  Crop diseases are responsible for the major production reduction and economic
losses in agricultural industry world- wide. Monitoring for health status of
crops is critical to control the spread of diseases and implement effective
management. This paper presents an in-field automatic wheat disease diagnosis
system based on a weakly super- vised deep learning framework, i.e. deep
multiple instance learning, which achieves an integration of identification for
wheat diseases and localization for disease areas with only image-level
annotation for training images in wild conditions. Furthermore, a new in-field
image dataset for wheat disease, Wheat Disease Database 2017 (WDD2017), is
collected to verify the effectiveness of our system. Under two different
architectures, i.e. VGG-FCN-VD16 and VGG-FCN-S, our system achieves the mean
recognition accuracies of 97.95% and 95.12% respectively over 5-fold
cross-validation on WDD2017, exceeding the results of 93.27% and 73.00% by two
conventional CNN frameworks, i.e. VGG-CNN-VD16 and VGG-CNN-S. Experimental
results demonstrate that the proposed system outperforms conventional CNN
architectures on recognition accuracy under the same amount of parameters,
meanwhile main- taining accurate localization for corresponding disease areas.
Moreover, the proposed system has been packed into a real-time mobile app to
provide support for agricultural disease diagnosis.
