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

作者：Pardeep Seelwal, Poonam Dhiman, Yonis Gulzar, Amandeep Kaur, Shivani Wadhwa, Choo Wou Onn · 发表于：Frontiers in Computer Science · 年份：2024 · DOI：10.3389/fcomp.2024.1452961 · 被引用次数：42 · 研究领域：Smart Agriculture and AI、Spectroscopy and Chemometric Analyses、GABA and Rice Research

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 effi...