Agriculture is the cornerstone of global food production and economic stability, making the detection and management of crop diseases a critical concern for farmers worldwide. Traditional methods of disease identification, relying on manual observation and expert knowledge, are time-consuming, prone to misdiagnosis, and dependent on human expertise. With the rapid growth of the global population and increasing food demands, the need for efficient, accurate, and scalable solutions to crop disease detection has never been more urgent.