Enhancing Smart Greenhouse Efficiency Using Bayesian Optimization and LSTM in IoT-Cloud Frameworks

Abstract

The integration of Internet of Things (IoT) and Cloud Computing technologies has revolutionized the management of smart greenhouses by enabling real-time data collection and centralized processing. Existing work in IoT and cloud computing integration faces significant challenges related to data security, access control, and scalability, particularly in managing large volumes of IoT data. Many systems struggle with maintaining real-time data processing and ensuring efficient interoperability between devices and cloud platforms. Additionally, fine-grained access control and data privacy remain unresolved issues, limiting the effectiveness of these solutions in sensitive environments such as healthcare and agriculture. This paper presents a framework that enhances greenhouse efficiency using Bayesian Optimization and Long Short-Term Memory (LSTM) within an IoT-Cloud infrastructure. IoT sensors collect environmental data such as temperature, humidity, soil moisture, and light intensity, which is then transmitted to a cloud platform for processing and storage. LSTM is employed to predict future environmental conditions, and Bayesian Optimization is used to optimize greenhouse control parameters, such as irrigation and ventilation. The proposed system demonstrates significant improvements in crop yield and resource efficiency. Performance evaluations, including accuracy, precision, recall, F1-score, and AUC, show strong model performance, while efficiency metrics and latency comparisons highlight consistent improvements in system optimization over time. This integrated approach promises sustainable and autonomous management of greenhouse environments, leveraging data-driven insights to enhance productivity and reduce resource consumption.