A Systematic Review of AI-Driven Wearable Plant Disease Recognition Systems

This review provides a comprehensive examination of recent methods that employ artificial intelligence (AI) and computer vision techniques for autonomous plant disease recognition. It evaluates advancements from the past five years, focusing on innovations in sensor integration, AI techniques, and image analysis methods. This review also compares state-of-the-art approaches by analyzing accuracy, efficiency, and trade-offs in terms of scalability. Furthermore, an analysis of convolutional neural networks (CNNs) and transformers is included, highlighting their superior performance compared to traditional machine learning methods, particularly in real-time disease screening. Finally, the review emphasizes the need to develop lightweight AI models specifically designed for edge-computing devices, aiming to strike a balance between high recognition performance and minimal computational demands.