KRISHI
ICAR RESEARCH DATA REPOSITORY FOR KNOWLEDGE MANAGEMENT
(An Institutional Publication and Data Inventory Repository)
"Not Available": Please do not remove the default option "Not Available" for the fields where metadata information is not available
"1001-01-01": Date not available or not applicable for filling metadata infromation
"1001-01-01": Date not available or not applicable for filling metadata infromation
Please use this identifier to cite or link to this item:
http://krishi.icar.gov.in/jspui/handle/123456789/68625| Title: | Wheat rust disease identification using deep learning |
| Other Titles: | Not Available |
| Authors: | Sapna Nigam Rajni Jain Sudeep Marwaha Alka Arora |
| ICAR Data Use Licennce: | http://krishi.icar.gov.in/PDF/ICAR_Data_Use_Licence.pdf |
| Author's Affiliated institute: | ICAR::Indian Agricultural Statistics Research Institute ICAR::National Institute of Agricultural Economics and Policy Research |
| Published/ Complete Date: | 2021-02-08 |
| Project Code: | Not Available |
| Keywords: | plant disease wheat rust CNN deep learning artificial intelligence |
| Publisher: | De Gruyter |
| Citation: | Nigam, S., Jain, R., Marwaha, S., & Arora, A. (2021). 12 Wheat rust disease identification using deep learning. In Internet of Things and Machine Learning in Agriculture (pp. 239-250). De Gruyter. |
| Series/Report no.: | Not Available; |
| Abstract/Description: | Automated image-based tools are required when a human assessment of plant disease identification is expensive, inappropriate, or unreliable. Thus, there is a need to recognize cost-effective automated computational systems and image-based tools for disease detection that would facilitate advancements in agriculture. Deep learning (DL) is a deep neural network that uses multiple levels of abstraction for the hierarchical representation of the data. The convolutional neural network model is used, in this chapter, on 2,000 images to identify the wheat rust disease in an unseen leaf image. The results show that DL has the potential to identify plant diseases with much higher accuracy. |
| Description: | Not Available |
| ISBN: | 9783110691276 9783110691221 |
| Type(s) of content: | Book chapter |
| Sponsors: | Not Available |
| Language: | English |
| Name of Journal: | Not Available |
| Volume No.: | Not Available |
| Page Number: | 239-250 |
| Name of the Division/Regional Station: | Not Available |
| Source, DOI or any other URL: | https://doi.org/10.1515/9783110691276-012 |
| URI: | http://krishi.icar.gov.in/jspui/handle/123456789/68625 |
| Appears in Collections: | AEdu-IASRI-Publication |
Files in This Item:
| File | Description | Size | Format | |
|---|---|---|---|---|
| Wheat Rust_De grutyer.pdf | 300.94 kB | Adobe PDF | View/Open |
Items in KRISHI are protected by copyright, with all rights reserved, unless otherwise indicated.