Multi-spectral UAV Data Analysis for Wheat Yield Prediction: A Deep Learning Approach
dc.contributor.advisor | Ingunn Burud | |
dc.contributor.advisor | Sahameh Shafiee | |
dc.contributor.author | Ashar, Muhammad | |
dc.date.accessioned | 2023-09-21T16:27:16Z | |
dc.date.available | 2023-09-21T16:27:16Z | |
dc.date.issued | 2023 | |
dc.identifier | no.nmbu:wiseflow:6872769:55141168 | |
dc.identifier.uri | https://hdl.handle.net/11250/3091161 | |
dc.description.abstract | Through the use of deep learning models and a comprehensive dataset of multispectral time series data collected by unmanned aerial vehicles (UAVs), this thesis aims to predict the grain yield of a wheat field. To find the best candidate for this prediction task, a variety of machine learning and deep learning models are investigated, including Gradient Boosting Regressor (GBR), Deep Neural Networks (DNNs), and Bidirectional Long Short-Term Memory (BiLSTM) networks. | |
dc.description.abstract | ||
dc.language | eng | |
dc.publisher | Norwegian University of Life Sciences | |
dc.title | Multi-spectral UAV Data Analysis for Wheat Yield Prediction: A Deep Learning Approach | |
dc.type | Master thesis |
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Master's theses (RealTek) [1724]