Seed quality testing represents a crucial step in agricultural practices aimed at ensuring the cultivation of top-tier crops. Traditional methods for assessing seed quality are characterized by time-consuming and labor-intensive procedures that are susceptible to human errors. This study introduces an innovative approach designed to evaluate the quality and characteristics of six distinct types of seeds, including cucumber, pea, soybean, tomato, watermelon, and maize. The proposed methodology entails the classification of seeds into categories such as excellent, good, average, bad, or worst quality, while also determining specific properties of the seeds based on provided input seed images. Leveraging deep learning algorithms like Convolutional Neural Networks (CNN) and Generative Adversarial Networks (GAN), this methodology effectively examines seed quality using input seed images. Notably, the CNN model demonstrates remarkable accuracy rates in seed quality classification, surpassing conventional seed testing methods. Additionally, the integration of the GAN model with the CNN model enables the generation of synthetic seed images for data augmentation during training. Through this augmentation, the GAN model adeptly produces realistic synthetic seed images, thereby enhancing the size and diversity of the training dataset.