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International Journal of Electrical and Computer Engineering (IJECE)International Journal of Electrical and Computer Engineering (IJECE)

Modern agriculture faces growing challenges in meeting food and resource demands, particularly with increasing pressure on water and fertilizer usage. This study proposes a fuzzy logic-based algorithm to optimize bio-fertigation by managing key greenhouse parameters: temperature, humidity, soil pH, and soil moisture. Implemented in MATLAB, the system automates the control of actuators (fan, heater, irrigation, fertilization and fertigation pumps) based on sensor data and fuzzy rules. Results show a 27.58% reduction in water use, 58.82% decrease in fertilizer consumption, and a 47.5% increase in tomato yield. Additionally, statistical error metrics mean absolute error (MAE), mean squared error (MSE), root mean squared error (RMSE), and mean absolute percentage error (MAPE) were reduced to zero, confirming the systems high precision and effectiveness in promoting sustainable agricultural practices.

This study demonstrates the effectiveness of the fuzzy logic-based control system in optimizing the greenhouse microclimate and automating actuator responses.Statistical evaluations confirm the models accuracy and reliability, leading to measurable improvements in resource efficiency with significant reductions in water and fertilizer usage.Ultimately, the optimized environmental conditions resulted in a noticeable increase in tomato yield, highlighting the potential of fuzzy logic for sustainable crop production.

Further research should explore the integration of additional environmental parameters, such as light intensity and CO2 levels, to create a more comprehensive greenhouse control system. Investigating the application of this fuzzy logic approach to other crops beyond tomatoes, considering their specific needs and growth characteristics, would broaden the systems applicability. Finally, combining the fuzzy logic system with machine learning algorithms could enable predictive control, anticipating environmental changes and proactively adjusting actuator settings for even greater efficiency and yield optimization, potentially leveraging cloud computing for real-time data analysis and remote control capabilities.

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