LeafGAN: An Effective Data Augmentation Method for Practical Plant Disease Diagnosis
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TL;DR: This article proposes LeafGAN, a novel image-to-image translation system with own attention mechanism, which generates countless diverse and high-quality training images; it works as an efficient data augmentation for the diagnosis classifier.
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Abstract: 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% from the baseline. In contrast, LeafGAN boosted the diagnostic performance by 7.4%. We also visually confirmed that the generated images by our LeafGAN were much better quality and more convincing than those generated by vanilla CycleGAN. The code is available publicly at https://github.com/IyatomiLab/LeafGAN.
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Citations
DCGAN-Based Data Augmentation for Tomato Leaf Disease Identification
Qiufeng Wu,Yiping Chen,Jun Meng +2 more
TL;DR: Experiments with tomato leaf disease identification show that DCGAN can generate data that approximate to real images, which can be used to provide a larger data set for the training of large neural networks, and improve the performance of the recognition model through highly discriminating image generation technology.
Generative adversarial networks (GANs) for image augmentation in agriculture: A systematic review
TL;DR: An overview of GANs in agriculture can be found in this paper , where the authors present an overview of the evolution of generative adversarial network (GAN) architectures followed by a first systematic review of various applications in agriculture and food systems.
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Trends in vision-based machine learning techniques for plant disease identification: A systematic review
TL;DR: A comprehensive review of vision-based machine learning techniques for plant disease detection is provided in this article , where the saliency of approaches is evaluated based on the availability of public datasets and their suitability in real-time applications.
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Data augmentation for deep learning based semantic segmentation and crop-weed classification in agricultural robotics
TL;DR: Comprehensive experimental evaluations and ablation studies show that the proposed framework can effectively improve segmentation accuracies, and the enhancements made over the original RICAP actually contribute to the performance gain.
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ResTS: Residual Deep interpretable architecture for plant disease detection
TL;DR: ResTS (Residual Teacher/Student) as discussed by the authors is a tertiary adaptation of the teacher/student architecture for diagnosis of the plant disease, which can yield finer visualizations of symptoms of the disease.
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