article · Smart Agricultural Technology
Rice is a staple crop essential to global food security, yet yields are frequently damaged by pathogens such as fungi, bacteria, and viruses. Traditional diagnostic approaches are labour-intensive, slow, and reliant on scarce expert knowledge. Automated vision systems using deep learning offer scalable alternatives, but software integration alone does not guarantee high accuracy. Model efficacy drops significantly without comprehensive and diverse datasets. Utilising unmanned aerial vehicles for image collection improves detection outcomes, while applying transfer learning supports generalisability across varied disease states. Furthermore, pairing convolutional neural networks with attention mechanisms increases model efficiency. Deploying these systems in real-world agricultural environments continues to face challenges involving hardware limitations, domain adaptation, and limited geographic and metadata diversity in training data.
Rice supplies staple nourishment to the majority of the global population, making prompt disease control vital for food security. Standard inspection methods are too slow to manage sudden pathogen outbreaks effectively. Automated imaging tools can accelerate disease identification, but addressing hardware and dataset constraints is essential before these artificial intelligence tools can reliably protect crops in everyday farming environments.
The reviewed technologies could enable automated crop diagnostic tools and drone-based field scanning services for agricultural operators and extension services. However, the work points out significant hurdles regarding hardware constraints, domain adaptation, and training data diversity. Because real-world deployment challenges remain unresolved, commercial applications appear to be at an early to intermediate development stage rather than ready for immediate market adoption.
AI-generated from the published abstract. Always read the original work before citing.
• Performance of rice disease detection models declines without diverse and comprehensive datasets. • Integration of Deep learning technologies into rice disease detection alone does not ensure high accuracy. • Utilization of UAVs for data collection improves performance of rice disease detection models during the application of deep learning techniques. • Performance of a deep learning model after implementing transfer learning enhances generalizability across various rice disease conditions. • Combining advanced architectures like CNNs with attention mechanisms enhances the efficiency of rice disease detection models. 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.
This page summarises published work. The authoritative version sits with the publisher.
DOI: 10.1016/j.atech.2025.100976
Is something wrong with this record? Report it or request removal.
Discussion
Have you built on this work, tried to replicate it, or seen it applied in practice? Share what you know. Verified researchers and MARATTO™ domain experts can open a discussion, and any member can reply. Contributions are reviewed before they appear.
No discussion yet. Open the first thread.
New to MARATTO™? Create a free account.
