TITLE: Uncovering bias in the PlantVillage dataset 

ABSTRACT: We report our investigation on the use of the popular PlantVillage dataset
for training deep learning based plant disease detection models. We trained a
machine learning model using only 8 pixels from the PlantVillage image
backgrounds. The model achieved 49.0% accuracy on the held-out test set, well
above the random guessing accuracy of 2.6%. This result indicates that the
PlantVillage dataset contains noise correlated with the labels and deep
learning models can easily exploit this bias to make predictions. Possible
approaches to alleviate this problem are discussed.
We report our investigation on the use of the popular PlantVillage dataset
for training deep learning based plant disease detection models. We trained a
machine learning model using only 8 pixels from the PlantVillage image
backgrounds. The model achieved 49.0% accuracy on the held-out test set, well
above the random guessing accuracy of 2.6%. This result indicates that the
PlantVillage dataset contains noise correlated with the labels and deep
learning models can easily exploit this bias to make predictions. Possible
approaches to alleviate this problem are discussed.
Uncovering bias in the PlantVillage dataset
http://arxiv.org/abs/2206.04374v1