@article{article_1537132, title={Developing Leaf Area Prediction Model for Curly Lettuce Grown Under Salinity Stress and Applied with Foliar Salicylic Acid}, journal={Journal of Agricultural Faculty of Gaziosmanpaşa University}, volume={41}, pages={178–185}, year={2024}, DOI={10.55507/gopzfd.1537132}, author={Kiremit, Mehmet}, keywords={Lactuca sativa, yaprak boyutları, tahribatsız yöntemler, hassas tarım, regresyon modelleri.}, abstract={Accurate and non-destructive methods for measuring leaf area are crucial for understanding the growth and physiological variations of 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 coefficient of determination (R2) values for the models ranged from 0.505 to 0.968, with Root Mean Square Error (RMSE) values between 4.59 and 17.79 cm² and Mean Absolute Error (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). Incorporating IWS and SA into prediction models enhanced 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.}, number={3}, publisher={Tokat Gaziosmanpaşa Üniversitesi}, organization={Bu çalışma için herhangi bir kurumdan maddi destek almamıştır.}