Developing Leaf Area Prediction Model for Curly Lettuce Grown Under Salinity Stress and Applied with Foliar Salicylic Acid

Accurate and non-destructive methods for measuring leaf area are crucial for understanding the growth and physiological variations of the plants under stress conditions. This investigation aimed to develop and assess the effectiveness of various regression models for predicting the leaf area of curly lettuce cultivated under different irrigation water salinities (IWS: 0.30, 4.15, 8.0 dS m-1) and salicylic acid doses (SA: 0, 1, 2 mM). The R2 values for the models ranged from 0.505 to 0.968, with RMSE values between 4.59 and 17.79 cm² and MAE values of 3.44 to 13.05 cm². Using only leaf length (LL) and leaf width (LW) can effectively estimate the leaf area of curly lettuce plants (Model 3, R²: 0.962, RMSE: 7.58 cm², MAE: 5.34 cm²). However, including IWS and SA in prediction models improved their accuracy and reliability. The best model for estimating the leaf area of curly lettuce was found from Model 13, which integrated all four parameters—SA, IWS, LL, and LW—achieving the highest R² (0.968) and the lowest RMSE (4.59 cm²) and MAE (3.44 cm²). Finally, using leaf area prediction models that consider stress conditions can enhance crop management by allowing accurate monitoring of plant health and growth in agriculture.