  This paper proposes an unsupervised anomaly detection technique for
image-based plant disease diagnosis. The construction of large and publicly
available datasets containing labeled images of healthy and diseased crop
plants led to growing interest in computer vision techniques for automatic
plant disease diagnosis. Although supervised image classifiers based on deep
learning can be a powerful tool for plant disease diagnosis, they require a
huge amount of labeled data. The data mining technique of anomaly detection
includes unsupervised approaches that do not require rare samples for training
classifiers. We propose an unsupervised anomaly detection technique for
image-based plant disease diagnosis that is based on the reconstructability of
colors; a deep encoder-decoder network trained to reconstruct the colors of
\textit{healthy} plant images should fail to reconstruct colors of symptomatic
regions. Our proposed method includes a new image-based framework for plant
disease detection that utilizes a conditional adversarial network called
pix2pix and a new anomaly score based on CIEDE2000 color difference.
Experiments with PlantVillage dataset demonstrated the superiority of our
proposed method compared to an existing anomaly detector at identifying
diseased crop images in terms of accuracy, interpretability and computational
efficiency.
