Plant diseases pose enormous challenges to world agriculture, resulting in significant crop losses and financial crises. Traditional diagnostic methods are labor intensive and strongly reliant on expert knowledge, making them unsuitable for large-scale use. This work investigates the classification of plant diseases using artificial intelligence (AI) approaches such as machine learning (ML), deep learning (DL), and transfer learning. The study compares approaches based on accuracy, training duration, and computing economy. Special emphasis is given to transfer learning models for their ability to generalize well across diverse datasets while mitigating common challenges like data imbalance, overfitting, and limited annotations. The results indicate that hybrid and deep transfer learning approaches outperform traditional methods in both performance and efficiency. Furthermore, the study outlines critical limitations in current approaches, such as a lack of explainability and deployment constraints on edge devices. The paper concludes with a discussion on future research directions aimed at developing scalable, interpretable, and resource-efficient plant disease detection systems, ultimately contributing to smarter and more resilient agricultural practices.