# Data Augmentation Method for Plant Leaf Disease Recognition
## 6 Conclusions  
Recently, weather changes caused by climate change have had a great impact on crop productivity. If climate change continues as it is now, it is expected to be very difficult to maintain current crop production. To address this, deep learning-based plant early diagnosis studies are actively underway, but some rare disease samples have the problem of increasing the bias of learning models because they are difficult to collect.  
Therefore, in this study, we study a data augmentation technique based on the Cycle-consistent Generative Adversarial Network to increase the diversity of rare disease samples by solving the above problem. The main content of the study is to address the problem of imbalances in plant disease data by generating diseased leaf images to be converted from healthy plant leaf images that are commonly collectible. We also propose a Pre-activation Residual Attention Block to improve the representation ability of the model,  
Figure 8: t-SNE visualization of feature vectors extracted from EfficientNet trained on the augmented apple rust dataset.  
which allows the generator to select features that should be considered more important during domain transformation. Furthermore, by learning the background loss function, the unnecessary transformation between the input leaf image and the fake leaf image generated from it was suppressed. Experiments were conducted to verify whether the plant leaf image generated from the proposed method could further improve the performance of the classification model for early diagnosis of plants, and Precision, Recall, and F1 Score were evaluated by indicators. The models used in the experiment were ResNet-18, DenseNet-161, MobileNet-v2, and EfficientNet-b0, all of which were fine-tuned using pre-trained weights. Experimental results confirmed that for all three plant leaves, the highest F1 score was achieved when the EfficientNet and the proposed data augmentation method were used together. In addition, improved FID scores and classification performance were achieved compared to CycleGAN as a whole. Through this, it was confirmed that the data augmentation method proposed in this study improves the generalization performance of various classification models.  
Conceptualization, B.M., T.K. and D.S. ([PERSON]); methodology, B.M., T.K. and D.S. ([PERSON]); software, B.M. and T.K.; validation, T.K., D.S. ([PERSON]) and D.S. ([PERSON]); formal analysis, B.M., T.K. and D.S. ([PERSON]); investigation, B.M.; resources, B.M. and T.K.; data curation, B.M., T.K., D.S. ([PERSON]) and D.S. ([PERSON]); writing--original draft preparation, B.M.; writing--review and editing, T.K., D.S. ([PERSON]) and D.S. ([PERSON]); visualization, B.M.; supervision, D.S. ([PERSON]); project administration, D.S. ([PERSON]); funding acquisition, D.S. ([PERSON]). All authors have read and agreed to the published version of the manuscript.  
Not applicable.  
The PlantVillage data set is available at the following link: [[https://github.com/spMohanty/PlantVillage-Dataset/tree/master/raw](https://github.com/spMohanty/PlantVillage-Dataset/tree/master/raw)]([https://github.com/spMohanty/PlantVillage-Dataset/tree/master/raw](https://github.com/spMohanty/PlantVillage-Dataset/tree/master/raw)) (accessed on 21 November 2022).  
The authors declare no conflict of interest.