Plant disease classification plays a crucial role in agricultural productivity by enabling timely detection and management of diseases that affect crop yields. Traditional approaches of disease prediction frequently depend on manual observation, which can be subjective and time-consuming. Tomato (Solanum lycopersicum) is one of the utmost thriftily significant crops globally, but its cultivation faces substantial threats from various diseases that can severely impact yield and quality. Effective disease management is crucial for sustainable tomato production, driving the need for advanced technologies in disease detection and classification. Automated systems using machine learning (ML) and, more recently, quantum-inspired algorithms have emerged as promising approaches to address these challenges.