# An In-field Automatic Wheat Disease Diagnosis System

arXiv: https://arxiv.org/abs/1710.08299 · HTML: https://mlsanity.com/paper/1710.08299/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 26 Sep 2017.

## Built on by (most cited first, 4 of 4)

- [Label-Efficient Learning in Agriculture: A Comprehensive Review](https://mlsanity.com/paper/2305.14691/) (2023)
- [Weakly Supervised Attention-based Models Using Activation Maps for Citrus Mite and Insect Pest Classification](https://mlsanity.com/paper/2110.00881/) (2021)
- [Self-Supervised Learning for Robotic Leaf Manipulation: A Hybrid Geometric-Neural Approach](https://mlsanity.com/paper/2505.03702/) (2025)
- [Automatic Detection of Rice Disease in Images of Various Leaf Sizes](https://mlsanity.com/paper/2206.07344/) (2022)

## Abstract (arXiv)

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.