A comprehensive review of deep learning approaches for rice disease detection: Datasets, methodologies, and future directions
As a staple food for the majority of the global population, rice plays a vital role in food security. However, rice crop yield is heavily influenced by factors such as soil quality, weather, irrigation, and biological threats like pathogens (fungi, bacteria, viruses). Traditional methods for detecti...
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| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Elsevier
2025-08-01
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| Series: | Smart Agricultural Technology |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2772375525002096 |
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| Summary: | As a staple food for the majority of the global population, rice plays a vital role in food security. However, rice crop yield is heavily influenced by factors such as soil quality, weather, irrigation, and biological threats like pathogens (fungi, bacteria, viruses). Traditional methods for detecting rice diseases are often labor-intensive, time-consuming, and require expert knowledge, making them inefficient for large-scale or timely response. This process has prompted the adoption of automated techniques that integrate deep learning (DL) vision techniques to improve detection accuracy and efficiency. Deep learning models, particularly artificial neural networks, have shown promising results in detecting diseases from rice leaf images. This review aims to address three core research questions: What are the available open-source datasets for rice disease detection, and how do their characteristics affect model performance? What are the most commonly used deep learning architectures, and what are their advantages and limitations? What challenges exist in dataset generalization and model deployment for real-world applications? In answering these questions, this paper reviews current open-source datasets, highlighting their metadata and geographic coverage. It also compares popular deep learning architectures, discussing their respective strengths and shortcomings in rice disease detection. Furthermore, it explores the limitations of existing models in terms of real-world deployment, including issues related to data diversity, domain adaptation, and hardware constraints. Finally, the paper outlines future directions to improve the robustness and applicability of deep learning models in practical agricultural settings. |
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| ISSN: | 2772-3755 |