احمد بناکار

Olive is a Mediterranean fruit that is cultivated mainly for its oil. Extra virgin olive oil has always been the attention and demand of users. Therefore, fraud in virgin and extra virgin olive oils is observed by adding valuable food oils and lower prices such as canola, sunflower, olive pomace, etc. Fraud detection with conventional methods such as gas chromatography is difficult, time-consuming, and requires sample and operator preparation. For this reason, the use of non-destructive technologies for fraud detection is important. In this research, detection of adulteration of pomace oil (olive pomace oil) was investigated using visible/near-infrared spectroscopy (Vis/NIR) technology. Three types of extra virgin olive oil, virgin and refined olive oil were used for sampling. Moreover, the samples were made in six categories of pure, 11, 20, 33, 50 and 100% fraud. Each treatment was prepared and tested in ten samples. Next, in order to analyze the qualitative features and classify the data extracted from the spectrometer, pattern recognition methods including linear discriminant analysis (LDA) and support vector machine (SVM) were used. The obtained results showed that visible/near infrared (Vis/NIR) spectroscopy is able to distinguish olive oil samples based on different percentages of pomace adulteration. Although the LDA method was able to classify olive oil samples with acceptable accuracy according to the adulteration rate, the SVM method had a better accuracy and fit with a training accuracy of 96.69% and a validation of 94.21%. According to the results, the linear function was suggested as the best function for building the classification models using the SVM method.

Nowadays, machine vision systems are extensively used in agriculture. The application of this technology in the field can help preserve agricultural resources while reducing manual labor and production costs. In the field of agricultural automation, accurately detecting crop rows is recognized as a crucial and challenging issue for weed identification and the automatic guidance of machines. Therefore, it is necessary to explore practical solutions to optimize this process. Hence, the purpose of this study is the precise identification of basil cultivation rows to enable the automatic navigation of robots in the cultivation field.

In the first stage of this research, six images from each growth period of basil plants (third, fourth, and fifth week) were taken and weeds were removed from the area between the crop rows using three different methods of area opening, dimensional removal, and masking. In the next stage, six images of crop rows without weeds were examined by performing image processing operations and implementing several routing algorithms, namely, Hough transform, wavelet transform, Gabor filter, linear regression, and an additional algorithm proposed in this study. The output of each of these algorithms was compared with the ideal path identified by the user. For this purpose, after capturing an image, green areas were extracted from it by performing the segmentation process. By applying each of the routing algorithms to the image, plant cultivation lines were identified and their equations were determined. Finally, the performance of the designed robot was evaluated using the most appropriate routing algorithm.

Examining the performance of three different methods of weed removal in three periods of plant growth (third, fourth, and fifth week) showed that during this interval, the masking method had the lowest error rate compared to the ideal path and the shortest average operation time of 1.64 seconds, followed by the dimensional removal and the area opening methods. Comparing the routes detected by different routing algorithms with the ideal routes and according to the results of the t-test at 5% probability level, the order of the studied routing methods from the most superior is as follows: the proposed algorithm, Gabor filter, linear regression, Hough transform and wavelet transform algorithm. Overall, the proposed algorithm had the highest rate of adaptation to the ideal path (with an average error of 3.65 pixels) and the shortest operation time (4.79 seconds) and was selected as the most appropriate routing algorithm and the performance of the designed robot was evaluated using it.

A reliable crop row detection algorithm can reduce production costs and preserve the environment. In this study, the masking method was used for removing weeds from the images. The new proposed routing algorithm has superior performance when compared with common routing algorithms such as the Gabor filter, linear regression, Hough transform, and wavelet transform. Additionally, it was shown that the designed robot using the proposed algorithm (with an average error of 3.65 pixels) has the desired performance.

Nowadays, due to the evaluation and high costs of maintenance and repair of sugarcane harvesting machines, it is necessary to monitor sugarcane harvester hydraulic oil using a faster and non-destructive method to determine contamination and TAN index. In this research, the ability of the visible spectroscopy method to non-destructively measure and predict the water content and TAN index in harvester Austoft 7000 hydraulic oil samples at different operating hours was investigated. For this purpose, spectra were taken from the samples in the spectral region of 400-780 nm. Multivariate Partial Least Squares (PLS) regression models were developed based on reference measurements and pre-processed spectra information by combining different pre-processing (Moving Average, Savitzky-Golay, Standard normal variate and First Derivative) methods to measure and to predict the water content and TAN index of hydraulic oil.  The results showed that the visible spectroscopy method could be used for quick and non-destructive measurement of water content and TAN index at different operating hours of harvester Austoft 7000 hydraulic oil. The best prediction results of water content in hydraulic oil were obtained with PLS model based on moving average (MA) preprocessing method (rcv=0.96, RMSECV=1.86, rp=0.89 and RMSEP=3.18), which had excellent accuracy (SDR=3.12). On the other hand, the PLS model based on the combination of moving average preprocessing and standard normal distribution (MA+SNV) was able to predict the TAN index with excellent accuracy (SDR=3.1) (rcv=0.94, RMSECV=0.007, rp=0.89 and RMSEP= 0.010). Therefore, the application of visible spectroscopy technology in agriculture and industries can be recommended for rapid monitoring of hydraulic oil quality and with the aim of controlling pollution.

In the poultry industry, reducing energy consumption is essential for reducing costs. Energy requirements in the poultry industry include heating, cooling, lighting, and power line energy. Identifying factors that increase energy usage is crucial, and providing appropriate solutions to reduce costs and energy consumption is inevitable. One of the major and expensive factors in the poultry industry is the use of fossil fuels, which also causes pollution. Energy costs directly impact the cost of production and increase the per capita cost of production in the meat and egg sectors. In Iran, poultry farms are among the most widely used energy consumers, especially for heating breeding halls, making them a significant subset of the agricultural sector.

The problem under study is the thermal simulation of a meat poultry farm located in Ardestan city, Isfahan province. Ardestan city is situated in a desert region in the north of Isfahan province, at a latitude of 33 degrees and 23 minutes north, and a longitude of 52 degrees and 22 minutes east. The dimensions of the poultry hall floor are 5 meters by 8 meters, and it has a capacity of 300 poultry pieces. There are two inlet air vents (windows), each with dimensions of 1.90 by 1.6 meters. The roof has an average height of 2.5 meters and is sloping, made from a combination of plastic carton, fiberglass, and sheet metal.To reduce energy consumption in this poultry farm, a solar heating system is designed and studied in this research. The farm is one of the functions of Isfahan province, with dimensions of 8 meters in length and 5 meters in width. The simulation is performed using TRNSYS software.

The results demonstrate that a collector surface area of 26 m2 is necessary to reach the technically optimal point, where the sun's maximum production is achieved with no energy dissipation. Furthermore, the findings indicate that a balance of 16 m2 is required to align the solar system with the auxiliary system.

By installing 2 square meters of solar collectors, 5.2% of the total energy demand can be met with solar energy. To fully meet the energy demand using solar energy, a collector area of 30 square meters is required. As the solar fraction increases, the system's ability to extract solar energy also increases. The maximum production of solar energy without any wastage is achievable with a collector area of 26 square meters. Moreover, to maintain a balance between the use of solar energy and the auxiliary system, a collector area of 16 square meters is needed.

Microbial contamination of spices and medicinal plants at various stages of the production leads to low quality and shorter shelf life. As a result, sterilization is required to decrease the microbial load. The purpose of the current study was qualitative comparison of the two methods induction heating technology (115, 135 and 155 °C for 45, 60 and 75 s) with gamma irradiation (5 and 10 kGy) for sterilization of cumin seeds. For this purpose, microbial load (total microbial count, mold and yeast and coliform), total color differences and essential oil content were studied as quality properties of cumin seeds. After examining the influence of various induction heating system treatments on the specified parameters, process optimization was carried out by using the response surface methodology. Optimal decontamination conditions were obtained with a combined treatment of 151 °C -46 s. In these conditions, total microbial count, mold and yeast, coliform, total color differences and the amount of essential oil were 3.06 CFU/g, 3.1 CFU/g, 2.28 CFU/g, 4.15 and 2.57%, respectively. The optimal points of the induction heating method were validated and compared with the gamma irradiation method. The results showed that gamma irradiation caused in the greatest decrease of microbial load (1.52 CFU/g) (10 kGy). The least amount total color differences were found at 5 kGy treatment with a value of 2.76. No significant effect on total color differences was identified between gamma irradiation (10 kGy) and induction heating system. The highest and lowest levels of essential oil were found in the induction heating treatment (151 °C -46 s) and gamma irradiation (5 kGy), with the values of 2.45 and 1.7 %, respectively. According to the results of scanning electron microscope, induction heating led to a change in the surface structure, pores and cracks in the seed surface compared to gamma irradiation.

Due to the reduction of fresh water resources, increasing population growth, increasing industrial activities and changing people's living standards, water consumption increases consequently and water crisis is rapidly growing around the world, especially in Iran. A variety of desalination systems have been developed and used with their common advantages and disadvantages. High energy consumption is a major problem in most of the desalination systems. Linking desalination systems with ultrasound technology is one of the novel technologies to improve the energy consumption of these systems. In this research, fluid-dynamic simulation of ultrasound desalination system was performed by ANSYSCFX software. The results of numerical simulations were validated with the results of experimental tests. Two types of fluids: hot air entering the system and a mixture of vapor and NaCl were defined as the system inputs in the numerical simulation. Investigating the solutes amount in system's output of the ultrasound desalination system was the aim of this research. It was shown that according the numerical results, the amount of these solutes in the output was approximately 2% of the total primary solutes in the water. It was also concluded that in order to increase the efficiency of the system, the hot air amount entering the system should be decreased. In order to validate the inlet and outlet temperature, fluid containing a mixture of vapor and NaCl was used. The difference in results of the numerical simulation and the experimental tests was just 15%, which indicates that the simulation results are trustful.

Determining the status of egg fertilization plays a major role in determining the quality of eggs and their products. In this regard, in order to achieve greater productivity and production, egg evaluation is considered necessary and important in terms of spermatogenesis. In this regard, spectroscopy was performed in the range of 0.01900 nm from 130 local egg samples in the direction of the main diameter for 3 days during the storage period. Spectrum data from spectrometers, in addition to sample information, include unwanted information and noise.  For this reason, in order to achieve accurate classification models, it is necessary to process spectral data before developing the appropriate model. In this regard, intelligent neural network classification was developed based on reference measurements and information of pre-processed spectra by combining different methods of smoothing, normalizing and increasing spectral separation power to determine the presence of sperm in the egg.  Classification results on day zero, first, second, warehousing with 72.3% accuracy, 73.1%, 75.5%, and detection, 86.31, 87.1%, 76% and sensitivity, respectively: 83 61%, 79.63% and 73.3% were obtained.

In This Study, According To The Regional Meteorological Station And Sandia Model, As Well As Through Meteorological Data On Virtual Test (Nasa), The Simulation And Design Of Solar Photovoltaic Power Plants In Five Bank Branches In The Provinces Of Tehran, Fars, Yazd And Qom. Ability To Meet All The Demand For Power From Photovoltaic Power Stations Where They Were Introduced As A Green Branch. The Results Showed: Branch Of Shahid Dastvare Is Ideal To Green Branch And Can Be Introduced In Tehran. In Other Locations, According To The Production Capacity Of Its Power Sector Energy Efficiency Can Be Part Of Requirements Through Supply Photovoltaic Power Plant. The Design Of Grid-Connected Power Plants To Branch Showed: Tehran Dastvare Branch Can Supply Its Electrical Power (151%) Independently Of The Electricity Distribution Network And Is Referred To As The Green Branch. Also, The Photovoltaic Power Plants Of Branches: Yazd, Amir Kabir Tehran, Sibouei Shiraz And Blv. Amin Qom Can Provide 26.27, 38.88, 41.94% And 21.72% Of The Energy Building's Needs. The Analysis Of The Curve Supplying The Requirements Of Branches Building Showed: Photovoltaic Power Plant That Use In Large Buildings Green Branches Not Optimal And It Is Better To Use The Parking Space, The Yard, Etc Which Do Not Require The Air Conditioner At The Same Time And Maintain The Plant Level.

In this research, based on meteorological data and also using the Sandia model standard in virtual meteorology, a photovoltaic power plant was designed and simulated in 5 company buildings in Tehran, Fars, Qom and Yazd provinces. Simulation and design data were validated with real power consumption data and green branch buildings were introduced. The results of technical design studies of grid-connected power plants (production-demand) showed: The Tehran branch office, independent of the national electricity grid, supplies electricity demand up to 151 percent. This branch was introduced as a green branch. Branch construction power plant: Central Yazd 26.37%, Amirkabir Tehran 38.80%, Sibouyeh Shiraz 41.90% and Amin Boulevard Qom 21.72% responded to the demand of electric power of branch buildings. Economic analysis showed the return on investment period in the power plant of Tehran Dastavareh branch to be minimum and equivalent to 5.16 years and Sibouyeh Shiraz to be maximum and equivalent to 8.51 years. The analysis of supplying the needs of the branch buildings through photovoltaic power plant proved: the use of large buildings in the green branches of banks is not optimal and it is better to use the parking space and yard, which do not need air conditioning system at the same time, to provide the case level. Use the power plant opinion. The highest radiation on the southern walls at the rate of 90 kWh per square meter was recorded in October and the lowest amount was recorded in June at the rate of 21 kWh per square meter. Buildings located in the latitude of Tehran in order to make the most of the area for energy production of photovoltaic power plants should be north and south wall should be used at the same time to provide the required level of photovoltaic power plant.

Greenhouses should provide a controlled environment to produce plants with sufficient sunlight, temperature, and humidity. Better growth conditions in the greenhouses are mainly achieved by maintaining the optimum indoor temperature compared to the outdoor temperature of the greenhouse. The objective of this study is design, construction and evaluation of a fuzzy control system for a research greenhouse with capability of measuring, control and monitoring of greenhouse environmental conditions. Greenhouses control variables are air temperature, ambient relative humidity, light intensity, CO2 levels which are monitored and controlled by operators (heating system, cooling system, carbon dioxide regulation system, ventilation and humidity regulation system). In this study, hydroponic greenhouse was first designed and constructed. Heating and cooling equipment as well as needed sensors were installed. Then, a fuzzy control system was designed. Finally, by considering information on the appropriate environmental conditions of the hydroponic greenhouse of cucumber, the values of temperature, humidity, light and carbon dioxide are considered as fuzzy system inputs and the rules of the fuzzy system were adjusted so that the environmental conditions of the greenhouse were also fuzzy defined according to the input data. The results of the system evaluation showed that fuzzy control can well control the greenhouse environmental conditions.

Immature birds, like humans and many animals, pass through the puberty period to sexual maturity that is accompanied by sound changes and after the sexual maturity, the sound structure evolves. The puberty period is one of the most important periods in the breeder chicken farms. Because the feeding of roosters at this age can delay or accelerate the time of sexual maturity. On the other hand, the diagnosis of mature roosters to mating with chickens increases egg production in early adulthood. Sexual maturity is a summary of the morphological and physiological changes its peak in the roosters from the age of 16 to 24 weeks. In female birds, the beginning of the first laying is considered to be sexual maturity, while the exact timing of sexual maturity in a male bird cannot be determined. The puberty term means the age at which reproduction is possible for the first time, but reproductive processes have not yet evolved. Therefore, the chance of pregnancy at this age is very low and fertility will not be optimal. Puberty can be likened to teenage years in humans. Bird sounds are generated mainly by the syrinx and humans speak with the stimulation of the vocal cords. The sound produced by the bird is similar to how human speech is produced. Therefore, techniques used to recognize human speech are also likely to be useful for classifying bird sounds.

Variation in an animal’s vocalizations can provide clues about how the animal uses sound, as well as qualities of the individual that is vocalizing. Bioacoustics research depends heavily on the ability to characterize these variations. The main goal of this study is to diagnosis puberty and the sexual maturity in bred roosters based on sound signals. To do this, the number of roosters with the first ejaculation for puberty and sperm concentration criterion for sexual maturity was divided into three groups of immature males, roosters during the puberty period and adult roosters and the rooster's acoustic signals were recorded by a microphone in a double-sided glass box (50x50x60 cm). The main purpose of using the box is to prevent the effects of noise in the environment on acoustic signals because otherwise, the sound signal of the rooster is unreliable due to the characteristics of the normal sound. Linear predictive coding (LPC) coefficients from the frequency domain were extracted as sound features. The sound features were used to classify k- nearest neighbors (K-NN) inputs for network training.

The results of accuracy, recall and precision values are, respectively, 97.7%, 98.3%, and 98.8% for the classification of roosters. Immature roosters had similar sound structures that with start the puberty and Leakage testosterone hormone, the rooster's syrinx, which is part of the secondary sexual feature, also begins to change. After sexual maturity, the syrinx has grown and this evolution also makes the sound structure of the mature rooster very similar. Therefore, according to the similarity of the sound of the mature rooster and immature one, as well as the syrinx continuous changes during the puberty period, the K-NN classifier with the LPC coefficients can show a high degree of accuracy in the classification of roosters. Because a feature of the k-NN algorithm is that it is sensitive to the data local structure.

The main objective of the present study is to detect sexual and puberty of roosters using acoustic signals. The LPC coefficients as K-NN classification inputs show accuracy, recall, and precision values of 98.7%, 98.3%, and 98.8%, respectively. These results indicate high accuracy of K-NN classification to identify and categorize immature roosters, rooster during puberty period, and mature roosters.

The cultivation of greenhouse crops is an advance in agricultural production technology that is increasing from temperate to warmer regions. However, greenhouse protects crops in a bad weather condition, but high temperature and humidity in the tropics have a converse effect on a production of crops. Therefore, in these regions, it is necessary to reduce the temperature inside the greenhouse. Since the design of the cooling system is highly dependent on environmental conditions, it is difficult to develop an appropriate system that provides local climate conditions for product growth. Therefore, in this research, the performance of fan-pad cooling system was studied in different climatic conditions. For this purpose, the greenhouse environment was first modeled using TRNSYS software. After verifying the model with experimental results, the system's ability to cool the greenhouse and system performance was evaluated relative to the parameters of air velocity, temperature and humidity ratio. The results showed that in ambient conditions with dry bubble temperature and humidity ratio less than 40˚C and 0.015 kg/kg, direct evaporative cooling system (fan-pad) at an air velocity of 1.25 m/s was a suitable method for cooling the greenhouse and provides suitable conditions for product growth. The cultivation of greenhouse crops is an advance in agricultural production technology that is increasing from temperate to warmer regions. However, greenhouse protects crops in a bad weather condition, but high temperature and humidity in the tropics have a converse effect on a production of crops. Therefore, in these regions, it is necessary to reduce the temperature inside the greenhouse. Since the design of the cooling system is highly dependent on environmental conditions, it is difficult to develop an appropriate system that provides local climate conditions for product growth. Therefore, in this research, the performance of fan-pad cooling system was studied in different climatic conditions. For this purpose, the greenhouse environment was first modeled using TRNSYS software. After verifying the model with experimental results, the system's ability to cool the greenhouse and system performance was evaluated relative to the parameters of air velocity, temperature and humidity ratio. The results showed that in ambient conditions with dry bubble temperature and humidity ratio less than 40˚C and 0.015 kg/kg, direct evaporative cooling system (fan-pad) at an air velocity of 1.25 m/s was a suitable method for cooling the greenhouse and provides suitable conditions for product growth. Also, the results showed that in the case of moisture content greater than 0.015 kg/kg, the evaporative cooling system alone could not cool the greenhouse.

This paper presents a new method for the design and operation of photovoltaic power plants connected to the network anywhere in the country. Using an appropriate module and inverter can power while increasing efficiency and ideal use of solar energy potential, prevent power dissipation and system cost in different parts of the Draft .also prevent the loss of power in the shadow of modules on each other or the other computing takes place. The main characteristics of the panel and inverter design that is fit and homogeneity. HelioScope Software due to weather conditions and the geometry of the selection panel, the best way to offer the appropriate inverter type of wiring. SketchUp Software suitable guideline for evaluating the shadows during the year. Acoustic station Mehrabad NASA weather data and virtual stations can be used with high accuracy. Plant Mehrabad weather station data can yield% annual 7/78 and 3/735 MW due to the virtual station NASA 8/79% yield equivalent to 7/724 MW power generation have. The highest temperature and the lowest power dissipation in the wiring and inverter are mismatched.

Packaging, distribution and hatchery of the cracked in eggshell are always big challenges in egg industries. Due to the high volume of the egg production in the world, sorting eggs with cracks in the shell is so important. In this study, cracks in sell eggs are detected by using their electrical conductivity. In this study, 203 Hy-line eggs, including 48 micro-cracks egg were used. Since there is no measurement technique for detecting eggshell crack, 100 crack eggs were selected, and have been examined by expertise. Among them, 48 eggs with micro-cracks were selected and tested. For this purpose, dielectric constant and loss factor of eggs were measured between 40 KHz to 20MHz. These coefficients are then used as input matrix of classifiers, which are artificial neural network and support vector machine. 50% of data allocated for testing included 75 intacts eggs and 26 micro-cracks eggs. Both of the classifiers, Neural network and support vector machine, were detected cracks in shell eggs with 100% accuracy and 1 Kappa statistic.

Separation of fertile and infertile eggs is a major concern of the poultry industry. If this separation is done at the early days of the incubation period, less space will be occupied in the incubator resulting in improved capacity and efficiency. Dielectric constant and loss factor were used in this study to detect infertile Hy-line eggs. In this study, 126 eggs were selected and their dielectric properties were measured on the third day of incubation. On the fifth day, it was detected that there were 112 fertile and 14 infertile eggs. Data were then analyzed by Weka software using artificial neural network (ANN) and support vector machine (SVM) classifiers. Results showed that, on the third day of incubation, SVM and ANN successfully detected infertile and fertile eggs with 100% accuracy. Detecting the fertile egg by using electrical conductivity is a non-destructive method and is also more accurate than image processing, signal processing and PCR methods.

In this study a photo-voltaic system of Flexible Solar Panels was designed, manufactured and evaluated in a scale of one square meter equivalent to 56 Watt. Conventional photo-voltaic systems are not suitable for curved surfaces. Accordingly, a flexible system was developed on a flat, cylindrical and hemisphere surface. As regards environmental variables, a temperature sensing system was designed to receive data online and communicate with LabVIEW Software. Economic analysis of the system using the COMFAR software is reported. Meteorological station data have been used as part of the required environmental data. Using Taguchi Experimental Design, the merit of alternative photo-voltaic surfaces is identified to be hemisphere, cylindrical and flat surfaces.The Taguchi Experimental Design Model indicated that the optimum System Power for flat, cylindrical and hemisphere surface is 54.05, 53.30 and 55.70W respectively. Economic analysis indicates that The Internal Return Rate(IRR) for flat, cylindrical and hemisphere surface was 23.82 and 26.70 and 27.46 percent respectively. The Net Present Value(NPV) was 6.747 And 9.952 and 10.830 million Rials for hemisphere, cylindrical and flat surfaces, respectively

In this research with utilization various neural networks models¡ the relationship between the amount of water production and the temperature of the vapor with different weather conditions¡ time of day and several water debit in desalination system equipped whit linear solar parabolic concentrator was investigated. The results showed that static and dynamic networks can be modeled the process of production fresh water with high accuracy. Static neural network can do the modelling process with higher speed than dynamic neural network. However it seems that the amount of error with using dynamic networks was reduced in process modeling. Coefficient of determination (R2) for training¡ validation and testing in static networks were 0.9898¡ 0.9899 and 0.9889¡ respectively. While coefficient of determination (R2) for training¡ validation and testing in dynamic networks were 0.9922¡ 0.9894 and 0.9901¡ respectively. Also the amount of mean square error (MSE) in static network for training¡ validation and testing was 0.0011¡ 0.0027 and 0.0024¡ respectively and for dynamic networks was 0.0018¡ 0.0007 and 0.0004¡ respectively. Comparison between dynamic and static networks show that the dynamic networks can be predicted the production of fresh water and vapor temperature according to changes in atmospheric parameters accurately than the static networks.

In this paper, a smart method is designed in order to classify healthy and illness ducks using their emission voice. For this purpose, firstly, the birds based on their healthy condition are divided into the different categories and then their voices are saved using a microphone and data acquisition card. Gained signals were transformed from time-domain signal to frequency domain using Fast Fourier Transform (FFT). Then, 5 statistical features are extracted from both time and frequency signals namely, mean, standard division, root mean square, variance and kurtosis. Two classifiers which are artificial neuralnetworks (ANN) and support vector machine (SVM) are used, in order to acquire the bird classification in healthy and sick accuracy. The accuracy of ANN classifier in detection of healthy birds within sick and weak birds was determined 75% and 82.1 % based on the time and frequency domain of the sound signals, respectively. The accuracy of SVM classifier in detection of healthy birds within sick and weak birds was determined 85.7 % and 92.8 % based on the time and frequency domain of the sound signals, respectively.

Energy consumption is very high in greenhouses mainly due to off-season production. The main source of its energy supply is fossil fuels. Due to the disadvantages of fossil fuels consumption and preventive laws in the incoming years, study on reducing consumption and replacing a part of them with renewable energy is essential. In this research, a solar heating system, equipped with a concentrator and Fresnel lens, adjustable to all regions in Iran, was designed and manufactured in Faculty of Agriculture, Tarbiat Modares University. Thermal heating requirement of a cucumber greenhouse was calculated and then solar heating system and thermal storage system were designed and manufactured. For more energy saving, besides insulation of the reservoir tank with an elastomeric layer, cupper tubes and paraffin was used. Then, behavior of the system was evaluated under three thermal conditions of 35, 50 and 65°C. The results of variations of water temperature in the tank showed that mean descending rate of temperature, with a higher initial temperature, has been faster in the tank as compared to the tank with lower initial temperature. But, with proper volume and insulation and use of phase-change materials, particularly in the melting point range of paraffin, temperature in the tank remains constant and the cooling process is slower. Therefore solar heating system and thermal tank with suitable volume and insulation can be used for heating the greenhouses.

In this research, an intelligent method is introduced for remaining useful life prediction of an internal combustion engine timing belt based on its vibrational signals. For this goal, an accelerated durability test for timing belt was designed and performed based on high temperature and high pre tension. Then, the durability test was began and vibration signals of timing belt were captures using a vibrational displacement meter laser device. Three feature functions, namely, Energy, Standard deviation and kurtosis were extracted from vibration signals of timing belt in healthy and faulty conditions and timing belt failure threshold was determined. The Artificial Neural Network (ANN) was used for prediction and monitoring vibrational behavior of timing belt. Finally, the ANN method based on Energy, Standard deviation and kurtosis features of vibration signals was predicted timing belt remaining useful life with accuracy of 98%, 98% and 97%, respectively. The correlation factor (R2) of vibration time series prediction by ANN and based on Energy, Standard deviation and kurtosis features of vibration signals were determined as 0.87, 0.91 and 87, respectively. Also, Root Mean Square Error (RMSE) of ANN based on Energy, Standard deviation and kurtosis features of vibration signals were calculated as 3.6%, 5.4% and 5.6%, respectively.

One of the best applications of automated navigation systems is the use of automatic steering systems in agricultural vehicles. In this research، a machine vision algorithm was designed، based on image acquisition from a camera mounted on front of acombine harvester cab fordetection of its forward path. After filtration and optimization of the image، Hough transform was utilized for extracting the path as a line. Then lateral and angular deviations from the path obtained as main inputs for the automatic guidance system of combine harvester. The algorithm was tested in 7 lateral and 7 angular deviation treatments with three replications. The tests were carried out under different conditions of day light. Comparison of the values extraction by the image processing algorithm with angles and deviations measured manually showed that the algorithm is able to detect the designed line with an accuracy of 98%.

In this research، an intelligent and automatic procedure is introduced for diagnosis of some common faults of timing belt bases on its vibration signals. For this goal، vibration signals were gained in different faulty conditions of timing belt. In data mining step، six features namely، standard deviation، kurtosis، skewness، impulse factor، shape factor and crest factor were extracted from vibration signals aim to better monitoring of signals behavior. After extracting the fault characteristics، Artificial Neural Network (ANN) classifier was used for intelligent detection of faulty belts. The ANN was trained with 60 percent of signals and was tested with other signals. The results show the faulty belts have a turbulence vibration and impulsive behavior. Also، ANN classifier detected and classified the timing belts faults with 90 percent accuracy. The results show which the use of vibration signals for small faults detection of timing belt was effective aim to prevent its complete rupture. The results demonstrate the combination of data mining and artificial intelligence techniques is a powerful and successful procedure for precision timing belt troubleshooting.

Damask rose with scientific name of Rosa damascena Mill. contains essential oils with large medicinal properties. Qualitative and quantitative extraction of essential oils as well as its economic justification depends on appropriate methods of drying. Appropriate method of drying reduces loss and damage during storage and helps maintain product quality. The purpose of this study was to predict Rose moisture content during the drying process with hot air flowing as a function of temperature at four levels (40، 50، 60 and 70°C) and air velocity at three levels (0. 5، 1 and 1. 5 m/s)، using artificial neural networks. The average initial and final moisture contents were calculated to be 78% and 9%، respectively. The drying process was modeled by mathematical models using matlab and then the moisture content graphs were achieved by excel. Then، the drying process was modeled using neural networks with three inputs including temperature، air velocity and time. Results showed that neural network was more accurate than mathematic models in modeling and predicting the drying process of damask rose and could be used in on-line controlling.

In this research drying kinetics، moisture diffusivity، determination of most appropriate mathematical modeling and activation energy of white mulberry were studied under microwave oven. For determination of most appropriate model، the highest value of R2 and the lowest values of c2and RMSE were used. For mathematical modeling، nine empirical models were fitted on experimental data and the best model was selected. The result of regression analysis showed that Midilli model has the best fitting with data. Moreover، the highest and lowest values of moisture diffusivity were obtained as 5. 3×10-6 and 1. 42×10-6 at power of 500 W and 200 W، respectively. Also the values of activation energy and moisture diffusivity in the drying of white mulberry were obtained as 10. 83 and 1E-5 respectively، based on an exponential relationship. Finally، the value of specific energy consumption for drying of white mulberry was found between 0. 7 to 2. 58.