Determination of the Effect of Different Nitrogen Doses on the Yield, Yield Components and Chlorophyll Contents of Bread Wheat (Triticum aestivum L.) Cultivars using Artificial Neural Network

Abstract

This research investigates an artificial neural network for predicting the chlorophyll concentration index and the effect of different nitrogen doses on the yield, yield components of Bread Wheat (Triticum aestivum L.). Plants were fertilized with 5, 10, 15, 20, and 25 kg da-1 nitrogen doses. The chlorophyll concentration index of each leaf was measured using a SPAD meter. The coefficient of determination values was found to be 0.99. In artificial neural network modeling, chlorophyll concentration values were estimated with SPAD readings. Artificial neural network modeling successfully described the relationship between actual chlorophyll concentration index values and predicted chlorophyll concentration index values. Agronomic parameters plant height (110.66-92.73 cm), the number of spikes per square meter (461.01-355.50), the number of seeds per spike (43.88-23.83), the weight of seed per spike (2.07-0.91 g), hectoliter weight, thousand-grain weight (43.10-35.89 g), grain yield (638.76-343.06 kg.da-1), protein contents (11.16-8.34 %), the value of sedimentation (19.40-11.94) were found statistically important.