Patil, S M and Choudhary, S and Kholova, J and Anbazhagan, K and Parnandi, Y and Gattu, P and Mallayee, S and Prasad, K S V V and Kumar, V P and Rajalakshmi, P and Chandramouli, M and Adinarayana, J (2023) UAV-based Digital Field Phenotyping for Crop Nitrogen Estimation using RGB Imagery. In: IEEE IAS Global Conference on Emerging Technologies (GlobConET), 19-21 May 2023, London, United Kingdom.
Full text not available from this repository. (Request a copy)Abstract
Nitrogen (N) is one of the essential nutrients required for healthy crop growth. Field phenotyping for nitrogen stress symptoms is laborious and time-consuming, that way, it is a major bottleneck in nutrition-inclusive agricultural research. Recent advancements in sensors and image processing facilitate color-based quantification of crop greenness from high-resolution RGB images. In this paper, we present unmanned aerial vehicle (UAV)-based digital field phenotyping for the estimation of crop nitrogen content. For this, we conducted a field experiment during the post-rainy season of 2021 at International Crops Research Institute for Semi-Arid Tropics (ICRISAT), Hyderabad, India with long-stature cereal model crop, sorghum (Sorghum bicolor L.) cultivated under three different regimes varying in moisture and soil nitrogen content. A high-resolution RGB sensor (XenmuseX5S) mounted on DJI Matric 210 quadcopter was used for capturing the spatiotemporal imagery. Five different RGB spectrum vegetation indices indicating crop greenness were correlated with ground truth values of crop N content using simple linear regression and stepwise backward regression. With a prediction potential of R 2 =0.65 and MAE=0.27 for an independent dataset, we present a stepwise backward linear regression model as a promising approach for real-time estimation of the N status of sorghum crop.
| Item Type: | Conference or Workshop Item (Paper) |
|---|---|
| Divisions: | Global Research Program - Accelerated Crop Improvement |
| CRP: | UNSPECIFIED |
| Uncontrolled Keywords: | Nitrogen, Unmanned Aerial Vehicle (UAV), Spatiotemporal RGB imagery, Stepwise backward linear regression, Sorghum |
| Subjects: | Mandate crops > Sorghum |
| Depositing User: | Mr Nagaraju T |
| Date Deposited: | 19 Jan 2024 11:02 |
| Last Modified: | 19 Jan 2024 11:02 |
| URI: | http://oar.icrisat.org/id/eprint/12384 |
| Acknowledgement: | UNSPECIFIED |
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