## 6 Discussion  
Synthetic data generation poses the challenge of capturing the complex patterns of real data. To determine the accuracy and quality of the synthetic data, a rigorous evaluation method must be used, followed by a direct comparison of the synthetic data produced by various generative models. It is noteworthy, however, that different evaluation strategies and metrics result in different results for generative models with high heterogeneity and complexity [51], particularly for high-dimensional data. In addition, the results of evaluation may differ based on the application domain and may not be simply transferable to other applications. In order to address this current target problem, a specific evaluation framework must be developed. Our study is primarily concerned with agricultural applications, for which no real data exist. Therefore, we introduced an extended dataset including both generated and real data to train the classifier. As a result, we can see how synthesized data can improve the accuracy of the classifier, while at the same time retaining the features of real data. Comparatively, other evaluation methods require the classifier to be trained exclusively on real data and to be tested on synthetic data [52; 53]. On the other hand, other researchers used synthetic data to train their classifiers and then tested them on real data [54]. It should be noted that, in the present study, the same real data were employed to train the generative models, and partially in an expanded dataset to train the classifier. However, this allows us to address one of the biggest challenges in agriculture--namely, data scarcity, where data are limited. Synthetic data are important because they can overcome the data limitations arising from a variety of factors that lead to complications in the field. However, it should be noted that these data are typically of lower quality than real data, as confirmed by supervised classification accuracy [55]. Based on the available (limited) real data, the amount of synthetic data required must be determined in order to ensure high accuracy. This study proposes a generative model called the conditional stacked generative adversarial network (cSGAN). It comprises a top-down stack of GANs that generate a set of \"plausible\" lower-level representations based on higher-level representations. Similarly, we introduced a set of representation discriminators, which are trained to distinguish \"fake\" representations from \"real\" representations, similar to the image discriminator in the original GAN model. The loss created by the representation discriminator drives the intermediate representations of the SGAN to lie on the manifold of the representation space of the bottom-up DNN. In this regard, the proposed GAN variant (i.e., StackGAN) could improve classification accuracy compared to a vanilla GAN trained only on real data [53]. Generative models encounter problems when a limited real data are considered, resulting in the generation of inferior data. This is particularly problematic in agriculture,  
Figure 6: Graph of loss function changes versus number of epochs for the generator and discriminator during training. where only limited amounts of data are available. Consequently, generating and testing data quality is difficult, as opposed to studies that use large datasets [53]. It is often used for clustering, where data points are grouped based on their similarity, which allows the detection of unknown correlations and patterns in the datasets. Clustering is a common application of unsupervised learning (UL), as it involves grouping data points according to their similarity, allowing the discovery of unknown correlations and patterns in the data. Another important task of UL is dimensionality reduction, which aims to find efficient low-dimensional representations of data while minimizing the resulting loss of information. This is closely related to finding a cluster of data with similar features.