AI-powered sensor fault detection for cost-effective smart greenhouses

• Published in: Computers and electronics in agriculture 2024-09, Vol.224, p.109198, Article 109198
• Main Authors: Mohammadhossein Shekarian, Seyed, Aminian, Mahdi, Mohammad Fallah, Amir, Akbary Moghaddam, Vaha
• Format: Article
• Language: English
• Subjects: Deep Learning; Fault Tolerance; Greenhouse Sensor Network; Internet of Things
• ISSN: 0168-1699; 1872-7107
• DOI: 10.1016/j.compag.2024.109198

•Connecting IoT systems to cloud servers enables real-time use of AI algorithms and flexible ML model implementation.•AI models can capture the nonlinear correlations among greenhouse variables.•Even weak correlations between sensor data can significantly influence prediction accuracy.•Model training on long-term collected data accounts for varying time and weathers.•Adding more predictors enhances model performance for fault control of sensors. Sensor networks in greenhouses play a pivotal role in controlling the stability of environmental and chemical factors. The Internet of Things (IoT) has been widely adopted for monitoring various sensor networks in greenhouses. In the present study, an IoT platform for remote monitoring of greenhouse environment is designed. The platform consists of a sensor node, a sink node, and an edge server. The sensor node measures indoor and outdoor humidity and temperature, interior CO(g), and interior luminosity and transmits data to the sink node, where it is timestamped. The sink node is connected to an edge server through the Mosquitto MQTT broker and the data is subsequently transferred to a MongoDB Cloud infrastructure, where the data of each variable is stored in proper formats. In the second part of the study, four 1D convolutional neural networks (CNNs) were developed for data prediction of each sensor to provide fault-tolerance in the system. The first three models, for predicting inner humidity and temperature, outdoor temperature, and outdoor humidity, directly predict the actual data of the faulty sensor based on regression analysis. The last model is designed for predicting CO and luminosity and performs data classification for faulty sensors. The models provide a high level of precision in their predictions. The RMSE for interior temperature and humidity and exterior temperature and humidity are 0.86 ℃, 3.47%, 0.682 ℃, and 2.74%, respectively. Additionally, the accuracy for luminosity and CO classification are 89.70% and 83.43%. Comparison of 1D CNN, decision tree, and linear regression model revealed that the machine learning models considerably outperform linear regression model, and they can better capture the nonlinear correlations among the variables. Furthermore, the predictive outcomes of the models were consistent across different weather conditions. The proposed methodology can be used to induce tolerance against faulty reads at sensor level in greenhouse sensor networks, independent of time and the d