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LeafGAN: An Effective Data Augmentation Method for Practical Plant Disease Diagnosis - 2018

Author(s) Name:  Quan Huu Cap; Hiroyuki Uga; Satoshi Kagiwada; Hitoshi Iyatomi
Journal name:  IEEE Transactions on Automation Science and Engineering
Conferrence name:  
Publisher name:  IEEE
DOI:  10.1109/TASE.2020.3041499
Research Area:  Machine Learning

Many applications for the automated diagnosis of plant disease have been developed based on the success of deep learning techniques. However, these applications often suffer from overfitting, and the diagnostic performance is drastically decreased when used on test data sets from new environments. In this article, we propose LeafGAN, a novel image-to-image translation system with own attention mechanism. LeafGAN generates a wide variety of diseased images via transformation from healthy images, as a data augmentation tool for improving the performance of plant disease diagnosis. Due to its own attention mechanism, our model can transform only relevant areas from images with a variety of backgrounds, thus enriching the versatility of the training images. Experiments with five-class cucumber disease classification show that data augmentation with vanilla CycleGAN cannot help to improve the generalization, i.e., disease diagnostic performance increased by only 0.7 perecnt from the baseline. In contrast, LeafGAN boosted the diagnostic performance by 7.4 perecnt We also visually confirmed that the generated images by our LeafGAN were much better quality and more convincing than those generated by vanilla CycleGAN.

Volume Information:  Page(s): 1 - 10
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