## 1 Introduction
Convolutional neural networks (CNNs) are widely applied in image classification, ranging from natural image classification to computer-aided diagnosis [15; 16], but the generalization of CNNs is limited when applied to validation or testing datasets because they require large training datasets. In practice, in silico generation has been successfully used in computer vision for data enhancement even when the number of biological samples is limited. The use of samples generated in silico has been combined with observations made from real samples to artificially increase the number of observations. In this study, we augmented real GC-MS data with newly generated samples, whose distribution mimics the original data distribution in their original metabolite space. Data modeling traditionally relies on deep-learning-based generative adversarial networks (GANs) for the generation of photorealistic images, rather than the priors underlying such data. GANs were introduced as deep learning tools by [PERSON] et al. in 2014 [17]. A GAN is a generative model that uses deep neural networks in an adversarial setting. Specifically, a GAN uses adversarial methods to learn generative models of the data distribution. This has become one of the hottest research areas in artificial intelligence, as one of the most successful generative models in recent years. Since its initial proposal, the GAN has attracted considerable attention owing to its exceptional performance. In addition to its excellent performance as a generative model, the GAN integrates deeply into all facets of deep learning, opening up an array of new research directions and applications [18,19]. Generative adversarial networks, as a technique for augmenting data scarcity, provide the ability to simulate existing images, so they are particularly promising for overcoming data scarcity [20,21]. By constructing an adversarial network, the GAN trains a generator and a discriminator. Conditional GANs (cGANs) are deep learning neural networks with additional parameters that depend on the project requirements. In addition, labels are used in the discriminator inputs so that the discriminator can correctly classify the input and cannot be easily filled in by the generator [22]. In this study, a novel artificial intelligence model was developed to classify Taif rose images based on their GC characteristics. Using this model, the oil and other latent characteristics of roses can be predicted without requiring laboratory testing. However, achieving this goal through traditional ANN models is difficult because of the relatively small dataset available for the Taif rose images and their GC analysis. Therefore, this study employs a state-of-the-art generative approach, namely, a conditional stacked GAN model (cSGAN). StackGANs are generative adversarial networks (GAN variants) that use hierarchical stacks of conditional GANs to generate images. As the name implies, StackGANs have two stacked GANs for creating a network that can generate high-resolution images. Using this approach, we evaluated 10 rose cultivars from different locations within the Taif region of Saudi Arabia. To the best of our knowledge, this is the first study to propose the use of a GAN to learn the chemical component patterns of Taif rose flowers directly from the raw GC-MS data. The key contributions of this study are as follows:  
1. Development of a novel GC-MS fingerprint based on the need to expand the genetic resources of oil-bearing roses for industrial cultivation in the Taif region (Saudi Arabia). 2. Providing agricultural researchers with a means of overcoming the problem of the shortage of observations caused by limited access to biosampling techniques, prohibitive costs, or ethical concerns. A more accurate data analysis may be possible by integrating samples generated in silico with real observations. 3. Evaluating cSGANs as a method for generating a variety of realistic classes by analyzing the GC-MS fingerprints of rose oils derived from populations of cultivated \(R\).