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

arXiv: https://arxiv.org/abs/2002.10100 · HTML: https://mlsanity.com/paper/2002.10100/

## Key facts

- Cited by 18 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 24 Feb 2020.

## Built on by (most cited first, 18 of 18)

- [Generative AI in Agriculture: Creating Image Datasets Using DALL.E's Advanced Large Language Model Capabilities](https://mlsanity.com/paper/2307.08789/) (2023)
- [Embrace Limited and Imperfect Training Datasets: Opportunities and Challenges in Plant Disease Recognition Using Deep Learning](https://mlsanity.com/paper/2305.11533/) (2023)
- [Weakly Supervised Attention-based Models Using Activation Maps for Citrus Mite and Insect Pest Classification](https://mlsanity.com/paper/2110.00881/) (2021)
- [Generative Adversarial Networks for Image Augmentation in Agriculture: A Systematic Review](https://mlsanity.com/paper/2204.04707/) (2022)
- [RAFA-Net: Region Attention Network For Food Items And Agricultural Stress Recognition](https://mlsanity.com/paper/2410.12718/) (2024)
- [CWD30: A Comprehensive and Holistic Dataset for Crop Weed Recognition in Precision Agriculture](https://mlsanity.com/paper/2305.10084/) (2023)
- [Inside Out: Transforming Images of Lab-Grown Plants for Machine Learning Applications in Agriculture](https://mlsanity.com/paper/2211.02972/) (2022)
- [Towards Robust Plant Disease Diagnosis with Hard-sample Re-mining Strategy](https://mlsanity.com/paper/2309.01903/) (2023)
- [Leaf-Based Plant Disease Detection and Explainable AI](https://mlsanity.com/paper/2404.16833/) (2023)
- [PotatoGANs: Utilizing Generative Adversarial Networks, Instance Segmentation, and Explainable AI for Enhanced Potato Disease Identification and Classification](https://mlsanity.com/paper/2405.07332/) (2024)
- [Investigation to answer three key questions concerning plant pest identification and development of a practical identification framework](https://mlsanity.com/paper/2407.18000/) (2024)
- [AgRegNet: A Deep Regression Network for Flower and Fruit Density Estimation, Localization, and Counting in Orchards](https://mlsanity.com/paper/2409.17400/) (2024)
- [Progressive Self-Distillation for Ground-to-Aerial Perception Knowledge Transfer](https://mlsanity.com/paper/2208.13404/) (2022)
- [Distance Guided Generative Adversarial Network for Explainable Binary Classifications](https://mlsanity.com/paper/2312.17538/) (2023)
- [Class-specific Data Augmentation for Plant Stress Classification](https://mlsanity.com/paper/2406.13081/) (2024)
- [Robust Plant Disease Diagnosis with Few Target-Domain Samples](https://mlsanity.com/paper/2510.12909/) (2025)
- [Interpretable Plant Leaf Disease Detection Using Attention-Enhanced CNN](https://mlsanity.com/paper/2512.17864/) (2025)
- [XAI and Few-shot-based Hybrid Classification Model for Plant Leaf Disease Prognosis](https://mlsanity.com/paper/2603.06676/) (2026)

## Abstract (arXiv)

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 datasets from new environments. In this paper, 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. Thanks 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 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.