Evaluation of State-of-the-Art Deep Learning Techniques for Plant Disease and Pest Detection

TL;DR

This paper reviews recent AI and deep learning techniques for plant disease and pest detection, highlighting their superior accuracy and efficiency over traditional methods, and discusses future research directions.

Contribution

It provides a structured taxonomy of modern AI-based detection methods and compares their performance, emphasizing the effectiveness of vision transformers like HvT.

Findings

Vision transformers achieve over 99.3% accuracy in detection

AI methods outperform traditional image analysis in speed and accuracy

Survey highlights the superiority of modern deep learning architectures

Abstract

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.