Development of an automatic watering system and plant growth monitoring for hydroponic chili production using neural networks - Rachmawanto, Eko Hari; Mulyono, Ibnu Utomo Wahyu; Widyatmoko, Karis; Sarker, Md Kamruzzaman; Mohd Yaacob, Noorayisahbe; Doheir, Mohamed A. S. (International Information and Engineering Technology Association, 2025)
This study addresses the challenges faced in traditional chili production, where reliance on manual methods often leads to inefficiencies and suboptimal crop yields. To enhance the efficiency of chili production, this research develops an automated monitoring system that integrates watering management and pH adjustment based on IoT. Utilizing Neural Networks (NN) for plant growth monitoring, the system executed 120 automatic watering sessions over a 30-day period, ensuring optimal moisture levels and nutrient absorption. The results revealed a predictive performance characterized by a Root Mean Square Error (RMSE) of 0.49 and a coefficient of determination (R²) of 0.99, indicating high accuracy in forecasting plant growth dynamics. The novelty of this research lies in its comprehensive approach, combining real-time monitoring and automated adjustments to optimize plant health. For future research, it is recommended to incorporate additional environmental sensors and expand the dataset to improve the model's adaptability and predictive capabilities. This could lead to the development of more advanced smart agriculture systems that can efficiently cater to various crops and environmental conditions, ultimately enhancing overall agricultural productivity.