Deep Learning Based Models for Paddy Disease Identification and Classification: A Systematic Survey

作     者：Tasfe, Mahrin Nivrito, Akm Al Machot, Fadi Ullah, Mohib Ullah, Habib 

作者机构：Norwegian Univ Life Sci NMBU Fac Sci & Technol REALTEK N-1433 As Norway Norwegian Univ Sci & Technol NTNU Dept Comp Sci IDI Intelligent Syst & Analyt ISA Res Grp N-2815 Gjovik Norway 

出 版 物：《IEEE ACCESS》 (IEEE Access)

年 卷 期：2024年第12卷

页      面：100862-100891页

核心收录：

基　　金：Norwegian University of Science and Technology through the open-access journal publication fund 

主　　题：Plant diseases Lesions Deep learning Surveys Seeds (agriculture) Manuals Smart agriculture Farming Classification algorithms Smart farming precision agriculture paddy disease detection paddy disease classification paddy disease segmentation deep learning 

摘      要：Automated early detection and classification of paddy diseases help in applying treatment efficiently according to the detected diseases. Early detection also minimises the usage of chemical substances and pesticides and hinders the spread of the disease to healthy crops. On a broader scale, it aids in halting the global spread of diseases. Thus, it ultimately promotes healthier rice crops and increased yield. In this survey paper, we present a thorough exploration of deep learning (DL) models for the classification of paddy diseases. Our paper delves into the motivation behind this research study, reveals different paddy diseases and their associated symptoms, and unravels various deep-learning models employed for disease detection. We have also discussed strategies used by researchers for improving the performance of DL models, along with adaptations tailored for application-specific contexts. Additionally, we illustrate relevant research findings, explore datasets utilised in this domain, and analyse approaches for data augmentation. Through an exhaustive investigation, we emphasise existing research gaps, challenges, and open issues, concluding in a discussion on avenues for future exploration.