摘要：Evaluation of neural network based simulation models for the prediction of site specific soil properties of the large agricultural areas will provide important technological benefits for the efficient use of land resources and Agricultural Decision Making System. In this study, an artificial intelligence model was investigated for simulation of site specific iron (Fe) and zinc (Zn) levels in the soils of apple orchard by using a feedforward multilayered Artificial Neural Network (ANN). The measurements of Fe and Zn were made for the topsoil (0-25 cm) and subsoil samples (25-50 cm) collected from forty five different coordinates within intervals of 20 x 10 m based on a grid sampling system in the east and north directions. The measured coordinate values were reflected to the input layer of the developed two input one output feedforward multilayer of the ANN model. Respectively, the measured Fe and Zn values were reflected to its output layer, and then the training stage was started. In the training, back propagation algorithms were used to get the most suitable NN structure for the prediction. As a result of varied trainings, the ANN having two hidden layers and ten neurons produced the best estimation values for site specific Fe and Zn estimations. The most suitable prediction values (R2 = 0.98 and R2 = 0.97, P<0.01) were obtained from the ANN with 10.10.1 structure for site specific Fe and Zn levels in the topsoil. When the simulated values obtained from the ANN model were compared with the measured values, it was observed that successful results were achieved based on varied training values. Hence, the ANN based model should be calibrated for varied training conditions depending on different soil conditions to get more reliable results.
关键词：Articifial neural network; neural structures; iron; zinc; apple orchard
