Red Chief elma çeşidinde yapay sinir ağları ve bazı matematiksel modeller kullanılarak yaprak alan tahminlerinin karşılaştırılması

Abstract

Leaf area index is an important variable in ecological and physiological studies. This study was aimed to determine the most suitable model explaining the leaf area estimation and weekly growth of leaf parameters in Red Chief apple variety. In the first part of the study, the leaf area was modeled through two different models (Model-1 and Model-2) developed based on ANN and power function (LA= AxB). In the second part, the weekly growth of each of the leaf width, length and area parameters were analyzed according to the Gompertz and Logistics function. The results of analysis revealed that leaf area estimations performed by ANN (Training: R2= 0.98, RMSE= 0.922, MAD= 0.614, MAPE= 4.22; Testing: R2= 0.94, RMSE= 3.346 MAD= 1.889 MAPE= 4.88) were more successful than Model-1 and Model-2. In addition, Gompertz has come to the fore as the model that best describes the weekly growth in all leaf parameters (Width: R2= 0.98, RMSE= 0.154, MAD= 0.134, MAPE= 3.65, Length: R2= 0.98, RMSE= 0.180, MAD= 0.145, MAPE= 2.26 and Leaf area: R2= 0.99, RMSE= 0.73, MAD= 0.654, MAPE= 4.60).