AI Summary & Key Takeaways
AI InsightIn the comparison made according to these criteria, it was concluded that the error values of the ANN model [R2= 0.96, RMSE = 3.30, MAE = 1.94, and MAPE = 0.05] were lower than those of the regression model [R2= 0.92, RMSE = 4.71, MAE = 3.31, and MAPE = 0.08], and that the ANN method provided a better fit to the actual values; therefore, the ANN model can be used as an alternative method in estimating the leaf area.
📌 3 Key Takeaways
Abstract
The leaf area measurement is an important parameter in understanding the growth and physiology of a plant. Therefore, this study aimed to develop the best leaf area estimation model for tomato plants grown in plastic greenhouse conditions. The artificial neural network (ANN) and regression analysis techniques were used in the formation of a leaf area estimation model by using the leaf width and leaf length measurements determined by the linear measurement method. The plant material for the study consisted of 420 leaf samples of the Typhoon F1 tomato type grown in plastic greenhouse conditions. In the comparison of the created models according to both methods, the criteria of selecting low values for the root mean square error (RMSE), the mean absolute error (MAE), and the mean absolute percentage error (MAPE), and high value for the determination coefficient (R2 ) were taken into account, and the best estimation models were determined. In the comparison made according to these criteria, it was concluded that the error values of the ANN model [R2= 0.96, RMSE = 3.30, MAE = 1.94, and MAPE = 0.05] were lower than those of the regression model [R2= 0.92, RMSE = 4.71, MAE = 3.31, and MAPE = 0.08], and that the ANN method provided a better fit to the actual values; therefore, the ANN model can be used as an alternative method in estimating the leaf area.