<![CDATA[In this study, we propose a comprehensive comparison of deep learning models for predicting the status of fan actuators in an IoT-enabled smart greenhouse environment. The dataset, consisting of 37,923 observations, captures environmental variables such as temperature, humidity, and soil nutrient levels, alongside actuator statuses. The aim is to accurately predict the binary status of the fan actuator (on or off) based on these environmental conditions. To address the challenge of class imbalance in the dataset, we apply the Synthetic Minority Oversampling Technique (SMOTE) to generate synthetic samples of the minority class, ensuring a balanced distribution for training. Three deep learning architectures Multi-Layer Perceptron (MLP), Convolutional Neural Network (CNN), and Long Short-Term Memory (LSTM) are implemented and evaluated using 10-fold cross-validation. The performance of each model is assessed using accuracy, precision, recall, and F1 score metrics. Results indicate that all models demonstrate strong predictive capabilities, with the LSTM excelling in capturing temporal dependencies, the CNN effectively extracting spatial patterns, and the MLP achieving overall high accuracy in structured data. The findings of this study provide valuable insights into the strengths and weaknesses of these models for actuator status prediction, which can guide future developments in smart greenhouse automation systems]]>
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