Role of Image Processing and Machine Learning Techniques in Disease Recognition, Diagnosis and Yield Prediction of Crops: A Review
Abstract
food security of agro-based country like India. In this Review we
present a comprehensive and critical survey on current
challenges and methodologies applied for various image
processing and Machine learning approaches on variety of crops
in their productivity increase, considering the following
measures: Early detection/recognition of crop diseases,
diagnosing methods and crop selection method in yield
prediction. This paper presents an overview of existing reported
techniques useful in detection of diseases in variety of crops.
Finally we identify the challenges and some opportunities for
future developments in this area.
Keywords
Full Text:
PDFReferences
P. Schmitter, J steinrucken, C. Romer, A. Ballvora, J.
Leon, U.Rascher, L. Plumer. Unsupervised domain
adaptation for early detection of drought stress in
hyperspectral images.ISPRS 0924-2716. 2017.
HemantKumar Wani, Nilima Ashtankar. An
Appropriate Model Predicting Pest/Diseases of Crops
Using Machine Learning Algorithms. ICACCS-2017.
Jan 06-07.
Vijai singh, A.K. Mishra. Detection of Plant leaf
diseases using image segmentation and soft
computing techniques. Information processing in
agriculture 4 (2017) 41-49, Elsevier.
Mukesh Kumar Tripathi, Dr. Dhananjay D.
Maktedar. Recent Machine Learning Based
Approaches for Disease Detection and Classification
of Agricultural Products. IEEE Xplore. Feb 23-2017.
Megha. S, Nivedita CR, SowmyaShree N, Vidhya K.
Image Processing system for plant disease
Identification by using FCM- clustering
technique.IJARIIT, ISSN: 2454-132X, vol3, issue2,
Dibio L. Borges, Samuel T. C, Abadia R, Pedro
Melo-pinto. Detecting and grading severity of
bacterial spot caused by Xanthomonas spp. in tomato
(Solanum lycopersicon) fields using visible spectrum
images. Elsevier, Computers and Electronics in
Agriculture 125(2016) 149-159.
Esmael Hamuda, Martin Glavin, Edward Jones. A
survey of image processing techniques for plant
extraction and segmentation in the field. Elsevier,
Computers and Electronics in Agriculture 125(2016)
-199.
Shanwen Zhang, Zhen Wang. Cucumber disease
recognition based on Global-Local Singular value
decomposition. Neurocomputing 205(2016), 341-
, Elsevier.
Jayme Garcia, Luciano Vieira, Thiago Teixeira
Santos. Identifying multiple plant diseases using
digital image processing. Biosystems Engineering
(2016) 104-116, Elsevier.
Godliver Owomugisha, Ernest Mwebaze. Machine
learning for plant disease incidence and severity
measurements from leaf images. ICMLA, 2016
IEEE.
Ancient K. Kouakou, Olivier K Bagui, Therese
Atcham Agneroh, Adama P Soro, Jeremie T Zoueu.
Cucumber mosaic virus detection by artificial neural
network using multi spectral and multi modal
imagery. Optik 127(2016) 11250-11257, Elsevier.
Anurag Panwar, Mariram Al-Lami, Pratool Bharti,
Sriram Chellappan, Joel Burken. Determining the
effectiveness of soil treatment on Plant stress using
smartphone cameras. MOWNET, IEEE conference,
Rakesh Kumar, M P Singh, Prabhat kumar and J P
Singh. Crop selection method to maximize crop yield
rate using Machine Learning technique. ICSTM, PP
-145, IEEE conference, May 2015.
Gouri C. Khadabadi, Vijay S Rajpurohit, Arun
kumar, V B Nagund. Disease detection in vegetables
using Image processing techniques: A Review,
IJETCSE ISSN: 0976-1343, Volume 4 Issue 2- April
Jayme Garcia Arnal Barbedo. Digital image
processing techniques for detecting, quantifying and
classifying plant diseases. Barbedo Springer Plus
:660, 2013.
Arun A Shanker. Central Research Institute for
dryland agriculture, India. An overview of biotic and
Abiotic factors that causes crop stress.
DOI: https://doi.org/10.26483/ijarcs.v9i2.5793
Refbacks
- There are currently no refbacks.
Copyright (c) 2018 International Journal of Advanced Research in Computer Science

