Modeling the relationship between iron concentration in citrus leaves and some soil properties using artificial neural network (case study of southern Kerman province)

This study was conducted to evaluate the relationship between leaf iron and some easily-available soil properties in citrus orchards in the southern region of Kerman province by artificial neural network modeling and stepwise regression. For this purpose, 40 orchards were selected from the study area and the physical and chemical properties of soil and iron in the plant leaves were measured. Using artificial neural network in different models with different data from soil properties as input and leaf iron as output, the ability of these models to predict leaf iron concentration was evaluated. The results showed artificial neural network with variables of organic carbon, pH, clay, phosphorus, TNV and electrical conductivity with explanation coefficient of 0.86 and 0.81 and root mean square error (RMSE) of 14.60 and 20.13 mg.kg-1 for data Training and testing were the best models in estimating leaf iron. Comparison of regression and neural network models in the test data showed that the neural network had a higher accuracy with an explanation coefficient of 0.81 than stepwise regression with an explanation coefficient of 0.2. The amount of RMSE in the neural network also improved and increased from 27.72 mg.kg-1 in the stepwise regression model to 20.13 mg.kg-1 in the neural network. Artificial neural networks have been able to predict the iron in plant leaves based on the easily-available properties of the soil, so that by choosing organic carbon as the input of the first model to the best model by selecting organic carbon, pH, clay, phosphorus, TNV and electrical conductivity, model accuracy increased.