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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| Format: | Article |
| Language: | English |
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Elsevier
2025-08-01
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| Series: | Smart Agricultural Technology |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2772375525002096 |
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| author | Usman Idris Ismail Hui Na Chua Rosdiadee Nordin Muhammed Kabir Ahmed |
| author_facet | Usman Idris Ismail Hui Na Chua Rosdiadee Nordin Muhammed Kabir Ahmed |
| author_sort | Usman Idris Ismail |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-08bfdc751a5a4f8bb488f01cec4cd632 |
| institution | Kabale University |
| issn | 2772-3755 |
| language | English |
| publishDate | 2025-08-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Smart Agricultural Technology |
| spelling | doaj-art-08bfdc751a5a4f8bb488f01cec4cd6322025-08-20T03:49:41ZengElsevierSmart Agricultural Technology2772-37552025-08-011110097610.1016/j.atech.2025.100976A comprehensive review of deep learning approaches for rice disease detection: Datasets, methodologies, and future directionsUsman Idris Ismail0Hui Na Chua1Rosdiadee Nordin2Muhammed Kabir Ahmed3Department of Smart Computing and Cyber Resilience, Faculty of Engineering and Technology, Sunway University, 47500 Selangor Darul Ehsan, Malaysia; Federal University Kashere Gombe, P.M.B 0182 Gombe State, NigeriaDepartment of Smart Computing and Cyber Resilience, Faculty of Engineering and Technology, Sunway University, 47500 Selangor Darul Ehsan, Malaysia; Corresponding author.Department of Smart Computing and Cyber Resilience, Faculty of Engineering and Technology, Sunway University, 47500 Selangor Darul Ehsan, MalaysiaDepartment of Computer Science, Gombe State University. P.M.B 0127 Gombe State Nigeria, NigeriaAs 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.http://www.sciencedirect.com/science/article/pii/S2772375525002096Rice Disease DetectionDeep LearningComputer Vision in AgricultureData AugmentationConvolutional Neural Networks (CNNs) |
| spellingShingle | Usman Idris Ismail Hui Na Chua Rosdiadee Nordin Muhammed Kabir Ahmed A comprehensive review of deep learning approaches for rice disease detection: Datasets, methodologies, and future directions Smart Agricultural Technology Rice Disease Detection Deep Learning Computer Vision in Agriculture Data Augmentation Convolutional Neural Networks (CNNs) |
| title | A comprehensive review of deep learning approaches for rice disease detection: Datasets, methodologies, and future directions |
| title_full | A comprehensive review of deep learning approaches for rice disease detection: Datasets, methodologies, and future directions |
| title_fullStr | A comprehensive review of deep learning approaches for rice disease detection: Datasets, methodologies, and future directions |
| title_full_unstemmed | A comprehensive review of deep learning approaches for rice disease detection: Datasets, methodologies, and future directions |
| title_short | A comprehensive review of deep learning approaches for rice disease detection: Datasets, methodologies, and future directions |
| title_sort | comprehensive review of deep learning approaches for rice disease detection datasets methodologies and future directions |
| topic | Rice Disease Detection Deep Learning Computer Vision in Agriculture Data Augmentation Convolutional Neural Networks (CNNs) |
| url | http://www.sciencedirect.com/science/article/pii/S2772375525002096 |
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