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Please use this identifier to cite or link to this item:
http://krishi.icar.gov.in/jspui/handle/123456789/53521
Full metadata record
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Tanuj Misra | en_US |
dc.contributor.author | Sudeep Marwaha | en_US |
dc.contributor.author | Alka Arora | en_US |
dc.contributor.author | Mrinmoy Ray | en_US |
dc.contributor.author | Shailendra Kumar | en_US |
dc.contributor.author | Sudhir Kumar | en_US |
dc.contributor.author | Viswanathan | en_US |
dc.date.accessioned | 2021-08-07T05:48:48Z | - |
dc.date.available | 2021-08-07T05:48:48Z | - |
dc.date.issued | 2020-11-12 | - |
dc.identifier.citation | Tanuj Misra, Sudeep Marwaha, Alka Arora, Mrinmoy Ray, Shailendra Kumar, Sudhir Kumar and Viswanathan (2021). Leaf area assessment using image processing and support vector regression in rice, Indian Journal of Agricultural Sciences, 91 (3), 388–92. | en_US |
dc.identifier.issn | Not Available | - |
dc.identifier.uri | http://krishi.icar.gov.in/jspui/handle/123456789/53521 | - |
dc.description | Not Available | en_US |
dc.description.abstract | Crop growth, health, and correspondingly yield are much affected by abiotic environmental factors. Abiotic stress is considered as a threat to food security and has a disastrous consequence. Phenotyping parameters such as leaf area assessment is of utmost importance in determining the stresses due to water and environmental factors, micronutrients deficiencies, leaf diseases, pests, etc. In this study, a non-destructive approach through digital image analysis has been presented to assess the total leaf area of rice plants grown in pot culture. Images have been captured from four different angles with respect to the initial position of the camera. Support Vector Regression (SVR) and Tuned SVR have been employed by considering the pixel area of leaves obtained from different angles. Performance of Tuned SVR has been found better than the SVR on training and testing dataset based on RMSE values. A web-solution has been designed and developed to implement the presented approach using 3-tier architecture: Client-Side Interface Layer (CSIL), Database Layer (DL) and Server Side Application Layer (SSAL). | en_US |
dc.description.sponsorship | Not Available | en_US |
dc.language.iso | English | en_US |
dc.publisher | Not Available | en_US |
dc.relation.ispartofseries | Not Available; | - |
dc.subject | Image analysis | en_US |
dc.subject | Leaf area | en_US |
dc.subject | Non-destructive phenotyping | en_US |
dc.subject | Rice | en_US |
dc.subject | Support Vector Regression (SVR) | en_US |
dc.subject | Tuned Support Vector Regression | en_US |
dc.title | Leaf area assessment using image processing and support vector regression in rice | en_US |
dc.title.alternative | Not Available | en_US |
dc.type | Research Paper | en_US |
dc.publication.projectcode | Not Available | en_US |
dc.publication.journalname | Indian Journal of Agricultural Sciences | en_US |
dc.publication.volumeno | Not Available | en_US |
dc.publication.pagenumber | 388-92 | en_US |
dc.publication.divisionUnit | Computer Application | en_US |
dc.publication.sourceUrl | Not Available | en_US |
dc.publication.authorAffiliation | ICAR::Indian Agricultural Statistics Research Institute | en_US |
dc.ICARdataUseLicence | http://krishi.icar.gov.in/PDF/ICAR_Data_Use_Licence.pdf | en_US |
dc.publication.naasrating | 6.25 | en_US |
dc.publication.naasrating | 6.21 | - |
Appears in Collections: | AEdu-IASRI-Publication |
Files in This Item:
File | Description | Size | Format | |
---|---|---|---|---|
IJAS Lead Area Estimation Tanuj Paper.pdf | 1.23 MB | Adobe PDF | View/Open |
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