Major productivity and financial losses, as well as decreases in both the quality and quantity of food products, are caused by plant disease (PD). PD identification is now receiving increased attention in the field of agricultural monitoring. Changing from one method of disease prevention to another is a major challenge for farmers. Traditional methods for identifying PD have relied on the naked eye inspection by professionals. In this work, we discuss how an automated approach for identifying PD would contribute to improvements in agriculture. Diseases can be managed more effectively with the use of early detection. Much research has been conducted to evaluate the potential of Artificial Intelligence (AI) approaches for precision agriculture. Despite the variety of applications, a few gaps in PD research must still be filled. Therefore, it is necessary to create a database of already-existing applications and to determine the obstacles and possibilities to move forward with the development of tools that meet farmers' demands. This survey provides a thorough analysis of the various studies conducted on the use of AI for PD identification. This paper walked over the fundamentals of PD analysis, including the structure of PD, freely accessible datasets, processing methods, segmentation techniques, feature extraction approaches, and categorization models. Finally, the difficulties of employing AI for PD detection are also presented. The primary goal of this literature review is to help new researchers learn more about automated PD categorization