محمدحسین آق خانی

On the field and in the paddy milling factory dryer losses have always been challenging issues in the rice industry. Different forms of losses in brown rice may occur depending on the field and factory conditions. To reduce the losses, proper management during pre-harvest, harvesting, and post-harvest operations is essential. In this study, different on-field drying and tempering methods were investigated to detect different forms of brown rice losses.

The present study was conducted on the most common Hashemi paddy variety during the 2019-2020 season in Talesh, Rezvanshahr, and Masal cities in the Guilan province, Iran with 0.2 hectares and 5 paddy milling factory dryers. On the fields, the method and date of tillage, irrigation, and transplanting used in all experimental units were the same. Moreover, the same amount of fertilizer and similar spraying methods were used across all experiments. For the pre-drying process on the fields, the following three pre-drying methods were applied on the harvest day: A1) The paddies were spread on the cut stems for insolating, A2) The paddies were stacked and stored after being placed on the cut stems for 5h, and A3) The paddies were covered with plastic wrap and stored after 5h of insolating. The first method (A1) is the most common in the area and was chosen as the control treatment. For the second step of the process, the time interval between the on-field pre-drying and threshing was considered: B1) 14 to 19h post-harvest; B2) 20 to 24h post-harvest, and B3) 25 to 29h post-harvest. Afterward, methods A1 to A3 were combined with methods B1 to B3 and feed into an axial flow-thresher at 10 kg min-1, 550 rpm PTO, and two levels of moisture content at 19 and 26 percent (% w.b). The third process was two-stage or three-stage tempering for 10 or 15 hours resulting in four levels (C1 to C4) and was done in the conventional batch type dryer under temperatures of 40 and 50 ˚C and airspeeds of 0.5 and 0.8 m s-1 in paddy milling factories. At the end of each process, a 100g sample was oven-dried for 48h and a microscope achromatic objective 40x was used to detect incomplete horizontal or vertical cracks, tortoise pattern cracks, and immature and chalky grains. The equilibrium moisture content was determined to be 7.3 percent. Losses properties were analyzed using a completely randomized factorial design with a randomized block followed by Tukey's HSD test at the 5% probability and comparisons among the three replications were made.

Results demonstrated that the stack and plastic drying methods significantly increased the percentage of losses. In the plastic drying method, the percentage of chalky grains and tortoise pattern cracks was higher than other forms of loss. In the first process, irrespective of the pre-drying method, the losses were reduced at a lower level of moisture content. At the end of the first stage, losses in the spreading method were significantly lower at 19% moisture content. Threshing the plastic-wrapped paddies after 14 to 19 hours at 19% moisture content resulted in the maximum threshing loss of 8.446% and over half of the grains were chalky or had tortoise pattern cracks. The threshing loss was halved (4.443%) for paddies threshed 25 to 29h after spreading at a moisture content of 26%. The mean of losses in the second step of the process were 7.229, 5.585, and 5.156% for the time interval between the on-field pre-drying and threshing of 14 to 19h, 20 to 24h, and 25 to 29h, respectively. In the last step of the process in paddy milling factory dryers, there was no significant difference in the minimum percent of losses between 10 and 15 hours of three-stage tempering at 40 °C and with 0.5 m s-1 airspeed. Furthermore, maximum total losses with the most incomplete horizontal and vertical cracks occurred in the two-stage 10h tempering at 50 °C and with 0.5 and 0.8 m s-1 airspeed.

Food security has always been a critical matter in developing countries. Furthermore, identifying the source of losses in the fields and the factories is one way to reduce losses and achieve food security. Stacking or wrapping the paddies in plastic after pre-drying on the fields for 5h is not recommended in terms of its effect on increasing the percentage of brown rice losses. Additionally, due to the importance of factory dryer scheduling in the management of the losses, it is recommended to use a three-stage 10h tempering at 40 °C and with 0.5 m s-1 airspeed.

Robots have been used for material handling for many years, and their applications have greatly expanded with the integration of intelligent technologies. While numerous researchers have proposed various robots for this field, it is crucial to design customized configurations that are suitable for agricultural farms. However, research in our country has been limited to a few mobile agricultural robots. The main focus of this paper is to design and model workspaces and analyze the kinematics of manipulators in agricultural settings.

This article investigates the workspace and kinematics of a robot manipulator to design and manufacture a four-DOF manipulator for farming. This manipulator will be capable of performing a variety of tasks, but the goal of this project is to enable it to load and unload materials and products on the farm as an auxiliary force for the farmer.
When designing and analyzing a manipulator, the first step is to determine the specific task that the robotic arm will perform. For example, consider a scenario where the task involves loading or unloading forage packages from a trailer at a designated location. This task specification forms the basis for further design and analysis, ensuring that the manipulator is appropriately designed to meet the requirements of the task.
An intelligent robotic arm that is attached to a tractor can perform this operation in the shortest possible time without the intervention of human workers. Otherwise, a large number of laborers would be required to move boxes weighing 10 kg over distances of 3 to 4 meters and heights of 1 to 2 meters, which would require a great deal of torque.
At this stage, the design of the arm kinematics model, direct kinematic equations, velocity kinematics, and Jacobian matrix solving were performed. The calculations were carried out using two methods manual calculation and kinematic modeling in MATLAB software for three arm configurations in two simulation tests. The results of both methods were compared.
The workspace analysis of the selected manipulator configurations, as well as the use of arm kinematic performance evaluation indices, were illustrated in graphs.

The issue of moving forage packages on the farm is described below. If a farmer were to move 48 packages of fodder weighing about 10 kg manually (using human workers) in the workspace modeled in Figure 10, each package would take an average of 30 seconds to be moved reciprocally along an unobstructed path. Hence, it would take approximately 24 minutes to move all the packages. However, the linear speed of the final operator of the robot arm during the first test was found to be 1 meter per second, which is 3.7 times faster than the manual work scenario, and the total movement of the packages can be completed in about 6.5 minutes.
Upon analyzing the velocity diagrams of the final performer in both tests, it becomes evident that there is not much variation in speed and acceleration due to the change in configurations. The evaluation of robot workspace indicators was conducted using two methods workspace index and structural length index. These indicators were calculated for all three configurations, and the results indicated that Configuration Type 1 was the most suitable option. Furthermore, the manipulability index of the robot arm was assessed based on the obtained diagrams for all three configurations in the two tests. It was observed that Configuration Type 1 outperformed the other two types in terms of score, indicating its superior performance. This aligns with the suggestion made by Yoshigawa for the first three joints of the Puma robot.
Overall, the results suggest that Configuration Type 1 is one of the most favorable options, ensuring better performance for the final performer.

One of the main considerations when using robots in agriculture is the appropriate kinematic design of joints and links for work operations. Using the example of robots assisting with moving products on the ground, it can be seen that using robots significantly reduces the time required compared to manual labor. Furthermore, in terms of energy consumption and cost within a certain period, the use of robots has economic justification.
Based on the studies conducted, Configuration Type 1 passed the kinematic path in both tests with a higher manipulability index and a more suitable workspace index based on both calculated criteria. Therefore, this configuration is recommended for the design of robots for the operation of moving products on the ground.

Manual harvesting bell peppers as a greenhouse product is time-consuming and has high labor costs. Various two-dimensional machine vision systems have been developed for the robotic harvest of this product. Due to the complex environment of the greenhouse, high density of branches and leaves, the presence of pests and diseases and sunburn, non-uniform and variable light conditions, and asynchronous maturation of the crop lead to non-uniformity in color. Therefore, harvesting colored sweet peppers are also associated with challenges in addition to green sweet peppers. The aim of this study is to recognize sweet peppers in the point cloud model based on the Fast Point Features Histogram (FPFH) descriptor, and machine learning. Kinect V2 sensor was used to record depth images. After creating a 3-D model, geometric features (FPFH) were used for classification, and H color channel was used to create supervision in 9 classification models on 15 3-D color peppers models supervised. According to the F1-Score criteria, the area under the roc curve, and the accuracy of the algorithm, the K-nearest neighbor classifier model was selected as the best model. To evaluate the KNN algorithm, 15 3D models of red, orange and yellow pepper were performed and finally, they were counted manually. The results showed that the value of F1-Score was higher than 0.7 in all cases, except for one case, the area under the roc curve was higher than 0.8 in all cases and the accuracy of the classification algorithm was higher than 0.9 in all cases. In addition, the human evaluation results showed that the minimum recognition accuracy is 71.42%, and the maximum is 100%.

Tillers (2WTs) are among the types of small tractors that are usually used in greenhouses, orchards, and  paddy fields; they are most useful  in places where it is difficult to move tractors. The existing tillers have only one clutch for power transmission to the gearbox, and they do not have a clutch to guide and steer. For this reason, they have many problems for turning and steering, especially under the trees and changing the movement direction during work. Therefore, in this research, an attempt was made to solve this problem. Two types of plate (disc) and cone friction clutches, which are the most used in agricultural machines, were thought about.The torque generated on the rotors of a 9-hp tiller was calculated while the tiller was running at gear ratio one. The applied forces from the operator (driver) to the clutch plate (disc) for engaging the clutch was calculated. Having the amount of transmission force, torque and available space to the design of the cone clutch, which can be used on the rotary axles of the tiller, was performed using 3D design software (CATIA). To ensure the resistance of parts against failure and their correct operation, static analysis of selected clutch parts that are exposed to maximum stress was performed in ANSYS software. The results indicated that the clutch parts had appropriate strength and hence, would operate safely. Therefore, it can be concluded that it is possible to use the cone clutch on such tillers.Two types of plate (disc) and cone friction clutches, which are the most used in agricultural machines, were considered.The torque generated on the rotors of a 9-hp tiller was calculated while the tiller was running at gear ratio one. The applied forces from the user (driver) to the clutch plate (disc) for engaging the clutch was calculated. Having the amount of transmission force, torque and available space to the design of the cone clutch, which can be used on the rotary axles of the tiller, was performed using 3D design software (CATIA). To ensure the resistance of parts against failure and their correct operation, static analysis of selected clutch parts that are exposed to maximum stress was performed in ANSYS software. The results indicates that the clutch parts have appropriate strength and hence, would operate safely. Therefore, it can be concluded that it is possible to use the cone clutch on such tillers.

Conversion of tree and grass pruning wastes, as abundant and available lignocellulosic sources, into biofuels can be a viable alternative to fossil fuels that pollute the environment. In the present study, flammable briquettes were produced from the pruning remnants of urban trees including mulberry, elm, acacia and ash, as well as grass using a natural binder i.e. frankincense. Then, the optimal density and friction of the produced briquettes under the influence of pressure, moisture, temperature, mixing percentage and additive parameters were investigated using regression methods, support vector machine (SVM) and genetic algorithm. The results showed that with increasing moisture between 9 to 17%, the density of briquettes decreases. Also, the examination of mixing percentage showed that by decreasing the percentage of sawdust and increasing the percentage of grass, density and friction force decrease. The most optimal sample of this experiment was determined with a mixing percentage of 87.5% sawdust and frankincense 10% at a temperature of 100 °C, a pressure of 10 bar and a moisture content of 13%. In this case, the average density and friction force of the briquettes were 1020 kg/m3 and 44 N/mm respectively. The results showed that regression model of the density and friction test related variables are significant at 5% level. Hence, the variables are involved in the explanation of friction and density. According to the results, the high calorific value and low calorific value of briquettes produced from grass and sawdust are more, and the calorific value of additives has a significant impact on the process of briquette production due to the high sticking effect.

Pistachio production has been adversely affected by Psylla, which is a devastating insect. The primary goal of this study was to select sensitive spectral bands to distinguish pistachio leaves infected by Psylla from healthy leaves. Diagnosis of psylla disease before the onset of visual cues is crucial for making decisions about topical garden management. Since it is not possible to diagnose psylla disease even after the onset of symptoms with the help of color images by drones, hyperspectral and multispectral sensors are needed. The main purpose of this study was to extract spectral bands suitable for distinguishing healthy leaves from psylla leaves. For this purpose, in this paper, a new method for selecting sensitive spectral properties from hyperspectral data with the high spectral resolution is presented. The intelligent selection of sensitive bands is a convenient way to build multispectral sensors for a specific application (in this article, the diagnosis of psylla leaves). Knowledge of disease-sensitive wavelengths can also help researchers analyze multispectral and hyperspectral aerial images captured by satellites or drones.

A total number of 160 healthy and diseased leaves were scanned in 64 spectral bands between 400-1100 nm with 10 nm spectral resolution. A random forest algorithm was used to identify the importance of features in classifying the dataset into diseased and healthy leaves. After computing the importance of the features, a clustering algorithm was developed to cluster the most important features into six clusters such that the center of clusters was 50 nm apart. To transfer the hyperspectral dataset into a multispectral dataset, the reflectance was averaged in spectral bands within ±15 nm of each cluster center and achieved six broad multispectral bands. Afterwards a support vector machine algorithm was utilized to classify the diseased and healthy leaves using both hyperspectral and multispectral datasets.

The center of clusters were 468 nm, 598 nm, 710 nm, 791 nm, 858 nm, and 1023 nm, which were calculated by taking the average of all the members assigned to the individual clusters. These are the most informative spectral bands to distinguish the pistachio leaves infected by Psylla from the healthy leaves. The F1-score was 90.91 when the hyperspectral dataset (all bands) was used, while the F1-score was 88.69 for the multispectral dataset. The subtle difference between the F1-scores indicates that the proposed pipeline in this study was able to select appropriately the sensitive bands while retaining all relevant information.

The importance of spectral bands in the visible and near-infrared region (between 400 and 1100 nm) was obtained to identify pistachio tree leaves infected with psylla disease. Based on the importance of spectral properties and using a clustering algorithm, six wavelengths were obtained as the best wavelengths for classifying healthy and diseased pistachio leaves. Then, by averaging the wavelengths at a distance of 15 nm from these six centers, the hyperspectral data (64 bands) became multispectral (6 bands). Since the correlation between the wavelengths in the near-infrared region was very high (more than 95%), out of the three selected wavelengths in the near-infrared region (710, 791, and 1023), only the 710-nm wavelength, which was closer to the visible region, was selected. The results of classification of infected and diseased leaves using hyperspectral and multispectral data showed that the degree of classification accuracy decreases by about 2% and if only 4 bands are used, the degree of accuracy decreases by about 3%.The results of this study revealed that the proposed framework could be used for selecting the most informative spectral bands and accordingly develop custom-designed multispectral sensors for disease detection in pistachio. In addition, we could reduce the dimensionality of the hyperspectral datasets and avoid the issues related to the curse of dimensionalitylity.

Nowadays, due to the competitive market, quality evaluation criteria at different stages of production are very important. One of these criteria is reliability, which is calculated using statistical evaluations of after-sales service data in most cases or with the help of accelerated tests at the early stage of the product development life cycle. In this research study, various accelerated test scenarios in proportion to the stresses on the diesel engine have been conducted to evaluate the lifetime of the connecting rod parts. The considered criterion for the reliability analysis is the time between failures which is evaluated after collecting and classifying the data using accelerated tests and confirming independent and uniform distribution of data via MIL-Hdbk, Laplace’s, and Anderson-Darling functions. Finally, the distribution diagram of reliability at different operating times was drawn using log-logistic statistical distribution, according to the best agreement with the experimental results. The results depicted that the reliability of the connecting rod would be equal to (0.99 @ 177417 hrs.) and (0.95@ 212753 hrs.) kilometers of cumulative operation, respectively, and after 448240 kilometers it would be less than 0.05.

In urban environments, root growth is limited because of limitation of soil and some problems the plants are faced with, for example soil compaction. The aim of this study was to build and evaluate a device that could improve the existing deficiencies for tree roots by increasing soil porosity and permeability through air injection. After construction of the machine, weighing 240 kg, equipped with a 7.5 HP engine and maximum injection pressure of 12 bar, the effects of machine operation and some important physical properties of soil such as porosity and infiltration were investigated. Multiple linear regression was used to investigate the relationship between the independent variables of the device including air pressure at 3 levels (4, 8 and 12 bars) and depth at 2 levels (30 and 60 cm) and the dependent variable of porosity. The results showed a significant regression model among the variables. Also, the regression model estimation error was 1.75%. Based on the results, the system can be reliably designed and manufactured to inject air into the soil to increase porosity.

Today, maximizing the efficiency of fuels and increasing the output power of diesel engines is considered inevitable due to the increasing need for energy resources, the reduction of fossil fuel resources, the need to maintain the environment, reduce air pollution, and limit the electricity supply and fuel supply. In the large cities of Iran, the problem of vehicle pollution is one of the main problems. The lack of proper fuel, soot filters, and absence of requirement for a technical inspection of diesel vehicles have led to an increase in mortality and the growth of lung cancer due to pollution. All of studies indicate that fossil fuels, despite the low cost of production, will increase the cost of both living and environment. A solution for this crisis is to reduce the sources of pollutant-producing sources from the source of these pollutants. In the internal combustion engines, the compression ratio and alternative fuels are two important factors affecting engine performance and exhaust emission.

In this research, a one-dimensional computational fluid dynamics solution with GT-Power software was used to simulate a six-cylinder diesel engine to study the performance and exhaust emissions with different compression ratios and alternative fuels. The compression ratio was chosen to be 15:1 to 19:1 with an interval at unity. Alternative fuels such as (as base diesel), methanol, ethanol, diesel and ethanol, biodiesel and decane were selected. To modeling engine, first, all parts of the engine were introduced as a real six-cylinder engine, and then the required data were entered according to the actual engine conditions at the atmospheric pressure of one atmosphere. Before this investigation was carried out, a validation model for evaluation was done by experimental and simulation data. The validation results showed that software model error is acceptable and the model has a good capability of fitting and predicting.

The engine performance was evaluated in terms of engine power, engine torque, and specific fuel consumption at different engine compression ratio and fuel. The results showed that with increasing the compression ratio, brake power and brake torque increased. Among the fuels used in this engine, the maximum brake power and brake torque in the compression ratio of 19 with the decane fuel were 3.86% higher than that the base fuel and the lowest value was awarded in the compression ratio of 15, with methanol fuel and it was equal with 56.04%. The results indicated that by increasing compression ratio, the brake specific fuel consumption was reduced due to more power than the fuel consumed in the engine. A fuel with lower heating value should be injected more mass to the engine. This will increase the brake specific fuel consumption. In this research, the decane fuel with a compression ratio of 19 with a reduction of 3.72% had the lowest brake specific fuel consumption among other fuels. The CO emission from the engine largely depended on the fuel's properties, the availability of oxygen, the fuel mix with air, temperature, and turbulence inside the combustion chamber. The results highlighted that by increasing compression ratio, CO emission increased and CO emission in biodiesel fuel, with a compression ratio of 15, was decreased by 82.37% compared to the base. CO2 emissions are not too harmful to humans, but they increase the potential for ozone depletion and global warming. With increasing compression ratio, CO2 and HC emissions increased for all fuels, CO2 emissions have risen up the base. The fuel heating mechanism, combustion temperature, oxygen content, and gas fuel availability are the most important factors in the formation of NOx. With increasing the compression ratio, the amount of NOX increases, which is due to the high temperature in the cylinder at a higher compression ratio. The viscosity and density of fuels have an effect on NOX emission, and because of the larger droplets of the fuel, it released NOX. The highest NOx emissions from biodiesel fuel are due to the high oxygen content of this fuel and the lowest NOx emissions from decane fuel, due to the low density of the fuel compared to other fuels.

The results of this study showed that the decane fuel with a compression ratio of 19 in total had the best functional and pollutant characteristics among the six fuel used in this study. Therefore, this fuel can be the best alternative for diesel fuel.

Nowadays, energy efficient systems are needed for concentrated juice production. Ohmic heating is one of these systems. In this study, the effect of ohmic heating technique on parameters such as: types of electrode (stainless steel, aluminum and brass), voltage gradient (10, 14, 18 and 22 V/cm), electrical conductivity, heating rate, pH and energy consumption of grape juice samples in the temperature range of 26-90℃ was investigated. The results showed that the effect of voltage gradient was statistically significant on the electrical conductivity, ohmic heating time, heating rate, pH, energy consumption and system performance coefficient (SPC) (p < 0.05). The best efficiency (85.09%) was observed for stainless steel electrodes and 22V/cm voltage gradient. By increasing the voltage gradient in all three electrodes (stainless steel, aluminum and brass), the heating time and energy consumption decreased, but the performance of the system increased. As the voltage gradient increased, time, pH and specific energy consumption decreased. The increase in concentration of grape juice was significantly increased electrical conductivity. The effect of electrodes on the heating rate of grape juice concentrated was not statistically different.

The eggshell of birds, as a natural shield and package, protects the tissues inside it from microbial and mechanical damages. Proper intake of calcium, as an important and effective factor in increasing the strength and quality of the eggshell, could reduce complications. In this paper, the effect of dietary calcium at five different levels on engineering features of Japanese quail eggs in a in their first laying period was investigated. The values for an average of mass, volume, specific mass, shell thickness, major diameter, central diameter and rupture force along the longitudinal and transverse axes were measured. Rupture energy or toughness, slope of the rupture curve (hardness), deformation along the longitudinal and transverse axis to the point of rupture as well as longitudinal and transverse deformation of 450 tested quail eggs (3 period of time, 5 treatment of calcium, 5 replication, 6 observation) were measured. The characters of the specific mass, shell thickness, rupture force, and slope of the rupture curve of quail eggs indicate the strength of quail egg. In this study, variations in all parameters indicating shell strength at different levels of dietary calcium were consistent with each other. Five different treatments with 1.5%, 2%, 2.5%, 3%, and 3.5% calcium content were supplied for the study. By increasing the calcium content of the quail diet from 1.5 to 3 wt%, the volume and weight of quail eggs dropped and shell thickness was reinforced. According to the results, the shell strength of quail eggs along the transverse axis was slightly less than the longitudinal axis, but the flexibility and energy required for quail egg rupture were much greater across the longitudinal axis.

In this study, a software model was developed for the spreader unit of a centrifugal fertilizer and its spread performance was studied with different blade designs to recommend the best suitable blade for spreading of the pellet fertilizer. The blade designs include trapezoid, angled trapezoid, channel (U shape) and angle (L shape) that were compared with the control blade. In order to validate the software's results, a laboratory spreader device was fabricated based on a commercial centrifugal fertilizer machine. The spread pattern of the designed blades was investigated experimentally in a completely randomized design with three replications. The influence of blade design as the dependent variable, on Total Transverse Spreading Pattern (TTSP) and Coefficient of Variation (CV) as the dependent variables, was investigated to achieve the best design for blade in the experimental mode. Results showed that the trapezoid blade, among other blades, had the best spread performance with in the effective swath width during pellet spreading (CV=0.18). Other blades were ranked as angle blade (CV=0.29), channel blade (CV=0.38), angled trapezoid blade (CV= 0.57) and control blade (CV= 0.64) respectively. This ranking observed for both numerical software and the experimental results. Investigation on adaptation and compatibility between the software model and experimental results showed that, for all blades designs, there were acceptable agreements between software model and experimental (R2 ≥ 0.90).

Counting the number of objects using image processing is very important in many aspects from medical science and biology to agronomy, genetic study and plant breeding. Although many studies were implemented for detecting the objects in image processing but the counting objects is easier and faster than recognition, because in some cases, an object can be counted and not be detected. In this study, a new algorithm is presented to count the granular objects without detection and recognition. This method can work correctly in despite of some noise in image. Presented method is based on distance-transform and because of this, the results almost are independent on size and depend on shape of objects and in other hand this method is relatively simple and fast. The other strength of this method is to use from detected edges. Not only perfect edge is not needed but also even one pixel from edge in some cases can be effective for counting correctly. The sharper edges are more helpful in this method. This is a good combination of detecting edge and using shape and size to count correctly. The results showed that in all of conditions, ideal or natural, the proposed method was better than the other investigated methods. Some of investigated methods were implemented only in special condition or special shape of objects but results showed that our method is capable in all of the investigated conditions. Disadvantage of this method is that it does not work on star shape objects unless changes to convex shape with morphological transforms. In spite of the good results, sharp edges and a good binary image of objects guarantee an acceptable counting result. The presented segmented image as graphical output is a valuable instrument for object recognition in machine vision systems. This method is recommended for counting plant seeds, especially convex seeds.

Packaging is one of the effective ways to increase the storage life and quality of the food products. Nowadays, most of the materials used in packaging are fossil origin and usually non-degradable and hardly dissoluble. Also biodegradable films, due to their fragility and poor resistant to gas exchange are in limited use. It is possible that by employing nanotechnology, some particles on nano scale may be added to these polymer composites to improve the mechanical properties and permeability of the biodegradable packing films. Silver nanoparticles and zinc oxide have been incorporated in polymers individually by researchers. The objective of this study is to compare the effect of incorporating the mixture of silver and zinc oxide nanoparticles with the case of adding them separately into poly vinyl alcohol matrix, on some relevant mechanical and physical properties of the outcome Nono-composite films.
 

To make polymer films polyvinyl alcohol, solvent (deionized water) and glycerol (as softener) were used. Then, zinc oxide and silver nanoparticles at 3% by weight, were added to the polymer solution in two different ways, separately and in combination. To specify the pattern of nano-particles size distribution, transmission electron microscope (TEM) test was performed. To determine the characteristics of the films’ surface scanning electron microscope (SEM) was employed. To investigate the bondings between the components of nono-composite films, FTIR was employed.For identification of matrix structure and formation of nano-composite, the XRD was performed. To measure the infiltration of water vapor, the approved E96 ASTM method was used. Also, for measuring the color and transparency of films, the HunterLab test and for mechanical properties of the films, Instron Universal Testing Machine (H5 KS, England) were used (considering the ASTM standard for tensile tests - D88201). For statistical analysis and comparison of means, variance analysis and Dunkan test were performed, using SPSS software.
 
Results &

SEM, XRD and FTIR tests showed that nanoparticles were distributed uniformly within the polymer matrix, and react well with the polymer chains. Besides, the effect of adding silver and zinc oxide nanoparticles on the relevant properties of the films was significant. By individual adding of these nanoparticles on polyvinyl alcohol matrix, the tensile strength and the elongation of films increased. On the other hand, their transparency and water vapor permeability decreased. The results also showed that the combined incorporation of silver and zinc oxide nanoparticles into the packing films can significantly affect their mechanical properties and permeability. Hence, due to the high prices of silver nanoparticles than zinc oxide nanoparticle, the combined incorporation of these two nano-particles is recommended, while maintaining the properties of the nano-composite films in a reasonable level. It can be implied that the combined use of silver and zinc oxide nanoparticles in the polymer provides a more affordable Nano-film with good enough quality. It may also reduce their mutual side effects.

In recent years much attention has been paid by researchers to the quinoa due to diverse functional properties, high nutritional value and gluten-free feature rather than other protein grains. The quinoa protein can be used as a nutritional supplement and a valuable food source for babies, children and adults in the formulation of sauces, sausages, soups, etc. The aim of this study has been comparing the effects of both alkaline extraction and ultrasound methods on the physicochemical and functional properties of quinoa protein isolate. Physicochemical properties includes Moisture content, fat, protein, carbohydrate, ash, weighted efficiency and color (L*,a*,b*), and functional properties including water and oil absorption capacity, foam production capacity and its stability, emulsifying capacity and its stability were examined . The results of the experiments showed that ultrasound assissted extraction without any effect on protein amount created higher extraction efficiency. It also showed the higher water absorption capacity (2/76 ml/g) and fat absorption capacity (4/63 ml/g) than the alkaline method. The alkaline method had better functions in foamability (foam production capacity 10/08% and its stability 38/88%) and emulsification properties (emulsifying capacity 17/57% and its stability 62/04%). The results have been showed that produced protein isolates have good functional properties and can be a substitute for other protein sources in different food formulations.Keywords: foam, emulsifying capacity, color properties, extraction efficiency, gluten free

The purpose of this study is to estimate the bio-oil from the pyrolysis process of waste products in terms of moisture, constant carbon, volatile matter and ash. The results of 41 different studies were used to modeling. We use the neural network model as a policy tool in the evaluation and prediction bio-oil from the pyrolysis process of waste products. Statistical method was used to determine the optimal values of the neural network parameters. The results of comparisons between two Multi- Layer Perceptron (MLP) and Radial Basis Function (RBF) neural networks showed that the RBF has a high ability to estimate the bio-oil. The value of correlation coefficient between experimental and predicted bio-oil by RBF was equal to 0.99. The neural network evaluation results showed that it can be used as a tool to estimate the production of bio-oil and it has been used in bio-oil production management decisions.

Ventilation rate of the poultries can be calculated by directly and indirectly method. This research was carried out in poultry with population of 18,000 broiler chicks in Sabzavar to compare these two methods. The output air velocity was measured in each of the fans at 24 points of the fan surface to measure the ventilation rate directly. The average speed at the effective surface of the fans was also calculated. The ventilation rate in the CO2 equilibrium method was obtained by calculating the CO2 production rate of chicks and measuring the CO2 concentration at the air inlets and outlets of the poultry, using the equations of gas balance in control volume. The results showed that the difference in two methods of measurement in different conditions of the salon was less than 22% (12500 m3/h) and with increasing ventilation rate, the difference between the two methods decreased to 2%.

Presently, the loss of ground water levels and the increase in dissolved salts have given importance to the determination of salinity and the management of their variations in irrigated farms. Soil electrical conductivity is an indirect method to measure soil salts. The direct electrode contact method (Wenner method) is one of the widely used methods to rapidly measure soil ECa in farms. However, soil scientists prefer soil actual electrical conductivity (saturated extract electrical conductivity) (ECe) as an indicator of soil salinity, though its measurement is only possible in the laboratory. The aim of this study was to find a relationship between the prediction of soil actual electrical conductivity (ECe) in terms of temperature, moisture, bulk density and apparent electrical conductivity of soil (ECa). Thereby, the estimation of ECe would allow the partial calculation of ECa that is dependent upon soil salinity and dissolved salts. This study used RBF neural network in Box-Behnken statistical design to explore the impacts of effective parameters on direct contact method in the measurement of soil ECa and provided a model to estimate ECe from ECa, temperature, moisture content and bulk density. In this study soil apparent electrical conductivity (ECa) was measured by direct contact (Wenner) method. The present study considered four most effective factors: ECa (saturated paste extract EC), moisture, bulk density, and temperature (Baradaran Motie et al., 2010). Given the characteristics of farming soils in Khorasan Razavi Province (Iran), the maximum and minimum of each independent variable were assumed as 0.5-6 mS.cm-1 for ECe, 5-25% for moisture content, 1-1.8 g.cm-3 for bulk density, and 2-37°C for soil temperature. Considering the experimental design, three moisture levels (5, 15 and 25%), three salinity levels (0.5, 3.25 and 6 mS.cm-1), three temperature levels (2, 19 and 37°C) and three compaction levels with bulk densities of 1, 1.4 and 1.8 g.cm-3 were assumed in 27 trials with predetermined arrangement on the basis of Box-Behnken technique. 13 common algorithms were explored in MATLAB software package for the training of the artificial neural network in order to find the optimum algorithm (Table 4). The input layer of the network designed by integrating a Randomized Complete Block Design (RCBD) with k-fold cross-validation. Using k-fold cross-validation, 20 different datasets were generated for training and validation of RBF neural network. A combination of an RCBD and k-fold cross-validation was used. The results of both training and validation phases should be considered in the selection of training algorithm. In addition, R2 of T1 training algorithm had a much lower standard deviation than other training algorithms. The lower standard deviation is, the more capable the algorithm would be in learning from different datasets. Considering all aspects, trainbr (T2) training algorithm was found to have the best performance among all 13 training algorithms of the neural network. Table 7 tabulates the results of means comparison for R2 of RBF model for both training and validation phases resulted from the application of some combinations of S and L2 factors as interaction. As can be observed, R2 = 0.99 for all of them with no significant difference. However, the magnitude of order differed between training and validation phases. Given the importance of the training phase, L2=9 and S=0.1 were regarded as the optimum values.
The sensitivity analysis of the network revealed that soil ECa, moisture, bulk density, and temperature had the highest to lowest impact on the estimation of soil ECe, respectively. This model can improve the precision of soil ECa measurement systems in the estimation and preparation of soil salinity maps. Furthermore, this model can save in time of data analysing and soil EC mapping because it does not need data recollection for the calibration of systems. A validation prose was done with a 60 field collected data set. The results of validation show R2=0.986 between predicted and measured ECa. The present research focused on improving the precision of soil ECe measurement on the basis of easily accessible parameters (ECa, temperature, moisture, and bulk density). In conventional methods of soil EC mapping, the systems only measure soil ECa and then calibrate it to ECe by collecting some samples and using statistical methods. In this study, Soil ECe was estimated with R2 = 0.99 by a multivariate artificial neural network model with the inputs, including ECa, temperature, moisture, and bulk density of soil without any need to collect further soil samples and calibration process. The Bayesian training algorithm was introduced as the best training algorithm for this neural network. Thereby, soil EC variation maps can be prepared with higher precision to estimate the spatial spread of salinity in farms. Also, the results imply that soil ECa, moisture, bulk density and temperature have the highest to lowest effectiveness on the estimation of soil ECe, respectively.

This research aimed to investigate the greenhouse gas emissions (GHG), energy consumption and production costs of dominant citrus produced in Mazandaran province of Iran. The relationship between input and output energies was investigated using the Cobb-Douglas model and sensitivity analysis. The results showed that two inputs including chemical fertilizer and diesel fuel with a share of 48.50% and 16.90% were widely consumed in the production of energy resources. Two inputs of animal manure and diesel fuel with 43.30% and 18.50% had the most greenhouse gas emissions in citrus production. The results of the sensitivity analysis showed that increasing 1 MJ in energy inputs, the machinery and fuel inputs had a positive effective on citrus yield, respectively. The results of economic analysis revealed that three inputs of human labor, chemicals and chemical fertilizers were allocated the highest of variable costs as 39.32%, 28.51% and 22.89%, respectively. In order to improve the sustainablility of citrus production, some practical solutions are suggested.

One of the important soil properties, which is related to the soil-plant relationship, is soil compaction. The aim of this study is to investigate the spatial variations of soil compaction using different Geostatistic methods, affected by conservation tillage. To evaluating the Geostatistic, soil samples were taken for regular grid of 10 × 10 meters in 117 points. The area of study was a 1.2 hectare land in Agricultural Research Center located in the vicinity of Mashhad in Khorasan Razavi province, the soil cone index was measured at the depth of 0-35 cm. After Geostatistic analysis and semivariogram for estimating variables using the kriging methods (EBK) and Deterministic interpolation methods in GS + and Arc (GIS). Criteria for evaluation in this research are the values of root mean square error (RMSE), mean error (ME), using the cross-validation method. The results showed that the empirical Bayesian kriging methods (EBK) and Ordinary Kriging (OK) are the best model to estimate soil compaction region.

Iran as one of the largest producers of poultry in Asia and plays major role in feeding the world's population, particularly in the poultry industry. Research about this industry will help to improve the quality and the quantity of products. Increasing of the concentration of toxic gases such as NH3 (ammonia), CO2 (carbon dioxide), SH2 and CH4 in poultry houses comes from bird activity inside the barn is one of the basic problems of the farming. Increasing the amount of these gases more than standard level would cause heavy mortality and reductions in the production. Ammonia is one of the most toxic gases in poultry houses, which must be controlled. Different studies have been carried out on measurement of ammonia emissions from poultry houses to reduce energy consumption and reduce emissions of ammonia. But no specific study has been found on ammonia emissions in Iran and there is no reliable documents of ammonia emissions from poultry in this country.
In this study a poultry house with 18 thousand chickens was used to measure the emission rate of ammonia, the effect of temperature, moisture and age of chickens on emissions of ammonia in Sabzevar city. The barn was equipped with semi-automatic mechanical ventilation. At the first step of this research all sensors was installed for data collection, i.e., air velocity, temperature, humidity and ammonia concentration. Recorded data information were stored in a central computer. Five digital sensors, model AM2303, have been used to measure the temperature and humidity of the ambient air quality. The concentration of ammonia in the air inputs and outputs of the farm was measured using an ammonia sensor model TGS2444 every 10 seconds throughout the study and recorded in the central system. The average speed of the exhaust air was measured using the hot wire anemometer probe for every fan. The outputs of all sensors was converted to digital data and transferred to the central computer using RS485 cable in each module. Converting of the sensors output to digital data reduces changing the data and probable errors. Ammonia emission rates was found by calculating the concentration of ammonia and measuring the rate of input air and fans exhaust air by ammonia gas equilibrium equation. Relation of the ammonia emission rate was achieved using affective factors such as age of the birds and inside air humidity and temperature by regression method.
The average rate of ammonia emission during broiler growing were measured 89 mg per day for each bird. Ammonia emission rates increased until the age of 37 days and then decreased after the age of 37 days. Age of birds has the highest impact coefficient and temperature and relative humidity of the barn have the least impact coefficients on the ammonia emission rate. The ammonia emission rate has also increased by increasing the age of the bird, temperature and relative humidity of the air. Comparing of the ammonia emission rate derived from regression equation with real conditions showed that the regression equation method has a high precision for estimating the ammonia emission rate.
It is showed that the results of this research can predict the ammonia emission rate in the poultry houses and predict the required ventilation rates to minimize the amount of ammonia concentration. The results of this study can be used for automatic control system to minimize energy consumption in the poultry houses. According to the results, the reduction of temperature and humidity in poultry house can be used to reduce the ammonia level.

3Associated professor, Department of Agricultural Engineering Research, Khorasan Razavi Agricultural and Natural Resources Research and Education Center, AREEO, Mashhad, Iran
4Ph. D student of öDept. of Biosystems Engineering, Faculty of Agriculture, Ferdowsi University of Mashhad
˜یÏå [English]
Separation of stigma from petals is one of the required tasks in saffron production. The mechanical separation of saffron flowers may be performed through the following steps: I) singulation of the flowers ii) aligning the flowers iii) cutting the style (Konje) and iv) separating the stigma from the petals. Because of the importance of saffron flower separation, a prototype device was constructed in this project for singulating and aligning of saffron flowers, prior to the cutting operation. This device can also be installed to work with other cutting and separating equipment and produce on an industrial scale. A pickup vacuum cylinder was used for singulating the flowers and an inclined V-shaped surface was employed for aligning of saffron flowers. The constructed apparatus was evaluated from the standpoint of singling efficiency and losses. For this purpose, three types of saffron flowers (buds, open short tail and long tail), three rotational speeds of pickup cylinder (6, 12 and 21. 5 rpm), three levels of suction (30, 70 and 100 mm Hg), and four different inner diameters of finger’s nozzle (2, 3, 4 and 5 mm) were used with three replications. The results of analysis on the constructed device showed that the highest efficiency (75%) is observed for short tailopen saffron flowers with 5 mm inner diameter of sucking nozzles, suction of 100 mm Hg and the rotational speed of 6 rpm. To evaluate the performance of flower aligning, 100 saffron flowers with an average length of 5 cm were selected and tested with 5 replications. The average aligning with the correct direction obtained was equal to 84%.

With advanced equipment and development of software in the field of audio signals, it is now possible to record and register the sounds of tiny insects. Subsequently the acoustic features of the insects’ sounds can be extracted and then can be used for insect detection within the storage of bulk materials. One of the big challenge in receiving the audio signals emitted from the tiny insects is their low sound level. The designed audio system utilizes a piezoelectric acoustic sensor for receiving audio signals of the pest which is rice Sawtoothed (Oryzaephilus surinamensis) (Col. Silvanidae) along with a temperature control system. Two audio signals (walking and feeding) of Sawtoothed were captured by the sound system. The audio frequency of Sawtoothed for feeding and walking were 2.1 kHz and 2.8 kHz, respectively. By changing the ambient temperature, it is found that the best temperature range for the pest activity ranged between 25 °C to 27 °C. It was also observed that the pest activity is stopped at temperature below 10 °C. Such a system may be installed within the storages of bulk rice for early detection and other management decision making of the rice Sawtoothed.

Conventional technology of sugar production from sugar beet roots consists of the next subsequent steps: sugar beet slicing, thermal denaturation of the sliced beet roots followed by diffusion in hot water at 70–75 _C, purification of extracted juice by lime, concentration of purified juice and crystallization. The diffusion process is one of the most important steps that effect on product yield. The quality of cossettes is too important that increase extraction yield. Therefore the main function of the beet-slicing operation is to improve the diffusion operation. The most important factors in producing quality cossettes are the type of knives. Most knives have a V shape that cause increase of the surface area of the beets. At slicing operation, Amount of the sucrose of beet extract directly. Considering the blade edge causes the rupture of beet cell so it effects on quality of juice and yield of extraction. Also the different blades edge causes different surface areas that effect on osmotic operation. In this work, the effect of two kinds of blades (serrate and flat edge) is studied on extraction process and the juice quality.
Material and Fresh sugar beets (Beta vulgaris) were obtained from Ferdowsi university farm and storage at 5C°. For each test run, 300gr of cossette 4mm thickness and 8cm in length were cut by cutting device with ability of blade change. The sucrose content of cossetttes was measured in ICUMSA method. These tests were organized as follows 4 steps. The first step was washing. Considering amount of sucrose of sugar beet extract directly at slicing operation, the decrease of sucrose content of cossettes and the sucrose of washing juice should be measured. For this stage, 120gr of cossettes were washed with 320gr of distilled water. After washing the cossettes, the sucrose content of cossettes was measured again and the decrease of suocrose content of cossettes was calculated and sucrose of washing juice was measured. For the thermal-pretreatment step, 130gr of sugar beet cossettes were treated with 260gr of distilled water 70C° at 7min and the sucrose of thermal-pretreatment juice was measured. In the diffusion step, 130gr of these cossettes were immersed in 260gr of distilled water 73C°. The solute concentration (Brix, g solubles 100 g_1 juice) was measured by digital refractometre (PR-101, Atago, 50 Kyo, Japan) every 5 to 10 min up to equilibrium conditions. The final step was pulp pressing. The pulps were compressed by laboratory press (2 bar) at 15min. following by the pressed pulp was weight then the sucrose content of them was measured. In the following, purity and nonsucrose of juice were determined for washing, thermal-pretreatment and diffusion steps. These parameters were measure in ICUMSA unit which is based on polarimeter method. Also extraction yield of diffusion, mass of extraction sucrose and extraction sucrose at diffusion, pressed pulp yield, extraction degree and energy were calculated. The data were analyzed in 2-sample t-test at minitab17 and the graphs were drew in excel.
Results and discussion The results showed that the edge of the baled had significant effect on the juice purity (washing and thermal-pretreatment steps), non-sucrose content and sucrose of juice, extracted sucrose at cutting operation, extracted sucrose at diffusion, degree of extraction, yield of pulp, time and energy (P

The diagnosis of agricultural machinery faults must be performed at an opportune time, in order to fulfill the agricultural operations in a timely manner and to optimize the accuracy and the integrity of a system, proper monitoring and fault diagnosis of the rotating parts is required. With development of fault diagnosis methods of rotating equipment, especially bearing failure, the security, performance and availability of machines has been increasing. In general, fault detection is conducted through a specific procedure which starts with data acquisition and continues with features extraction, and subsequently failure of the machine would be detected. Several practical methods have been introduced for fault detection in rotating parts of machineries. The review of the literature shows that both Artificial Neural Networks (ANN) and Support Vector Machines (SVM) have been used for this purpose. However, the results show that SVM is more effective than Artificial Neural Networks in fault detection of such machineries. In some smart detection systems, incorporating an optimized method such as Genetic Algorithm in the Neural Network model, could improve the fault detection procedure. Consequently, the fault detection performance of neural networks may also be improved by combining with the Genetic Algorithm and hence will be comparable with the performance of the Support Vector Machine. In this study, the so called Genetic Algorithm (GA) method was used to optimize the structure of the Artificial Neural Networks (ANN) for fault detection of the clutch retainer mechanism of Massey Ferguson 285 tractor.
The test rig consists of some electro mechanical parts including the clutch retainer mechanism of Massey Ferguson 285 tractor, a supporting shaft, a single-phase electric motor, a loading mechanism to model the load of the tractor clutch and the corresponding power train gears. The data acquisition section consists of a data analyzer (PCA-40), a personal computer, a piezoelectric accelerometer (VMI-102, DT-2234B), a tachometer and two rubber vibration absorbing elements are located between the rig’s components and the plate holder. An evaluation function was employed in order to achieve the optimal structure of neural network models by selecting the number of layers, number of cells in the layers, transfer function, training function, learning functions, performance function, and number of epochs, in such a way that the MSE of the calculated output error was minimal. The data were collected by means of the accelerometer sensor attached on the clutch mechanism, with three different working conditions (normal condition, with worn bearing, and with worn shaft), and three rotational speeds including: 1000 rpm, 1500 rpm and 2000 rpm. The Wavelet Packet Transform (WPT) was applied on the data-set for features vector extraction and the principle component analyses (PCA) was applied for dimension reduction of the features vector. The signal processing and the features extraction are the most important characteristics of the monitoring methodology, by which the working condition of the machine can be determined. These characteristics may be acquired by transforming the signals from the time domain to the frequency domain and MATLAB software is used for this purpose. This software receives the vibration data (time series of output voltage) which are in Excel files format. To remove the noise a suitable filtering procedure was used and finally the statistical parameters of time - frequency were calculated.
To verify the accuracy of the Genetic Algorithm model, the required data were collected from the training and testing steps of the Neural Network. For this purpose, the statistical parameters such as mean squared error (MSE), mean absolute error (MAE) and correlation coefficient (r) were used. The optimal parameters of the neural network obtained for the family of Db4. A trial and error procedure was used to minimize the mean square error of the network output and the desired amount of training step. During the training step, four neural networks including Db4, Db30, Db35 and Db40 achieved a gradient descent weight in the learning bias and four neural networks including Db9, Db15, Db20 and Db25 achieved a gradient descent with momentum weight in the learning bias. The two of the achieved neural networks including Db4, Db20 have circular logarithm function and the remaining networks have annular hyperbolic tangent transfer function. The most appropriate networks configuration was acquired when the network exhibited the minimal error with the training and testing data sets. The results show that the highest accuracy of the GA-ANN Artificial neural networks for all rotational speeds (1000, 1500 and 2000 rpm), and working conditions (intact gear and shaft, damaged bearing and worn shaft) observed for the network family of Db4. The highest error observed for the family of Db20 with MSE of 0.011.
Artificial neural networks can somewhat think and make decisions similar to an expert person. In this project in order to predict the occurrence of a failure of the clutch mechanism of MF 285 tractor, the experimental data were obtained using some sensors, and the data were transferred to a computer by means of a data analytical. By training of the neural networks, the errors were identified separately. The output data from the combined Neural Network and Genetic Algorithm shows that the performance of the prediction model is enhanced. Based on the experiments and calculations, the best data set belongs to the family of Db4 network with the least MSE equal to 4.09E-07 and r equal to 0.99999, indicating that the model could precisely detect the faulty bearings or shafts.