Evaluation of State-of-the-Art Deep Learning Techniques for Plant Disease and Pest Detection
It addresses the problem of crop production losses for farmers and researchers by providing a structured taxonomy of detection methods, though it is incremental as a review and comparative study.
This study reviews deep learning techniques for detecting plant diseases and pests from images, finding that modern AI-based methods, including vision transformers, achieve high accuracy, such as exceeding 99.3% with HvT, and outperform older approaches in speed and accuracy.
Addressing plant diseases and pests is critical for enhancing crop production and preventing economic losses. Recent advances in artificial intelligence (AI), machine learning (ML), and deep learning (DL) have significantly improved the precision and efficiency of detection methods, surpassing the limitations of manual identification. This study reviews modern computer-based techniques for detecting plant diseases and pests from images, including recent AI developments. The methodologies are organized into five categories: hyperspectral imaging, non-visualization techniques, visualization approaches, modified deep learning architectures, and transformer models. This structured taxonomy provides researchers with detailed, actionable insights for selecting advanced state-of-the-art detection methods. A comprehensive survey of recent work and comparative studies demonstrates the consistent superiority of modern AI-based approaches, which often outperform older image analysis methods in speed and accuracy. In particular, vision transformers such as the Hierarchical Vision Transformer (HvT) have shown accuracy exceeding 99.3% in plant disease detection, outperforming architectures like MobileNetV3. The study concludes by discussing system design challenges, proposing solutions, and outlining promising directions for future research.