An In-field Automatic Wheat Disease Diagnosis System

TL;DR

This paper introduces a deep learning-based in-field wheat disease diagnosis system that accurately identifies and localizes diseases using only image-level annotations, verified on a new dataset and deployed in a mobile app.

Contribution

It presents a novel weakly supervised deep learning framework for wheat disease diagnosis and localization, along with a new dataset and real-time mobile application.

Findings

Achieves over 97% accuracy with VGG-FCN-VD16 architecture.

Outperforms conventional CNNs in recognition accuracy.

Maintains accurate localization of disease areas.

Abstract

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.