  Contemporary Artificial Intelligence technologies allow for the employment of
Computer Vision to discern good crops from bad, providing a step in the
pipeline of selecting healthy fruit from undesirable fruit, such as those which
are mouldy or gangrenous. State-of-the-art works in the field report high
accuracy results on small datasets (<1000 images), which are not representative
of the population regarding real-world usage. The goals of this study are to
further enable real-world usage by improving generalisation with data
augmentation as well as to reduce overfitting and energy usage through model
pruning. In this work, we suggest a machine learning pipeline that combines the
ideas of fine-tuning, transfer learning, and generative model-based training
data augmentation towards improving fruit quality image classification. A
linear network topology search is performed to tune a VGG16 lemon quality
classification model using a publicly-available dataset of 2690 images. We find
that appending a 4096 neuron fully connected layer to the convolutional layers
leads to an image classification accuracy of 83.77%. We then train a
Conditional Generative Adversarial Network on the training data for 2000
epochs, and it learns to generate relatively realistic images. Grad-CAM
analysis of the model trained on real photographs shows that the synthetic
images can exhibit classifiable characteristics such as shape, mould, and
gangrene. A higher image classification accuracy of 88.75% is then attained by
augmenting the training with synthetic images, arguing that Conditional
Generative Adversarial Networks have the ability to produce new data to
alleviate issues of data scarcity. Finally, model pruning is performed via
polynomial decay, where we find that the Conditional GAN-augmented
classification network can retain 81.16% classification accuracy when
compressed to 50% of its original size.
