Plant Disease Detection using AI & ML

Traditional diagnostic methods, while reliable, are constrained by subjectivity, time-intensive procedures, limited scalability, and the inability to detect presymptomatic infections. The emergence of artificial intelligence (AI) and machine learning (ML) technologies has revolutionized plant disease detection by enabling automated, objective, and real-time diagnostic capabilities. This comprehensive review examines state-of-the-art AI/ML approaches for plant disease detection, including deep learning architectures such as convolutional neural networks (CNNs), traditional machine learning algorithms like support vector machines (SVMs), and random forests, as well as ensemble methods and transfer learning techniques.