Early identification of crop diseases is critical to ensure food security by reducing yield losses and promoting sustainable agricultural practices. Advanced image processing and artificial intelligence methods are employed for analysing visual symptoms and detecting anomalies indicative of disease onset. Digital twin environment supports informed decision-making and timely responses by simulating disease progression and assessing various intervention strategies. This chapter discusses the methodologies used in plant health monitoring. It also elaborates the concept of digital twins, the technologies that enable them, and their key components. This chapter presents a smart framework that utilizes digital twin technology to detect preliminary stage of plant diseases. The proposed system creates a real-time virtual replica of the agricultural environment by integrating data streams originating from sensors, drones, and interconnected IoT systems.