Automated Hydroponic Growth Simulation for Lettuce Using ARIMA and Prophet Models During Rainy Season in Indonesia
Lendy Rahmadi, Hadiyanto Hadiyanto, Ridwan Sanjaya · International Journal of Advanced Computer Science and Applications · 2024
Hydroponic farming particularly lettuce cultivation, is gaining popularity in Indonesia due to its economical use of water and space, as well as its short growing season. This study focuses on developing of an Automated Hydroponic Growth Simulation for Lettuce Using ARIMA and Prophet Models during the Rainy Season in Indonesia. We developed a simulation model for lettuce development in the Nutrient Film Technique (NFT) hydroponic system using data collected over four harvest periods during the rainy season in early 2024. Two machine learning models, ARIMA and Prophet, are tested to see which is more effective at forecasting lettuce growth. The Prophet model has the greatest results, with a Mean Absolute Error (MAE) of 1.475 and a Root Mean Square Error (RMSE) of 1.808. Based on this, the Prophet model is utilized to create a web application using Streamlit for real-time growth predictions. Future studies should include more data, particularly from the dry season, to increase model flexibility, as well as investigate the use of other crops and machine learning methods, including hybrid models, to improve forecasts.