Hybrid CNN-LSTM Model with Custom Activation and Loss Functions for Predicting Fan Actuator States in Smart Greenhouses

Hybrid CNN-LSTM Model with Custom Activation and Loss Functions for Predicting Fan Actuator States in Smart Greenhouses

Smart greenhouses rely on precise environmental control to optimize crop yields and resource efficiency. In this study, we propose a novel hybrid Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM) architecture to predict fan actuator states based on environmental data. The hybrid m...

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Bibliographic Details
Main Authors: Gregorius Airlangga, Julius Bata, Oskar Ika Adi Nugroho, Boby Hartanto Pramudita Lim
Format: Article
Language:English
Published: MDPI AG 2025-04-01
Series:AgriEngineering
Subjects:
smart greenhouse management
precision agriculture
deep learning
CNN-LSTM hybrid model
predictive automation
Online Access:https://www.mdpi.com/2624-7402/7/4/118
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