Sensor‐Guided Smart Irrigation for Tomato Production: Comparing Low and Optimum Soil Moisture in Greenhouse Environments - Ibrahim Dirlik; Ferhat Uğurlar; Cengiz Kaya (Food and Energy Security)
ABSTRACT Effective irrigation management is crucial for optimizing crop production, particularly in water‐scarce regions. This study evaluated the performance of an Arduino‐based system designed to monitor and control soil moisture in a greenhouse setting, focusing on its impact on tomato plant growth, fruit yield, and fruit size under two different irrigation treatments. Treatment 1 (T1) involved low moisture with significant fluctuations (55%–85% soil moisture), while Treatment 2 (T2) maintained optimal and stable moisture levels (70%–85%). Soil moisture dynamics revealed that in T1, moisture levels oscillated significantly, dropping to 55% before irrigation restored them to 85%. This cyclical pattern indicates a stress‐response mechanism triggered by the system, which is essential for mitigating plant stress and ensuring optimal growth. Conversely, the optimal moisture treatment maintained more stable soil moisture levels between 70% and 85%, promoting healthy plant development and physiological functions. The correlation between sensor readings and gravimetric measurements was analyzed using a 45° diagonal correlation approach, demonstrating strong agreement between the two methods and reinforcing the reliability of sensor‐based irrigation. Physiological assessments indicated that seedlings under optimal irrigation experienced a 30% increase in fresh weight, a 6% increase in dry weight, a 16% increase in plant height, and a 25% higher SPAD values compared to T1 at the young stage. At maturity, T2 plants exhibited a 52% increase in fresh weight, a 78% increase in dry weight, and a 121% increase in plant height. Fruit yield increased by 47% in T2, with an average of 56 fruits per plant compared to 45 in T1, and the average fruit weight was 85 g in T2 compared to 56 g in T1. Future research should explore the integration of advanced sensors, machine learning algorithms, and predictive models to further optimize irrigation strategies, with an emphasis on scalability and environmental impact. By refining these technologies, agriculture can achieve more sustainable and productive outcomes in the face of increasing environmental challenges.