Estimating Corn Plant Leaf Area Using Artificial Neural Network

This experiment was designed for easy and accurate estimation of corn plant leaf area with multilayer perceptron (MLP) neural network and conducted at Faculty of Agricultural Sciences, University of Guilan, Rasht, Iran in 2013. Three plant densities (75, 85 and 95 thousand plants/ha) and five genotypes (Persia 454, 484, 565, 626 and 647) were considered as treatments. Samplings were conducted at various times (66, 80, 94 & 108 DAP). At each sampling, number of leaves per plant, number of green leaves, leaf length and width, plant leaf area, leaf and stem dry weight, plant height, stem diameter and biological yield were measured. Correlations analysis indicated that measured characteristics had positive significant correlation with plant leaf area (r≥0.859**) and they can be used as inputs for estimation of leaf area. Among these variables, the highest sensitivity was associated to leaf width, number of green leaves, leaf length, number of leaves per plant and stem diameter, respectively. However the model with a lower number of variable, i.e. including leaf width, number of green leaves and leaf length was more appropriate for quick estimation of leaf area. When a single input had been used for estimation of leaf area, leaf dry weight offered a better simulation than other variables (d = 0.989), so that 95.69% of leaf area changes was described through leaf dry weight (R2 = 0.9569) and it can estimate leaf area well (RMSE (%) = 15.67). In both methods of estimation for leaf area (by using single input and sensitivity analysis), the best fitted models were not affected by cultivar, plant density and interaction of these two factors. Therefore, a general model can be used for rapid and accurate leaf area estimation of genotypes and plant densities used in the experiment.