Mayuri Sharma , Chandan Jyoti Kumar , Dhruba K. Bhattacharyya
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By presenting both research findings and existing gaps, this systematic literature review (SLR) offers valuable insights to direct future research endeavours in this domain. Our investigation involves a comprehensive review of articles sourced from Scopus, IEEE Xplore, Science Direct and Google Scholar resulting in a dataset of 91 unique articles spanning from the year 2013–2023. Following rigorous selection criteria, these 91 articles have been considered for in-depth analysis. Through an extensive examination of this corpus, our study seeks to provide answers to seven key questions pertaining to the past, present, and future directions of research of ML/DL application in rice crop health monitoring and disease/disorder diagnosis. The review adheres to the agricultural science-based PRISMA systematic review methodology and incorporates statistical analysis to explore relationships among variables such as dataset sample size, experimental accuracy, and classification models employed in various studies.</p></div>","PeriodicalId":9173,"journal":{"name":"Biosystems Engineering","volume":"244 ","pages":"Pages 77-92"},"PeriodicalIF":4.4000,"publicationDate":"2024-06-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Machine/deep learning techniques for disease and nutrient deficiency disorder diagnosis in rice crops: A systematic review\",\"authors\":\"Mayuri Sharma , Chandan Jyoti Kumar , Dhruba K. Bhattacharyya\",\"doi\":\"10.1016/j.biosystemseng.2024.05.014\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><p>Disease and nutrient deficiency disorders significantly impact the productivity of rice crops. 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引用次数: 0
摘要
病害和养分缺乏症严重影响水稻作物的产量。及时发现这些情况对于有效减轻潜在的作物损害至关重要。为应对这一挑战,利用机器学习(ML)/深度学习(DL)等尖端技术,在水稻作物监测和维护领域开展了大量研究。本研究旨在探讨研究领域的关键问题,包括水稻作物健康方面的出版趋势、数据模式、ML/DL 模型、预处理方法、分割技术和特征选择方法。本系统性文献综述(SLR)通过介绍研究成果和现有差距,为指导该领域未来的研究工作提供了宝贵的见解。我们的调查包括对 Scopus、IEEE Xplore、Science Direct 和 Google Scholar 上的文章进行全面审查,最终获得了一个包含 91 篇文章的数据集,时间跨度为 2013-2023 年。按照严格的筛选标准,我们对这 91 篇文章进行了深入分析。通过对该语料库的广泛研究,我们的研究试图回答七个关键问题,这些问题涉及 ML/DL 在水稻作物健康监测和疾病/病害诊断中应用的过去、现在和未来研究方向。本综述遵循以农业科学为基础的 PRISMA 系统综述方法,并结合统计分析来探讨各种研究中采用的数据集样本大小、实验准确性和分类模型等变量之间的关系。
Machine/deep learning techniques for disease and nutrient deficiency disorder diagnosis in rice crops: A systematic review
Disease and nutrient deficiency disorders significantly impact the productivity of rice crops. Timely identification of these conditions is essential for effective mitigation of potential crop damage. To address this challenge, considerable research is happening in the field of rice crop monitoring and maintenance, using cutting-edge techniques like Machine learning (ML)/Deep learning (DL). This study aims to address critical aspects of the research landscape, including publication trends, data modalities, ML/DL models, pre-processing methods, segmentation techniques, and feature selection approaches in the context of rice crop's health. By presenting both research findings and existing gaps, this systematic literature review (SLR) offers valuable insights to direct future research endeavours in this domain. Our investigation involves a comprehensive review of articles sourced from Scopus, IEEE Xplore, Science Direct and Google Scholar resulting in a dataset of 91 unique articles spanning from the year 2013–2023. Following rigorous selection criteria, these 91 articles have been considered for in-depth analysis. Through an extensive examination of this corpus, our study seeks to provide answers to seven key questions pertaining to the past, present, and future directions of research of ML/DL application in rice crop health monitoring and disease/disorder diagnosis. The review adheres to the agricultural science-based PRISMA systematic review methodology and incorporates statistical analysis to explore relationships among variables such as dataset sample size, experimental accuracy, and classification models employed in various studies.
期刊介绍:
Biosystems Engineering publishes research in engineering and the physical sciences that represent advances in understanding or modelling of the performance of biological systems for sustainable developments in land use and the environment, agriculture and amenity, bioproduction processes and the food chain. The subject matter of the journal reflects the wide range and interdisciplinary nature of research in engineering for biological systems.