and confounding datasets.
We propose an approach for automated class-specific data augmentation using a
genetic algorithm. We demonstrate the utility of our approach on soybean
[Glycine max (L.) Merr] stress classification where symptoms are observed on
leaves; a particularly challenging problem due to confounding classes in the
dataset. Our approach yields substantial performance, achieving a
mean-per-class accuracy of 97.61% and an overall accuracy of 98% on the soybean
leaf stress dataset. Our method significantly improves the accuracy of the most
challenging classes, with notable enhancements from 83.01% to 88.89% and from
85.71% to 94.05%, respectively.
  A key observation we make in this study is that high-performing augmentation
strategies can be identified in a computationally efficient manner. We
fine-tune only the linear layer of the baseline model with different
augmentations, thereby reducing the computational burden associated with
training classifiers from scratch for each augmentation policy while achieving
exceptional performance. This research represents an advancement in automated
data augmentation strategies for plant stress classification, particularly in
the context of confounding datasets. Our findings contribute to the growing
body of research in tailored augmentation techniques and their potential impact
on disease management strategies, crop yields, and global food security. The
proposed approach holds the potential to enhance the accuracy and efficiency of
deep learning-based tools for managing plant stresses in agriculture.
