Open Access

REVIEW

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

MD Tausif Mallick¹, Saptarshi Banerjee², Nityananda Thakur³, Himadri Nath Saha^4,*, Amlan Chakrabarti¹

1 Department of AKCSIT, University of Calcutta, Kolkata, 700106, India
2 Department of Computer Science, Illinios Institute of Technology, Chicago, IL 60616, USA
3 Department of Mathematics, SNEC, University of Calcutta, Kolkata, 700009, India
4 Department of Computer Science, SNEC, University of Calcutta, Kolkata, 700009, India

* Corresponding Author: Himadri Nath Saha. Email:

Computers, Materials & Continua 2025, 85(1), 121-180. https://doi.org/10.32604/cmc.2025.065250

Received 07 March 2025; Accepted 24 June 2025; Issue published 29 August 2025

Abstract

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 artificial intelligence, machine learning, and deep learning have revolutionised the precision and efficiency of this process, surpassing the limitations of manual identification. This study comprehensively reviews modern computer-based techniques, including recent advances in artificial intelligence, for detecting diseases and pests through images. This paper uniquely categorises methodologies into hyperspectral imaging, non-visualisation techniques, visualisation approaches, modified deep learning architectures, and transformer models, helping researchers gain detailed, insightful understandings. The 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 often outperform older image analysis methods in terms of speed and accuracy. Further, this survey focuses on the efficiency 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. The study concludes by addressing the challenges of designing the systems, proposing potential solutions, and outlining directions for future research in this field.

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Keywords

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