Abstract:
The growth of indoor plants depends heavily on environmental conditions yet researchers have not conducted enough quantitative studies that combine visual data with contextual information. This research evaluates Money Plant (Epipremnum aureum) growth performance through the development of a time-series dataset We obtained daily plant photographs under both indoor and outdoor settings together with the measurements of Light Intensity,Temperature, Humidity and Water pH. The plant area measurements for growth assessment were obtained through image preprocessing steps that included contrast enhancement and HSV-based green masking and pot region exclusion. This study employed eight feature importance techniques to evaluate each parameter's contribution. This analysis revealed that Light Intensity stood as the primary growth factor while Water pH ranked as the secondaryinfluential factors. This research proves that using environmental features with image-derived growth metrics produces both interpretable and accurate predictions for plant growth. The established framework serves as a base for smart horticulture systems which optimize resource utilization and promote sustainable indoor plant maintenance. Keywords: Indoor plant growth, Money Plant (Epipremnum aureum), Image-based analysis, Machine learning, Feature importance, Environmental factors, Smart horticulture.
