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

Addressing plant diseases and pests is not just crucial; it’s a matter of utmost importance for enhancing crop production and preventing economic losses. Recent advancements in artiffcial intelligence, machine learning, and deep learning have revolutionised the precision and efffciency of this process, surpassing the limitations of manual identiffcation. TTis study comprehensively reviews modern computer-based techniques, including recent advances in artiffcial intelligence, for detecting diseases and pests through images. TTis paper uniquely categorises methodologies into hyperspectral imaging, non-visualisation techniques, visualisation approaches, modiffed deep learning architectures, and transformer models, helping researchers gain detailed, insightful understandings. TTe exhaustive survey of recent works and comparative studies in this domain guides researchers in selecting appropriate and advanced state-of-the-art methods for plant disease and pest detection. Additionally, this paper highlights the consistent superiority of modern AI-based approaches, which offen outperform older image analysis methods in terms of speed and accuracy. Further, this survey focuses on the efffciency of vision transformers against well-known deep learning architectures like MobileNetV3, which shows that Hierarchical Vision Transformer (HvT) can achieve accuracy upwards of 99.3% in plant disease detection. TTe study concludes by addressing the challenges of designing the systems, proposing potential solutions, and outlining directions for future research in this ffeld.