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Review of image processing approaches for detecting plant diseases
IET Image Processing ( IF 2.0 ) Pub Date : 2020-06-01 , DOI: 10.1049/iet-ipr.2018.6210
Aditya Sinha 1 , Rajveer Singh Shekhawat 1
Affiliation  

There is intense pressure on agricultural productivity due to the ever-growing population. Several diseases affect crop yield and thus, effective control of these can significantly improve the production of food for all. In this regard, detection of diseases at an early stage and quantification of the severity, in general, has acquired urgent attention of the researchers. In this study, a summary of prevalent techniques and methodologies used for the detection, quantification and classification of diseases is presented to understand the scope of improvement. The study pays attention to critical gaps that exist in available approaches and enhance them for the early prediction of diseases. Diseases affect almost all parts of plants, e.g. root, stem, flower, leaf; a manifestation in different ways for different parts of the plant of the same disease presents a challenge for researchers. This study extends the review work published by JGA Barbedo in 2013, as there have been significant advances and numerous new techniques introduced since then. A novel approach of classifying and categorisation of the existing techniques based on pathogen types is a significant contribution by the authors in this study.

中文翻译:

审查植物病害的图像处理方法综述

由于人口的不断增长,农业生产力面临巨大压力。几种疾病会影响作物的产量,因此,有效控制这些疾病可以显着提高所有人的粮食产量。在这方面,一般而言,疾病的早期发现和严重程度的量化已引起研究人员的紧急关注。在这项研究中,总结了用于检测,量化和分类疾病的流行技术和方法,以了解改善的范围。该研究关注可用方法中存在的关键缺口,并在疾病的早期预测中增强这些缺口。疾病影响植物的几乎所有部分,例如根,茎,花,叶;对于同一病害的植物不同部位以不同方式表现出来,对研究人员提出了挑战。这项研究扩展了JGA Barbedo在2013年发表的审查工作,因为此后取得了重大进展并引入了许多新技术。作者基于病原体类型对现有技术进行分类的新方法是这项研究的重要贡献。
更新日期:2020-06-01
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