## 7 Conclusions  
This paper presented a data augmentation framework to improve semantic segmentation accuracy based on style transfer using GANs of two types. In particular, this study specifically addressed data generation of crop images for remote and smart farming to reduce the burden associated with image annotation. The proposed framework comprised pix2 pixHD, CUT, and DeepLabV3+ as a conditional GAN, an unconditional GAN, and a semantic segmentation network. We used the Cityscapes public benchmark dataset and two originally collected benchmark datasets termed Rice blast and Soybeans. In addition, we conducted four experiments using the source images and augmented images from the combination of two GANs. The mIoU scores for the public benchmark dataset improved by 0.03 for the training subset, while remaining similar on the test subset. For the first original benchmark dataset, the mIoU scores improved by 0.01 for the test subset, while they dropped by 0.03 for the training subset. The mIoU scores for the second original benchmark dataset improved by 0.18 for the training subset and 0.03 for the test subset. These results demonstrated the effectiveness of our proposed framework using augmented images generated from two GANs in response to dataset combinations.  
As future work, we must evaluate the effectiveness of our framework for use with various agricultural benchmark datasets. We will analyze the replacement of GANs and semantic segmentation backbones by considering a tradeoff between the accuracy and computational cost. We will consider the introduction of an automatic parameter optimization method for improving accuracy. Finally, we would like to collaborate in the application of our framework with a weed-removing robot or a harvesting robot to achieve remote and smart farming.  
Figure 26: Results obtained from comparing semantic segmentation in the respective experiments with Soybeans.  
Figure 27: Comparison results of IoU scores in each class: training dataset (**left**) and test dataset (**right**).  
**Author Contributions:** Conceptualization, S.Y.; methodology, H.M.; software, K.T.; validation, K.T. and T.K.S.; formal analysis, K.S. ([PERSON]); investigation, S.C.; resources, Y.N.; data curation, Y.N.; writing--original draft preparation, H.M.; writing--review and editing, K.S. ([PERSON]); visualization, S.N.; supervision, K.S. ([PERSON]); project administration, Y.N.; funding acquisition, S.Y. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research was funded by Japan Society for the Promotion of Science (JSPS) KAKENHI Grant Number 21H02321.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** Datasets described as a result of this study are available on request to the corresponding author.  
**Acknowledgments:** We would like to show our appreciation to the students at our lab for their great cooperation in the experiments.  
**Conflicts of Interest:** The authors declare that they have no conflict of interest. The funders had no role in the design of the study, in the collection, analyses, or in interpretation of data, in the writing of the manuscript, or in the decision to publish the results.