Greenhouse Environmental Monitoring and Control
Artificial Intelligence in Environmental Monitoring and Control
Predicting and managing greenhouse microclimates is challenging due to the complex dynamics of heat transfer within these controlled environments. Effective environmental management is essential to cultivating crops under optimal microclimate conditions, influenced by key factors such as temperature, relative humidity, and photosynthetically active radiation. In greenhouses with no feedback system control (i.e., open loop system), environmental control and scheduling are determined according to the grower’s perspective. The various environmental factors are set manually, resulting in unfavorable climate conditions due to overestimating and underestimating environmental conditions. In a closed control loop system, the grower sets up a general strategy for controlling the environment. Once the general strategy is defined, the control system takes over and makes detailed decisions of when to make the necessary adjustments to the greenhouse environment. Closed loop systems require data acquisition of environmental parameters from sensors, such as temperature, radiation, and relative humidity. The current state of the closed control loop system is compared against the system’s desired state, after which a decision will be made based on the comparison. The advantages of a closed loop system over a conventional one include significant energy savings since sensors responsible for heating, humidity, shading, etc., are only activated when needed for optimum climate control. The main disadvantage of closed-loop systems in greenhouses is that they focus on the current state of a single environmental factor, so actions are taken a posteriori—knowledge that is gained through empirical evidence. Artificial intelligence (AI) offers the potential to predict changes in environmental parameters more accurately than simple closed-loop systems.
Machine Learning
Machine learning (ML) is the most widely used AI technique in greenhouse climate control due to its ability to analyze complex data patterns, predict environmental changes, and automate climate management systems. It involves training algorithms to recognize patterns in data collected from sensors placed inside the greenhouse. Greenhouse environments are highly dynamic, with multiple factors influencing plant growth. ML models excel in managing this complexity by:
Deep Learning
Among AI approaches, deep learning techniques, a subset of machine learning, excel in extracting meaningful patterns from complex datasets, making them particularly suited for modeling the nonlinear systems typical of greenhouse environments. These techniques are valued for their ability to produce reliable outputs by learning from historical data and continue to play a key role in greenhouse modeling.
Fuzzy Logic
Fuzzy logic is an AI technique for handling uncertain or imprecise data. Unlike traditional binary logic, fuzzy systems use degrees of truth. In greenhouses, fuzzy logic controllers help manage complex systems where inputs such as temperature, humidity, and carbon dioxide levels may not be strictly binary. They provide smooth transitions in control, improving system stability. For example, a fuzzy logic controller can adjust the greenhouse ventilation system based on real-time data inputs, considering slight variations in temperature and humidity rather than fixed threshold levels.
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