Prediction of Strawberry Leaf Color Using RGB Mean Values Based on Soil Physicochemical Parameters Using Machine Learning Models

Intensively grown strawberries in a greenhouse require frequent and precise soil physicochemical constituents for optimal production. Strawberry leaf color analyses are the most effective way to evaluate status protect against excess environmental nutrients financial setbacks. Meanwhile, precision agriculture (PA) endorsements have been utilized mimic solutions these problems. This research aimed create machine learning models such as multiple linear regression (MLR) gradient boost (GBR) simulating strawberry changes related components plant age using RGB (red, green, blue) mean values. The properties of largest varied colored leaves were precisely measured by multifunctional sensor from rooting zones. Simultaneously, 400 leaflets detached each vegetative reproductive stage, individual captured digital imaging system. values images extracted image segmentation algorithms processing technique. Consequently, MLR GBR developed predict based on measurements age. model vigorously fitted with throughout growth R2 RMSE (R = 0.77, 7.16, G 0.72, 7.37, B 0.70, 5.68), respectively. Furthermore, performed moderately 0.67, 8.59, 0.57, 9.12, 0.56, 6.81) when consecutively predicting leaves. Eventually, more effectively than high-performance metrics. In addition, uses visualization technology measure progress, it performs well dynamic color.