Image Analysis and Detection of Olive Leaf Diseases Using Recurrent Neural Networks
DOI:
https://doi.org/10.23851/mjs.v35i1.1416Keywords:
Recurrent Neural Network (RNN), Texture Based Image Retrieval (TBIR), Disease diagnosis, Image analysis, Deep learningAbstract
The widespread adoption of DL has led to a rise in academic interest in image recognition approaches, enabling applications such as automated image classification and the detection of plant diseases. The world's largest producer of olives is Morocco. Plant health might be harmed by illnesses, which therefore affects its development. Numerous illnesses affecting olive leaves specifically target crop growth rate. The objective of this research is to create deep RNNs to identify olive plant illnesses using a collection of leaf images, collected from various sources (Disease note The peacock eye falls on olive trees, Field Guide to Olive Pests, Diseases and Disorders in Australia. Thus, this technique is the best RNN model and is employed in further applications to enhance diagnostic measurements regarding olive leaves and other plant leaves.
Downloads
References
M. Sahu, et al., "Image Mining: A New Approach for Data Mining Based on Texture," Third International Conference on Computer and Communication Technology, Allahabad, India, pp. 7-9, 2012,
S. Gayathri, et al., "Image Analysis and Detection of Tea Leaf Disease using Deep Learning,"International Conference on Electronics and Sustainable Communication Systems (ICESC), Coimbatore, India, pp. 398-403,2020,
G. Savita, A. Parul,"Detection and classification of plant leaf diseases using image processing techniques: a review", Int. J Recent Adv. Eng. Technol;2(3):2347-8122014.
V. A. Gulhane, Dr. A. A. Gurjar, "Detection of Diseases on Cotton Leaves and Its Possible Diagnosis",International Journal of Image Processing (IJIP), Vol.5, Issue 5, pp.590-598,2014.
K. Majid et al., "I-Pedia: Mobile Application For Paddy Disease Identification Using Fuzzy Entropy And Probabilistic Neural Network",ICACSIS, 2013.
P. Graves et al.," Speech recognition with deep recurrent neural networks" IEEE International Conference on Acoustics, Speech and Signal Processing - 2013.
S. Siddique, "A Wavelet Based Technique for Analysis and Classification of Texture Images," Carleton University, Ottawa, Canada, Proj. Rep. 70.593, April 2002.
K. Smelyakov et al., "Search by Image. New Search Engine Service Model,, Conference: Problems of Infocommunications. Science andTechnology, 2018.
S. Gang et al., "A leaf recognition algorithm for plant using probabilistic neural network", in Proc. IEEE ISSPIT,2007.
D. Sanjay ,K. Nitin. "Agricultural plant leaf disease detection using image processing". Int J Adv Res Electr Electron Instrum Eng 2013.
H. Ajra et al. ,"Plant leaf disease detection using advanced image processing and Neural Network", International Journal of Recent Trends in Engineering and Research, 4(4),138-143,2014.
R. Narmadha & G. Arulvadivu, "Detection and measurement of paddy leaf disease symptoms using image processing", 2017 International Conference on Computer Communication and Informatics (ICCCI),2017.
J.Barbedo et al., "An automatic method to detect and measure leaf disease symptoms using digital image processing" Plant Disease, 98(12), pp. 1709-1716,2014.
Ü.Atila, et al.,"Plant Leaf disease classification using EfficientNet Deep Learning Model", Ecological Informatics, 61,p.101182,2021.
J.Lu, et al., "Review on Convolutional Neural Network (CNN) applied to plant leaf disease classification" ,Agriculture, 11(8), p.707,2021.
L. Sherly. et al.,"Machine Learning for Plant Leaf Disease Detection and Classification: A Review," International Conference on Communication and Signal Processing (ICCSP), Chennai, India, 2019.
S.Hart, "Sustainable pest and disease management in Australian olive production.2005.
Downloads
Key Dates
Received
Revised
Accepted
Published
Issue
Section
License
Copyright (c) 2024 Al-Mustansiriyah Journal of Science
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
Articles accepted for publication in Al-Mustansiriyah Journal of Science (MJS) are protected under the Creative Commons Attribution 4.0 International License (CC BY-NC). Authors of accepted articles are requested to sign a copyright release form prior to their article being published. All authors must agree to the submission, sign copyright release forms, and agree to be included in any correspondence between MJS and the authors before submitting a work to MJS. For personal or educational use, permission is given without charge to print or create digital copies of all or portions of a MJS article. However, copies must not be produced or distributed for monetary gain. It is necessary to respect the copyright of any parts of this work that are not owned by MJS.
How to Cite
