Automated sensing and variable-rate supply of nutrients in hydroponic solutions according to the status of crop growth would allow more efficient nutrient management for crop growth in closed systems. The Structure from Motion (SfM) method has risen as a new image sensing method to obtain 3D images of plants that can be used to estimate their growth, such as leaf cover area (LCA), plant height, and fresh weight. In this sense, sensor fusion technology combining ion-selective electrodes (ISEs) and a machine vision technique would be useful in measuring and managing the concentrations of nutrients based on the biomass estimation of crops grown in a plant factory. In this study, a computer-based nutrient management system was developed to effectively manage concentrations of NO₃, K, and Ca ions using an array of ISEs and fertilizer pumps to grow lettuce in a closed hydroponic system. Images of lettuce grown were obtained with a RGB camera and an image processing algorithm based on Excess Green (ExG) and RGB indexes was developed to estimate biophysical parameters related to lettuce growth, such as LCA and fresh weight. The growth parameters estimated with the developed image algorithm were validated by a comparison to the actual values. In a validation test, the fresh weights of lettuce plants estimated with the developed image processing algorithm were almost comparable to actual values, exhibiting slopes of 1.26 and with R² of 0.89. In addition, a Gompertz growth model fitted changes in the estimated fresh weight over time well (R² >0.99). There were no significant correlations between individual ion absorption rate and the parameters of lettuce growth, but NO₃ ion absorption showed a potential (P<0.1) as a nutrient for managing plant growth. The results of this research provided a potential of using an automated nutrient control system and an image processing method for efficient management of lettuce plants grown in a plant factory. Further studies include variable nutrient management of lettuce based on automated sensing of lettuce growth status using an on-the-go image acquisition system that can automatically take lettuce images while moving along a predefined path.