An In-field Automatic Wheat Disease Diagnosis System
By Jiang Lu, Jie Hu, Guannan Zhao, Fenghua Mei, Changshui Zhang. Computers and Electronics in Agriculture, 2017
347 citations recorded by the source corpus.
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, 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 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.
Subfields: Plant Science
View Paper: https://www.semanticscholar.org/paper/fe9ad6300ef634c177393ace3903545b75d35576
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Plant Science
- Johan Potgieter
- Muhammad Hammad Saleem
- Shujuan Zhang
- Marcin Wozniak
- Geeta Rani
- Sangeeta Vaibhav Meena
- Lili Li
- Bin Wang
Citations in the corpus
- Plant Disease Detection and Classification by Deep Learning (2019, methodology): “In most of the above approaches, the selected datasets considered plain backgrounds which are not realistic scenarios for identification and classification of the diseases [25,27,32,56–58,60,61,65,72,77,78], except for a few of them that have considered the original backgrounds [35,59,68,70,73,74].”
- Plant Disease Detection and Classification by Deep Learning—A Review (2021, methodology): “[40] realized that wheat disease detection by using VGG-FCN and VGG-CNN model and visualized the module features.”
- A Survey of Deep Convolutional Neural Networks Applied for Prediction of Plant Leaf Diseases (2021, methodology): “Exemplified as researchers in [38] used the Theano framework by running their code on a Geforce GTX 1080 GPU for automatic disease diagnosis in a wheat field.”