Abstract
The abstract discusses the use of artificial neural networks (ANNs) in greenhouse technology, highlighting their potential applications in areas such as microclimate prediction and energy management. It also mentions the importance of adapting neural network models to new technologies like the internet of things (IoT) and machine learning (ML) for future developments in smart agriculture. The document emphasizes the need for optimization models in training neural networks and provides insights that can guide the development of advanced agricultural technologies incorporating neural networks.
Introduction
The introduction explains that greenhouses are structures designed to shield crops from harmful elements, providing an optimal environment for plant growth. These closed systems help maintain specific climate conditions, enhancing the quality and quantity of agricultural products by protecting plants from external factors. The design and control of the microclimate within greenhouses are crucial for maximizing crop yield and quality.
Artificial neural network
Artificial Neural Networks (ANNs) are computational models inspired by the human brain that are used in greenhouse technology to optimize various tasks such as microclimate prediction and energy expenditure. They are primarily implemented with feedforward architecture, with potential for further development by incorporating technologies like the Internet of Things (IoT) and machine learning (ML) in the future. ANNs offer advantages in enhancing crop production quality and quantity by controlling the greenhouse environment, but their application in greenhouses is still evolving, with room for improvement in utilizing optimization algorithms and exploring different network architectures.
Application in green house
The application of Artificial Neural Networks (ANNs) in predicting the greenhouse microclimate is crucial for simplifying the treatment of variables related to greenhouse conditions. ANNs help address challenges in calculating speed, predicting behavior, and controlling various elements within the greenhouse environment. By utilizing non-linear system models, ANNs offer a valuable tool for modeling and optimizing the greenhouse climate to improve efficiency and precision in managing environmental variables.
For more precision
Recurrent Neural Networks (RNNs) are utilized for predicting microclimate conditions in greenhouses, offering advantages over Feedforward Neural Networks (FFNNs) due to their faster computation and structure similarity. Researchers like Fourati et al. have applied RNNs, specifically Elman-type RNNs, to model greenhouse dynamics and control systems effectively. By combining RNNs with FFNNs in a cascaded manner, they achieved improved performance in greenhouse operation and control, showcasing the potential of RNNs in adapting to changing environmental conditions for accurate predictions.
Green house energy optimization
Artificial Neural Networks (ANNs) are utilized in greenhouses to optimize energy consumption, particularly from heating and ventilation systems. These networks help in developing optimal control strategies based on mathematical models to calculate energy consumption and minimize overall energy usage. By integrating sustainable technologies like photovoltaic collectors, ANNs enable real-time energy optimization and performance prediction in greenhouses.
Application guidelines
The guidelines for the application of Artificial Neural Networks (ANNs) in greenhouses focus on using ANNs to predict the microclimate and optimize energy usage in greenhouse environments. These guidelines highlight the promising potential of ANNs in improving greenhouse operations and suggest exploring the integration of physical models with neural networks to enhance data processing and forecasting accuracy in greenhouse settings. The use of ANNs in conjunction with techniques like fuzzy logic and physical models can lead to more effective decision-making and optimization in greenhouse agriculture.