مجله پژوهش های مکانیک ماشین های کشاورزی
سال پنجم شماره 1 (بهار و تابستان 1395)

The solar radiation data play a very important role for understanding internal structure of solar systems. Due to weather conditions, solar radiation has high fluctuation. Analysis of radiation data help to optimizing these systems. Fractal dimension is one of the methods to analyze time series such as radiation. In this paper some methods such as Sevcik, Katz, MRL, PetrosianD, PetrosianC and Higuchi are used to estimate fractal dimension. These methods were coding by MATLAB software and then the data was analyzed. The result of ANOVA showed that temperature on the wet basis in the MRL method was significant at 1% and relative humidity in the PetrosianC was significant at 5%. According to results, Higuchi method had maximum mean value of fractal dimension and Petrosian method had minimum mean value of fractal dimension. Also fractal dimension of Petrosian method was less than1, therefore, Petrosian method is not reliable(because fractal dimension is a number between 1 and 2). Also, Sevcik and Higuchi was the suggested method for future researches.

The aim of this research is to study two intelligent systems including artificial neural network and multi – layer adaptive neuro-fuzzy inference system (ANFIS) for modeling the output energy in production of greenhouse and open-field cucumber. In order to determine the pattern of consumed energy, the required information was collected directly from the 160 cucumber beneficiary. By analyzing the results, the average input and output energy was respectively equal to 1159901.1 MJ and 173985.256 MJ for greenhouse cucumber production and respectively 75648.11 MJ and 22694.433 MJ for open-field cucumber production . The maximum input energy in production of greenhouse and open-field cucumber was related to fuel and electricity respectively. The minimum input energy was related to seed energy. Using the input and output energy data, modeling of output energy was performed based the input energy and using the mentioned modeling methods. Refer to the results of ANFIS model, the values of coefficient of determination and root mean square error for greenhouse and open-field cucumber were equal to 0.9924, 9920 and 0.051 and 0.0130, respectively, Furthermore, these values were determined using the neural network modelwith optimum structure of (8-10-1) for greenhouse cucumber and structure of (8-12-1) for open-field cucumber. It was revealed that the coefficient of determination value was equal to 0.9492 and 0.9785, and RMSE was equal to 0.0121 and 0.0418, respectively. The result.

In order to study the effect of nitrogen fertilizer and planting density on stem physical properties and shear energy of Gorgan-3 variety of soybean, an experiment was conducted in 2013-2014 at field No.1 of Gorgan University of Agricultural Sciences and Natural Resources. This study had two main factors, the first factor was the amount of nitrogen fertilizer at three levels (100, 200 and 300 kg ha-1) that was added to the soil and the second factor was plant density at three levels (100000, 150000 and 200000 plant ha-1). Plant height, stem diameter and stem shear energy were measured. Cutting experiment was conducted by pendulum system and then the relationship between shear energy and stem diameter was examined. The experiments were carried out in the form of factorial experiment based on the complete randomized block design. The highest and lowest plant height was 71.23 cm and 50.73 cm respectively wich were respectively acquired with planting density of 200,000 and 100,000 plants per hectare and nitrogen fertilizer amount of 300 kg and 100 kg nitrogen per hectare, respectively. With increasing the nitrogen fertilizer, stem diameter increased from 8.15 to 9.6 mm. And With increasing the planting density, stem diameter decreased from 11.02 to 6.61 mm. With increasing the nitrogen fertilizer, shear energy increased from 2.49 to 2.88 J. And with increasing the coefficient.

Mulberry is one of the fruits with high levels of useful sugar which one of ways to maintain the product is drying. Evaluation of the moisture content reduction is essential to find the appropriate drying time and conditions. In this study, mulberry drying was carried out using a hot air dryer in three temperature levels (50, 60 and 70 °C) and three air speed levels (1.5, 2 and 2.5 m/s). Results of comparing the measured moisture loss with the predicted values by mathematical models indicated that the Page model, due to the high correlation coefficient and low root mean square error can be used for describing and predicting mulberry drying kinetics. Results of modelling by neural network indicated that the leading model with training algorithm of Trainlm, overall structure of (4-25-1) and threshold functions of Logsig and Tansig had the highest performance (R2=0/9999 and MSE=0/000009) and consequently, it offered tbetter results as compared with other topologies.

Persian cumin (Bunium persicum) is one of the medicinal plants with high economic and expert value which its identification and classification is very important. Manual or visual inspection and classification of Persian cumin is very time-consuming and associated with errors. Therefore machine vision technology as a novel and non-destructive method can be a very good technique for identification and classification of the product. The aim of this study was to identify and to classify Persian cumin landraces based on color and texture features using image processing and artificial neural network. In this research, seven samples of Persian cumin landraces were collected from natural habitats of Kerman province and after image acquisition of samples, 36 color features and 108 textural features were extracted from the images. Identification of landraces was carried out using back propagation ANNs. Based on the results of the study, the mean classification accuracy using a one layer ANN, for the color, texture and color-texture features was equal to 93.55%, 93.50% and 96.40%, respectively. Also, the minimum value of mean square error for color, texture and color-texture features were obtained 0.172, 0.182 and 0.148, respectively.

Three varieties of apple namely Red Delicious, Golden Delicious and Granny Smith were purchased from a garden located in Urmia city. Then, 100 aples of medium size (120-180 g) were randomly selected. The samples were put in sterile nylon bags and kept into refrigerator (4 °C) for one week. Afterwards, the samples were irradiated with doses of zero (control level), 250, 500, 750 and 1000 Gy by the use of gamma irradiation (Co60). Therefore, the samples were stored for 0, 60, 120 and 180 days under two conditions: storage condition (15±2 °C temperature and 80% relative humidity) and refrigerator condition (3±1 °C temperature and 90% relative humidity). Every two months, the samples were checked in terms of total microbes and fungi (yeasts and molds) and compared with control (unirradiated) samples. Results of the investigations indicated that the proper conditions in terms of microbial control in order to maintain the quality of apple varieties in 6 months of storage is the dose rate of 750 Gy at 4 °C temperature.