A multi-model deep learning approach to address prediction imbalances in smart greenhouses-Reference-Cited by-同舟云学术

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A multi-model deep learning approach to address prediction imbalances in smart greenhouses

Published:2024-01 Issue: Volume:216 Page:108537
ISSN:0168-1699
Container-title:Computers and Electronics in Agriculture
language:en
Short-container-title:Computers and Electronics in Agriculture

Author:

Morales-García Juan^ORCID,Terroso-Sáenz Fernando,Cecilia José M.

Funder

Faculty of Science and Engineering, University of Manchester

España Ministerio de Ciencia e Innovación

Publisher

Elsevier BV

Subject

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

Reference32 articles.

1. Time-series prediction for sensing in smart greenhouses;Ali,2020

2. Greenhouse monitoring and automation using arduino: a review on precision farming and internet of things (IoT);Ardiansah;Int. J. Adv. Sci., Eng. Inf. Technol.,2020

3. An ensemble learning based hybrid model and framework for air pollution forecasting;Chang;Environ. Sci. Pollut. Res.,2020

4. Ai at the edge: a smart gateway for greenhouse air temperature forecasting;Codeluppi,2020

5. Performance analysis of time series deep learning models for climate prediction in indoor hydroponic greenhouses at different time intervals;Eraliev;Plants,2023

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1. Multi-Timeframe Forecasting Using Deep Learning Models for Solar Energy Efficiency in Smart Agriculture;Energies;2024-08-29

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