NDLI: Review of image processing approaches for detecting plant diseases

Content Provider	IET Digital Library
Author	Sinha, Aditya Shekhawat, Rajveer Singh
Abstract	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.
Starting Page	1427
Ending Page	1439
Page Count	13
ISSN	17519659
Volume Number	14
e-ISSN	17519667
Issue Number	Issue 8, Jun (2020)
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/iet-ipr/14/8
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/10.1049/iet-ipr.2018.6210
Journal	IET Image Processing
Publisher Date	2019-12-11
Access Restriction	Open
Rights Holder	© The Institution of Engineering and Technology
Subject Keyword	Agricultural Productivity Agriculture Classification Computer Vision And Image Processing Technique Crops Fisheries Computing Forestry Image Classification Image Processing Approach Image Recognition Plant Diseases
Content Type	Text
Resource Type	Article
Subject	Signal Processing Electrical and Electronic Engineering Computer Vision and Pattern Recognition Software

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