Corn (Zea mays) is a vital crop globally, serving as a staple food source, feed for livestock, and raw material for various industries. However, corn cultivation faces significant challenges, including the prevalence of pests and diseases that can severely impact crop yield and quality. Traditional methods of pest and disease detection and management are often labor-intensive, time-consuming, and may lack accuracy. This paper proposes a novel deep learning-based approach for the detection and classification of pests and diseases in corn plants, aimed at enhancing agricultural sustainability and productivity. By leveraging advances in deep learning technology, this research contributes to the development of efficient and scalable solutions for pest and disease management in corn cultivation. The proposed approach not only offers a more accurate and timely means of identifying and addressing pest and disease issues but also holds the potential to reduce reliance on chemical pesticides and promote environmentally friendly farming practices. Ultimately, the integration of deep learning methods into corn plant pest and disease management can enhance agricultural sustainability, improve crop resilience, and contribute to global food security.