Improving Yield of Tomatoes Grown in Greenhouses Using IoT Based Nutrient Management System

Abstract

Aims: This paper discuss, a study conducted to evaluate the developed automated IoT based fertigation control system for greenhouse for tomato (Solanum lycopersicum L.) crop. Study Design: Different nutrient and irrigation water levels were used to evaluate developed system using three replications in a factorial randomized block design (RBD). Methodology: An automated fertigation scheduling system was implemented in a greenhouse with soil moisture sensors at three depths (15, 30, and 45 cm) within the tomato root zone. R2, RMSE, NSE and MAE values were used to establish the correlation between sensor values and actual soil moisture. Tomato crop biometric parameters were collected and analyzed to evaluate the system's performance. Results: The results indicated strong correlation between sensor and observed soil moisture with R2 (0.8642 to 0.9528), RMSE (1.0786 to 1.8328), NSE (0.8438 to 0.9463), and MAE (0.9729 to 1.7043) values. Highest plant height (255 cm), girth (2.29 cm), number of leaves (21), number of flowers (23.1), fruit length (8.05 cm), fruit weight (110 g), yield/plant (2.75 kg), yield (68.77 t/ha) and sugar (5.1°Brix) were observed with drip irrigation at the rate of 100% ETc and 100% recommended dose of fertilizer (RDF), while minimum values of these parameters were noted in the control treatment. Conclusion: Using sensor-based drip irrigation at 100% ETc and 100% RDF led to a 62.92% increase in tomato yield and water saving of 14.84% compared to the control treatment. For tomato crop, the system required 2.27 l/plant/day water at 100% ETc. The developed automated fertigation system found suitable for greenhouse vegetable crops with the use of sensor based drip irrigation at 100% ETc and 100% RDF.