Abstract: Crop diseases are responsible for the major production reduction and economic losses in agricultural industry worldwide 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 supervised deep learning framework, ie 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, ie VGG-FCN-VD16 and VGG-FCN-S, our system achieves the mean recognition accuracies of 9795% and 9512% respectively over 5-fold cross-validation on WDD2017, exceeding the results of 9327% and 7300% by two conventional CNN frameworks, ie 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 maintaining 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
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