“Operating Status and Anomaly Monitoring Techniques for ICT Components in Smart Greenhouse”

Information and Communication Technology (ICT) based smart greenhouses have now evolved methods of controlling climate in horticulture to make the production process more efficient. These include the sensors, actuators, communication protocols and possibly cloud computing to enhance the use of resources and obtain the best crop production. Nonetheless, the key components in the implementation of ICT are prone to a number of risks, such as hardware breakdown, interruption in the communication network, and data corruption. Based on the literature review, the paper aims to identify existing methods of monitoring and detecting abnormalities in smart greenhouses for proper functioning. The types of anomalies identified in the study include hardware failure, communication problems, and data outliers, and therefore, appropriate detection methods need to be implemented. Among the approaches of AI and machine learning to use for characterizing and monitoring abnormal activities or improving the system efficiency are the supervised, unsupervised, and deep learning methods. Moreover, signal processing techniques such as Fourier and Wavelet have been introduced for the analysis of anomalous data patterns. The application of multi-variant methods based on machine learning, statistical data analysis, and brainstorming with domain experts would be more effective for creating highly reliable systems for anomaly detection. It also revealed that temperature, humidity, and dust are some of the environmental factors that may have a negative impact on ICT. There are four published corresponding articles planned for the follow-up work, including improving the hybrid models, scalability, and energy saving of the system for sustainable greenhouse management. This research contributes to the development of ICT in modern agriculture, which leads to the establishment of an efficient and effective form of agriculture.