  Crop diseases are a major threat to food security, but their rapid
identification remains difficult in many parts of the world due to the lack of
the necessary infrastructure. The combination of increasing global smartphone
penetration and recent advances in computer vision made possible by deep
learning has paved the way for smartphone-assisted disease diagnosis. Using a
public dataset of 54,306 images of diseased and healthy plant leaves collected
under controlled conditions, we train a deep convolutional neural network to
identify 14 crop species and 26 diseases (or absence thereof). The trained
model achieves an accuracy of 99.35% on a held-out test set, demonstrating the
feasibility of this approach. When testing the model on a set of images
collected from trusted online sources - i.e. taken under conditions different
from the images used for training - the model still achieves an accuracy of
31.4%. While this accuracy is much higher than the one based on random
selection (2.6%), a more diverse set of training data is needed to improve the
general accuracy. Overall, the approach of training deep learning models on
increasingly large and publicly available image datasets presents a clear path
towards smartphone-assisted crop disease diagnosis on a massive global scale.
