A systematic review of deep learning applications for rice disease diagnosis: current trends and future directions

Background: The occurrence of diseases in rice leaves presents a substantial challenge to farmers on a global scale, hence jeopardizing the food security of an expanding global population. The timely identification and prevention of these diseases are of utmost importance in order to mitigate their impact. Methods: The present study conducts a comprehensive evaluation of contemporary literature pertaining to the identification of rice diseases, covering the period from 2008 to 2023. The process of selecting pertinent studies followed the guidelines outlined by Kitchenham, which ultimately led to the inclusion of 69 studies for the purpose of review. It is worth mentioning that a significant portion of research endeavours have been directed towards studying diseases such as rice brown spot, rice blast, and rice bacterial blight. The primary performance parameter that emerged in the study was accuracy. Researchers strongly advocated for the combination of hybrid deep learning and machine learning methodologies in order to improve the rates of recognition for rice leaf diseases. Results: The study presents a comprehensive collection of scholarly investigations focused on the detection and characterization of diseases affecting rice leaves, with specific emphasis on rice brown spot, rice blast, and rice bacterial blight. The prominence of accuracy as a primary performance measure highlights the importance of precision in the detection and diagnosis of diseases. Furthermore, the efficacy of employing hybrid methodologies that combine deep learning and machine learning techniques is exemplified in enhancing the recognition capacities pertaining to diseases affecting rice leaves. Conclusion: This systematic review provides insight into the significant research endeavours conducted by scholars in the field of rice disease detection during the previous decade. The text underscores the significance of precision in evaluation and calls for the implementation of hybrid deep learning and machine learning methodologies to augment disease identification, presenting possible resolutions to the obstacles presented by these agricultural hazards.