A systematic review of deep learning techniques for rice disease recognition: Current trends and future directions

Abstract

This systematic review paper provides a comprehensive analysis of the recent advances in deep learning techniques for rice disease recognition. Rice is one of the most important crops in the world, providing food for more than half of the global population. However, rice diseases pose a major threat to rice production and can cause significant yield losses. In recent years, deep learning techniques have shown great potential in automating the process of rice disease recognition, which can help in early disease detection and management. This paper reviews the current trends in deep learning techniques for rice disease recognition, including various pre-processing and augmentation techniques, as well as popular deep learning models such as convolutional neural networks (CNNs) and their variants. The paper also provides an in-depth analysis of the different datasets used in the studies, along with their limitations and challenges. Furthermore, the paper discusses the future directions for research in this field, such as the need for larger and more diverse datasets, the development of novel deep learning architectures, and the integration of other data sources such as weather data and satellite imagery. The paper concludes by summarizing the key findings of the systematic review and highlighting the potential impact of deep learning techniques in rice disease recognition. In addition, the review provides a useful resource for researchers and practitioners in the field of agricultural technology and can help in the development of more accurate and efficient automated systems for rice disease detection and management.

Keywords

• Rice disease
• Deep learning
• Transfer learning
• Convolutional neural networks
• Artificial intelligence
• Object recognition