Open Access
| Issue |
ITM Web Conf.
Volume 44, 2022
International Conference on Automation, Computing and Communication 2022 (ICACC-2022)
|
|
|---|---|---|
| Article Number | 03002 | |
| Number of page(s) | 6 | |
| Section | Computing | |
| DOI | https://doi.org/10.1051/itmconf/20224403002 | |
| Published online | 05 May 2022 | |
- E. David et al. (2020).: Global Wheat Head Detection (GWHD) dataset: a large and diverse dataset of high resolution RGB labeled images to develop and benchmark wheat head detection methods. https://arxiv.org/abs/2005.02162. [Google Scholar]
- S.P. Mohanty, D.P. Hughes and M. Salathe Using Deep Learning for Image -Based Plant Disease Detection. Front.Plant Sci. 7:1419. DOI: 10.3389/fpls.2016.01419 (2016) [CrossRef] [Google Scholar]
- Kothawale, Shweta S., S. R. Barbade, and P. P. Mirajkar. “Grape leaf disease detection using SVM classifier.” International Journal of Innovative Research in Computer and Communication Engineering 6.4 (2018). [Google Scholar]
- S. Pavithra, A. Priyadharshini, V. Praveena and T. Monika Electronics and Communication Engineering, VSB Engineering College, Karur. “PADDY LEAF DISEASE DETECTION USING SVM CLASSIFIER.” International Journal of Communication and Computer Technologies 3.1 (2015), 16–20. [Google Scholar]
- Islam, Monzurul, et al. “Detection of potato diseases using image segmentation and multiclass support vector machine.” 2017 IEEE 30th Canadian conference on electrical and computer engineering (CCECE). IEEE, (2017). [Google Scholar]
- Ramesh, Shima, et al. “Plant disease detection using machine learning.” 2018 International conference on design innovations for 3Cs compute communicate control (ICDI3C). IEEE, (2018). [Google Scholar]
- Mohan, K. Jagan, M. Balasubramanian, and S. Palanivel. “Detection and recognition of diseases from paddy plant leaf images.” International Journal of Computer Applications 144.12 (2016). [Google Scholar]
- Orillo, J. William, et al. “Identification of diseases in rice plant (Oryza sativa) using backpropagation Artificial Neural Network.” 2014 International Conference on Humanoid, Nanotechnology, Information Technology, Communication, and Control, Environment and Management (HNICEM). IEEE, (2014). [Google Scholar]
- Kusumo, B. Suryo, et al. “Machine learning-based for automatic detection of corn-plant diseases using image processing.” 2018 International Conference on Computer, Control, Informatics and its Applications (IC3INA). IEEE, (2018). [Google Scholar]
- Sabrol, H., and S. Kumar. “Intensity-based feature extraction for tomato plant disease recognition by classification using the decision tree.” International Journal of Computer Science and Information Security 14.9 (2016): 622. [Google Scholar]
- S.E. Raza, G. Princ, J.P. Clarkson, N.M. Rajpoot Automatic detection of diseased tomato plants using thermal and stereo visible light images. PLoS ONE 10(4):1–20 (2015) [Google Scholar]
- M.M. Hasan et al.: Detection and analysis of wheat spikes using Convolutional Neural Networks. Plant Methods. 14 DOI: 10.1186/s13007-018-0366-8. (2018). [Google Scholar]
- Wu Wei et al.: Detection and enumeration of wheat grains based on a deep learning method under various scenarios and scales. Journal of Integrative Agriculture 19: 1998–2008. DOI: 10.1016/s2095-3119(19)62803-0, ISSN 2095-3119 (2020). [CrossRef] [Google Scholar]
- Ngowda, S.N., Yuan, C. (2019).: ColorNet: Investigating the Importance of Color Spaces for Image Classification. Computer Vision - ACCV 2018 Lecture Notes in Computer Science. 581–596 (2019). [Google Scholar]
Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.
Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.
Initial download of the metrics may take a while.