An intelligent monitoring model for greenhouse microclimate based on RBF Neural Network for optimal setpoint detection

Abbood, Hayder M.; Nouri, N.M.; Riahi, M.; Alagheband, Seyed Hamed

doi:10.1016/j.jprocont.2023.103037

Cited by 8 publications

(4 citation statements)

References 42 publications

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“…According to this learning mechanism, the adjustment formulas for the three input weights of the neuron, w kp , w ki , w kd , can be derived. These specific formulas are detailed in Equations ( 13) to (15).…”

Section: Design Of Snpid Controllermentioning

confidence: 99%

“…These greenhouse systems not only encompass classical automatic control theories, but also support nonlinear, time-varying, and complex systems. For instance, PID control systems that combine expert systems [14], genetic algorithms [15], fuzzy control [16], and neural networks [17] maintain the simplicity and convenience of PID, while significantly enhancing accuracy, stability, and robustness. In recent years, with the increasing demands for controlling complex industrial systems and nonlinear dynamic systems, various advanced control algorithms have emerged.…”

Section: Introductionmentioning

confidence: 99%

“…Despite significant advancements, current technologies exhibit notable limitations when dealing with the complexity and dynamic changes of greenhouse environments. For example, in the field of greenhouse environment management, Abbood H M et al [15] developed an intelligent monitoring model based on Radial Basis Function (RBF) neural networks, aimed at optimizing the microclimate of greenhouses by precisely adjusting set points. In a 2021 study, Jia Y.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Intelligent Regulation of Temperature and Humidity in Vegetable Greenhouses Based on Single Neuron PID Algorithm

Huang,

Xiang,

Leng

et al. 2024

Electronics

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In order to meet the demands of autonomy and control optimization in solar greenhouse control systems, this paper developed an intelligent temperature and humidity control system for greenhouses based on the Single Neuron Proportional Integral Derivative (SNPID) algorithm. The system is centered around the Huada HC32F460 Micro-Controller Unit (MCU) and the RT-Thread operating system, integrated with the SNPID control algorithm. Through comprehensive simulation, model construction, and comparative experiments, this system was thoroughly evaluated in comparison with traditional PID control systems (cPID) that rely on overseas software and hardwsbuare. Simulation results show that our new system significantly outperforms traditional PID (Proportional Integral Derivative) systems in terms of temperature control stability and accuracy. Experimental data further confirm that, while ensuring cost-effectiveness, the new system achieves a remarkable 50.2% improvement in temperature and humidity control precision compared to traditional systems. The temperature Root Mean Square Error (RMSE) in the experimental greenhouse is 0.734 compared to 1.594 in the comparison greenhouse, indicating better stable temperature control capability. The vents in the experimental greenhouse have a maximum opening of 67 cm and a minimum of 5 cm, showing a quick response property to high temperatures. In contrast, the control greenhouse has a maximum vent opening of 55 cm, remaining unchanged during the test period, which reflects its slower response to temperature fluctuations. These results demonstrate the significant advantages of the designed solar greenhouse temperature and humidity control system in terms of autonomy and control optimization, providing an efficient and economical solution for solar greenhouse environmental management. This system shows significant practical application perspective in promoting intelligent agriculture and sustainable agricultural production, highlighting its broad impact and potential significance.

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Section: Design Of Snpid Controllermentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Intelligent Regulation of Temperature and Humidity in Vegetable Greenhouses Based on Single Neuron PID Algorithm

Huang,

Xiang,

Leng

et al. 2024

Electronics

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“…This work effectively estimates the P-concentration in rice leaves but is limited to the tillering stage of potted rice. Moreover, in [8], the authors identify the optimal environmental parameters for crops under specific conditions during greenhouse production. Specifically, they combine genetic algorithms with IoT devices to maximize crop yields while minimizing energy consumption.…”

Section: Introductionmentioning

confidence: 99%

Investigating Precise Decision-Making in Greenhouse Environments Based on Intelligent Optimization Algorithms

Zhu,

Bi,

Tang

2024

Processes

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The precise control of a greenhouse environment is vital in production. Currently, environmental control in traditional greenhouse production relies on experience, making it challenging to accurately control it, leading to environmental stress, resource waste, and pollution. Hence, this paper proposes a decision-making greenhouse environment control strategy that employs an existing monitoring system and intelligent algorithms to enhance greenhouse productivity and reduce costs. Specifically, a model library is created based on machine learning algorithms, and an intelligent optimization algorithm is designed based on the Non-Dominated Sorting Genetic Algorithm III (NSGA-3) and an expert experience knowledge base. Then, optimal environmental decision-making solutions under different greenhouse environments are obtained by adjusting the greenhouse environmental parameters. Our method’s effectiveness is verified through a simulated fertilization plan that was simulated for a real greenhouse tomato environment. The proposed optimization solution can reduce labor and time costs, enable accurate decision-making in the greenhouse environment, and enhance agricultural production efficiency.

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An optimized approach to hourly temperature and humidity setpoint generation for reducing tomato disease and saving power cost in greenhouses

Wang,

Laktionov,

Rodríguez Díaz

et al. 2024

Computers and Electronics in Agriculture

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An intelligent monitoring model for greenhouse microclimate based on RBF Neural Network for optimal setpoint detection

Cited by 8 publications

References 42 publications

Intelligent Regulation of Temperature and Humidity in Vegetable Greenhouses Based on Single Neuron PID Algorithm

Intelligent Regulation of Temperature and Humidity in Vegetable Greenhouses Based on Single Neuron PID Algorithm

Investigating Precise Decision-Making in Greenhouse Environments Based on Intelligent Optimization Algorithms

An optimized approach to hourly temperature and humidity setpoint generation for reducing tomato disease and saving power cost in greenhouses

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