VertINGreen: A Practical Application for Planning and Monitoring Indoor Vertical Green Living Walls Based on Remote Sensing and Machine Learning Models

Maintaining indoor air quality in densely built environments presents growing challenges due to rising energy demands. Vertical green living walls offer a promising, sustainable, and nature-based solution; however, their performance varies widely across different conditions, and their maintenance remains complex, posing barriers that limit their widespread adoption. We introduce VertINGreen, a first-of-its-kind web application that supports both the planning and real-time monitoring of indoor green wall systems. VertINGreen tools were developed using machine learning models trained on extensive environmental and remote sensing hyperspectral data. The planning tool is based on 1957 gas exchange measurements taken from six common indoor plant species. Data were used to model carbon assimilation and plant transpiration under varying indoor conditions. The resulting models achieved high predictive accuracy (R² > 0.94 for assimilation and > 0.66 for transpiration), enabling users to estimate carbon reduction and potential energy savings from decreased air exchange rates. The monitoring tool uses hyperspectral images and machine learning to map physiological activity across the wall and detect early signs of stress. Feature-selection methods allowed accurate predictions using as few as 10 spectral bands, making the system compatible with low-cost imaging hardware. The monitoring model successfully detected declines in plant performance weeks before visible symptoms appeared. By integrating accurate planning with early warning monitoring, VertINGreen provides a comprehensive framework for enhancing indoor environmental quality and reducing energy consumption. VertINGreen empowers architects, engineers, and building managers to design and maintain green wall systems with confidence and efficiency, translating scientific insight into practical tools for sustainable indoor environments.