@inproceedings{4d4577172bed4e0d8e75a0285c1ca49b,
title = "A Bayesian Neural ODE for a Lettuce Greenhouse",
abstract = "Greenhouse production systems play a crucial role in modern agriculture, enabling year-round cultivation of crops by providing a controlled environment. However, effectively quantifying uncertainty in modeling greenhouse systems remains a challenging task. In this paper, we apply a novel approach based on sparse Bayesian deep learning for the system identification of lettuce greenhouse models. The method leverages the power of deep neural networks while incorporating Bayesian inference to quantify the uncertainty in the weights of a Neural ODE. The simulation results show that the generated model can capture the intrinsic nonlinear behavior of the greenhouse system with probabilistic estimates of environmental variables and lettuce growth within the greenhouse.",
author = "Sjoerd Boersma and Xiaodong Cheng",
year = "2024",
month = sep,
day = "11",
doi = "10.1109/CCTA60707.2024.10666596",
language = "English",
isbn = "9798350370959",
series = "2024 IEEE Conference on Control Technology and Applications, CCTA 2024",
publisher = "IEEE",
pages = "782--786",
booktitle = "2024 IEEE Conference on Control Technology and Applications, CCTA 2024",
address = "United States",
note = "2024 IEEE Conference on Control Technology and Applications, CCTA 2024 ; Conference date: 21-08-2024 Through 23-08-2024",
}