Empirical curvelet transform based deep DenseNet model to predict NDVI using RGB drone imagery data
Article
Article Title | Empirical curvelet transform based deep DenseNet model to predict NDVI using RGB drone imagery data |
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ERA Journal ID | 41630 |
Article Category | Article |
Authors | Diykh, Mohammed, Ali, Mumtaz, Jamei, Mehdi, Abdulla, Shahab, Uddin, Md Palash, Farooque, Aitazaz Ahsan, Labban, Abdulhaleem H. and Alabdally, Hussein |
Journal Title | Computers and Electronics in Agriculture |
Journal Citation | 221 |
Article Number | 108964 |
Number of Pages | 15 |
Year | 2024 |
Publisher | Elsevier |
ISSN | 0168-1699 |
Digital Object Identifier (DOI) | https://doi.org/10.1016/j.compag.2024.108964 |
Web Address (URL) | https://www.sciencedirect.com/science/article/pii/S0168169924003557 |
Abstract | Predicting accurately the Normalized Difference Vegetation Index (NDVI) trends from RGB images are essential to monitor crops and identify issues related to plant diseases, and water shortages. The current NDVI prediction models are primarily based on traditional machine learning models which lack reliability due to the problem related to atmospheric conditions. To predict NDVI in Prince Edward Island using RGB drone imagery data, this paper proposed a novel framework integrating empirical curvelet transform and DenseNet models. Each channel of RGB drone imagery data was passed through empirical curvelet transform method where the curvelet coefficients were analysed which result in creating a new formula to design NDVI. The output of the new formula was sent to the deep DenseNet to predict the final NDVI. The proposed model was evaluated using quantitative metrics including, Q-Q plot, regression, correlation coefficients, structural similarity (SSIM), peak signal to noise ratio (PSNR) and mean square error (MSE) as well as accuracy ( |
Keywords | NDVI; RGB; DenseNet; Curvelet coefficients ; Drone image ; Prediction |
Contains Sensitive Content | Does not contain sensitive content |
ANZSRC Field of Research 2020 | 461103. Deep learning |
Public Notes | Files associated with this item cannot be displayed due to copyright restrictions. |
Byline Affiliations | UniSQ College |
University of Thi-Qar, Iraq | |
Al-Ayen University, Iraq | |
University of Prince Edward Island, Canada | |
Shahid Chamran University of Ahvaz, Iran | |
Deakin University | |
King Abdulaziz University, Saudi Arabia |
https://research.usq.edu.au/item/z63wy/empirical-curvelet-transform-based-deep-densenet-model-to-predict-ndvi-using-rgb-drone-imagery-data
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