Towards automatic field plant disease recognition

Plant disease is a significant threat to food security and subsistence farmers. Despite the rapid development of automatic recognition of plant disease under controlled laboratory conditions since the employment of deep learning technology, it is still quite challenging to distinguish plant disease under uncontrolled field conditions. In this paper, based on a backbone convolutional neural network (CNN), we propose an improved CNN model towards field plant disease recognition (FPDR) by exploring the potential and generalization capabilities of the CNN model. To train the model, we propose background replacing to make the model more robust to background distraction, and leaf resizing to deal with inconsistent size and location of disease symptoms. Both background replacing and leaf resizing are used as data augmentation methods of the improved model. To further enhance the feature discriminativeness, we propose channel orthogonal constraint to improve the ability of feature to distinguish similar categories, and utilize species information as an auxiliary species classification task. In addition, we collect 665 plant disease images under field conditions, namely Field-PlantVillage (Field-PV) to remedy for lack of in-the-field images. The Field-PV is only used as an independent test set to evaluate the performance of the method applied to FPDR. Our improved CNN model improves the FPDR accuracy on Field-PV from 41.81% to 72.03%, though only the PlantVillage dataset is used for training. Experimental result on the PlantVillage achieves the state of the art performance (99.84%). Code and data are available at https://github.co m/PatrickGui/FPDR/tree/master.

Automated summary

This paper proposes an improved CNN model for field plant disease recognition, which enhances the recognition accuracy through data augmentation and feature optimization.