Agriculture is one of the fundamental pillars of human civilization. In addition to providing food, it boosts the economy. Crops and plant leaves are susceptible to several diseases during agricultural production. Diseases prevent each species from growing. Early and accurate plant leaves disease diagnosis helps to minimize major damages to plants. Plant leaves disease classification and detection has grown to be major issues. Failure to promptly identify and categorize plant diseases could lead to agricultural plant loss and a sharp decrease in product. Utilizing digital image processing techniques in their fields can help farmers enhance output and decrease losses. Various techniques have been developed and implemented to identify and classify plant diseases. Over the years, considerable advancements have been made in finding different disease by exploring and applying different methodologies. However, because of new developments, and conversations, improvements are needed. Globally, crop production can be greatly increased with the application of technology. Conventional techniques, such as laboratory-based diagnostics and manual inspection, are still dependable but time-consuming and labor-intensive. Emerging technologies, such as Machine learning (ML) and deep learning (DL) techniques have revolutionized automated disease detection, offering robust solutions for analyzing complex patterns in plant images. This survey highlights recent advancements in these areas.