Boosting the prediction accuracy of a process-based greenhouse climate-tomato production model by particle filtering and deep learning

Author:

Zhou XiaohanORCID,Liu Qingzhi,Katzin David,Qian Tian,Heuvelink Ep,Marcelis Leo F.M.

Publisher

Elsevier BV

Subject

Horticulture,Computer Science Applications,Agronomy and Crop Science,Forestry

Reference50 articles.

1. Advanced metaheuristic optimization techniques in applications of deep neural networks: a review;Abd Elaziz,2021

2. A tutorial on particle filters for online nonlinear/non-Gaussian Bayesian tracking;Arulampalam;IEEE Trans. Signal Process.,2002

3. Blok, C., Leyh, R., Baeza Romero, E. J., van Os, E. A., & van der Salm, C. (2020). An investment order tool to guide development of greenhouse horticulture for two specific regions. Acta Horticulturae, 1268, 27–34. 10.17660/ActaHortic.2020.1268.4.

4. Parameter estimation in a model of the human circadian pacemaker using a particle filter;Bonarius;I.E.E.E. Trans. Biomed. Eng.,2021

5. A method for modelling greenhouse temperature using gradient boost decision tree;Cai;Inform. Process. Agric.,2022

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