Exploring the best model for sorting Blood orange using ANFIS method

OranOrange has abundant nutritional properties and is consumed worldwide.  Sorting oranges of different masses based on their physical traits could help reduce packaging and transportation cost.  The ‘Blood’ cultivar of Iranian oranges from Kermanshah province of Iran (7.03 °E 4.22 °N) was used in this study.  100 samples were randomly selected.  During the two-day experiment, all measurements were carried out inside the laboratory at mean temperature of 24°C.  In this study, some physical properties of ‘Blood’ orange were measured, such as length, width, thickness, volume, mass, mean value of geometric diameter, sphericity and projected area.  ANFIS and linear regression models were employed to predict the mass based on sphericity and mean of projected area inputs.  In ANFIS model, samples were divided into two sets, with 70% for training set and 30% for testing set.  The coefficient of determination (R2) for ANFIS and linear regression models were 0.983 and 0.927, respectively.  It is shown that the mass can be estimated based on ANFIS model better than linear regression model.   Keywords: linear regression, orange, packaging, physical properties, sorting

Modeling of wheat yield and sensitivity analysis based on energy inputs for three years in Abyek town, Ghazvin, Iran

To get a proper energy consumption pattern and an increase in energy productivity, determining a relationship between energy inputs and outputs is necessary.  In this study, the equivalent energy of inputs and outputs data used in wheat production in Abyek town of Ghazvin province, Iran was collected from farmers over three years.  The energy ratio was obtained as 2.11, 2.08 and 2.03 and energy productivity was obtained as 0.15, 0.14 and 0.14 (kg MJ-1) for 2010, 2009 and 2008, respectively.  It was found that the contributions of indirect and non-renewable energies on wheat yield were more than the impacts of direct and renewable energies.  To determine the effects of energy inputs on wheat yield, the Cobb–Douglas production function was used.  Model 1 was composed of individual energy inputs: labor, machinery, electricity, diesel fuel, water for irrigation, fertilizer, chemicals and seed energies  In Model 2 energy inputs divided to direct and indirect energies and in Model 3 they divided to renewable and non-renewable energies.  The R2 values in all three models were more than 0.98 and showed that the models can estimate well.  The sensitivity analysis results for Model I showed that the major marginal physical productivities (MPPs) were water for irrigation, human labor and water for irrigation in 2010, 2009 and 2008, respectively. In Model II, the major MPP belongs to for renewable energy in the same years.   Keywords: energy consumption pattern, Cobb-Dauglas, marginal physical productivity, renewable, return to scal