ترحم مصری گندشمین

Climate change, driven by greenhouse gas emissions, necessitates a shift in how we approach fuel consumption. Since a significant portion of these emissions originates from the transportation sector, it is crucial to implement measures to decarbonize this area. Currently, a large percentage of vehicle energy is derived from fossil fuels. To mitigate pollution and combat global warming, there is a pressing need to replace conventional combustion engine vehicles with sustainable alternatives. This study provides a comprehensive review and comparison of four prominent vehicle technologies: biofuel vehicles, fuel cell vehicles, electric vehicles, and solar vehicles. Among these options, electric vehicles show the most promise for reducing CO2 emissions. Both electric and solar vehicles are recognized as practical and sustainable choices. Notably, fuel cell vehicles produce the least greenhouse gas emissions from their exhaust. However, several challenges persist, including infrastructure limitations, a shortage of biofuels, increased weight, and rapid tire wear for fuel cell vehicles. Additionally, the intermittent nature of solar power generation presents difficulties for solar vehicles, as their performance is influenced by factors such as sunlight, temperature, and shading. Electric vehicles emit fewer greenhouse gases compared to conventional vehicles, although the extent of emission reduction is contingent upon the electricity generation source. Utilizing renewable energy sources for electricity significantly lowers greenhouse gas emissions, whereas reliance on petroleum sources yields only minimal reductions.

A stable and readily available energy supply is of paramount importance for economic development. This is evidenced by historical global competition for energy resources, which has been driven by their significance for national security and governmental stability. The current global energy consumption trend presents significant resource depletion and environmental degradation challenges. Two principal solutions are put forth: the enhancement of energy efficiency and the transition to renewable energy sources, with the latter being the optimal long-term strategy. Nanoparticles, due to their minute size, diverse shapes, high reactivity, and stability, have garnered considerable research interest. This study assesses the impact of Fe₃O₄ nanoparticles on the anaerobic digestion of cow manure in two types of digesters: discontinuous and semi-continuous.

This study utilized four transparent plastic batch-flow digesters, each with a volume of 4 liters, and four semi-continuous horizontal plug-flow digesters, constructed from a combination of PVC and plexiglass pipes. The length of the digesters was approximately 120 centimeters, divided into three sections by two baffles to create a three-stage digester system. The volume of each digester was approximately 12 liters, resulting in a volume of approximately 4 liters per section. Each section was furnished with a gas discharge valve and an inspection and sampling port. The four digesters were situated within an enclosure. The experiments were conducted at temperatures suitable for mesophilic organisms. A thermostat module and two 1000-watt heaters were employed to regulate the temperature. Two fans were positioned behind the heaters to facilitate air circulation. To prevent the accumulation of sediment, clogging, and the formation of foam on the surface of the substrate, and to ensure the uniform dispersion of nanoparticles within the substrate, agitators were installed within the digesters. The experiments were conducted using iron oxide nanoparticles with a diameter of 50-100 nanometers, manufactured by Sigma-Aldrich. Three different concentrations of Fe3O4 nanoparticles were utilized: 50, 100, and 200 milligrams per liter, respectively, for the first, second, and third experiments.

During the initial four-day period, digesters one, two, and three generated greater quantities of biogas than the control in both batch and plug-flow systems, despite the overall low production levels observed initially. Digester 2 in the batch system demonstrated the highest biogas production, with a volume of approximately 37 liters over 39 days, representing a 41% increase compared to the control. In the plug-flow system, digesters 2 and 3 produced 165.68 and 149.45 liters of biogas, respectively, representing a 36% and 23% increase over the control. The biogas production of Digester 3 was comparable to that of the control. The decomposition of organic matter was found to be accelerated by lower concentrations of Fe₃O₄ nanoparticles, while higher concentrations were observed to inhibit anaerobic digestion. The highest methane production in the batch system was 11.94 liters in digester 2, representing a 51% increase over the control, while digesters 1 and 3 exhibited comparatively smaller increases. It is necessary to allow sufficient time for methanogenic microorganisms to adapt to additions of nanoparticles. In the plug-flow system, digester 2 produced a total of 50.3 liters of methane, representing a 48% increase over the control. This result demonstrates the effectiveness of a 100 mg/L dose. The findings indicate that there is no linear relationship between nanoparticle concentration and methane production. Instead, effective concentrations vary based on nanoparticle size and other factors.

The study revealed that the incorporation of nanoparticles into anaerobic digestion processes enhances biogas and methane production. However, the optimal concentration of nanoparticles varies depending on the specific conditions, feedstock type, and size of the system. The highest biogas and methane production was observed at 100 mg/L in a semi-continuous digester, with a 36% and 48% increase, respectively. The highest biogas and methane production was observed in tank number two (36%), followed by tank number three (34%), and the lowest in tank number one (29%). This indicates that biogas production necessitates an adequate period for microorganisms to effectively engage in methanogenesis.

A large number of biogas plants fed by municipal solid waste (MSW) are installed around the world. However, extensive research is being conducted with the aim of maximizing the performance of the anaerobic digestion process, in order to further compare it with fossil fuels. One of the most important problems of this process and the main obstacle to this transformation is the complex structure of lignocelluloses and the difficulty of its biological transformation. For this purpose, the effect of different concentrations of nanoparticles (NPs) of zero valent iron (ZVI) (50, 70, 90 and 110 ppm ZVI NPs) on maximizing biogas and methane production was evaluated. In this study, the ratio of co-digestion mixing MSW and sewage sludge (SS) was determined from our previous studies as the optimal ratio (MSW: SS: 60:40). The results showed that the highest methane yield was obtained by adding 90 ppm ZVI NPs based on statistical analysis (at least a significant difference using Duncan test) compared to the control treatment (p<0.05). Iron nanoparticles (Fe NPs) also led to an 85% increase in cellulose, a 64% decrease in lignin and a 33% decrease in hemicellulose, indicating an increase in biodegradability due to NPs. The highest methane production was obtained at 90 ppm ZVI NPs concentration which increased by 45% compared to the control treatment and the highest reduction of TS and VS in this digester was 31 and 17% compared to the control digester, respectively.

Peanut (Arachis hypogaea L.), which is also called ground pistachio in Iran, is one of the most important and economical oilseeds in tropical and subtropical regions, which is rich in minerals, vitamins, fatty acids, fiber and compounds. It is phenolic. Peanut production is important for consumption, income generation and improving the food security of smallholder farmers and due to its high quality of oil and protein, it is cultivated in at least 109 countries of the world. In recent years, due to changes in the price of dried fruits, this product has also attracted the attention of the farmers of Maghan Plain (North of Ardabil Province, Iran) and in the crop year of 2019-1400, with the cultivation of more than 10,000 hectares, the amount of production reached 30,000 tons of peanut products in Mughan has been harvested. Previously, Gilan province was in the first place of this product with the cultivation of 2,500 hectares. Despite the fact that about a century has passed since the cultivation of peanuts in Iran, not much research has been done on its sustainable production. One of the critical stages of production of this product is the harvest stage. This stage, while having its own difficulties, is associated with significant losses, which are considered by experts due to its high economic value and direct and indirect adverse environmental effects. In recent years, this product has also been considered by farmers in the Moghan Plain due to the special conditions of the Iranian economy. Peanut harvesting in Moghan is done manually and three machine methods including semi-mechanized, pull type mechanized and self-propeller. Each of these methods has unique features. Therefore, in this study, while evaluating important harvest indicators such as quantitative loss, quality loss, actual field capacity, number of labor required, cost of harvest operations and price of machines required, the best harvesting system using the Multiple Criteria Decision Making Matrix (TOPSIS model) was introduced.

The present research was carried out in Pars Abad, Moghan, in the crop year of 2018-2019, which was cultivated with Astana flower variety and mechanized. The final product was harvested on the 10th of October 2019 with approximately 19% soil moisture. Harvesting options were done randomly and with three repetitions as follows:1- Manual method (A1): Using a V-shaped chisel, the root of the crop was loosened and harvested by human labor. Separation of Almond kernels from Materials Other than Grain (MOG) and cleaning of the final product was done using manual methods using a special sieve.2- Semi-mechanized (A2): In this method, the peanut thresher behind the tractor manufactured by Dezful Machine Company, model PPH70 was used. This device does not have a pick-up platform. Therefore, feeding the device was done by two humanlabours at the same time as the tractor moved forward.3- Pull type mechanized harvesting system (A3): In this method, the peanut harvesting pull type combine machine behind the tractor made by Bacanaklar Company in Turkey was used. This device is equipped with a pick-up platform and does not require human labors.4- Self-propeller system (A4): In this method, a self-propeller combineharvester (special peanut harvesting combine) made in China (Henan Longfei 4HZJ-2500) was used. Due to its self-driving nature, it does not need a tractor.The parameters considered in this research are:1- Quantitative losses (%C1): These losses include the pods dropped on the ground and the pods left on the plant, which were not collected due to the improper operation of the machine harvesting system and the improper operation of the worker in manual harvesting, and are considered among the crop losses.Where: B (kg/m^2) is the weight of the sample (loss) collected inside the fram with dimention 1×1 m^2 and P is the estimated yield of the product (kg/ha).2- Quality loss (%C2): Quality loss is an estimate of the percentage of non-grain material among the pods of the product in the grain tank.Where: Ws is the mass of the sample taken from inside the tank and WMOG is the mass of Material Other than Grain (MOG) in the sample.3- Field capacity (C3) (ha/h): The actual capacity (taking into account the time intervals caused by the operation of the machine and the operator from the moment the machine entered the farm) was calculated as the amount of machine operation basedon the area (A) per time unit (T).4- Number of human labours (C4) (person days per hectare): This index was also calculated by considering the amount of work done by one worker in 8 hours of working day. 5- The final cost of harvesting operations (C5) (million Tomans per hectare): The price index was calculated based on the regional rate of peanut harvesting operations.6- The amount of initial fixed investment including the purchase of related tools and machines (million Tomans) (C6) In order to choose the best harvesting and ranking system, the multi-criteria decision matrix method and TOPSIS model were used.

The results showed that the lowest quantitative loss with C1=10.5% was for the manual method and the highest value C1=20.4% was for the semi-mechanized method. Manual and semi-mechanized harvesting systems had the highest harvest quality index and the lowest non-grain material (approximately 11.5%). Also, self-propeller system with Ca=0.7 ha/h and manual harvesting with Ca=0.0033 ha/h had the highest and lowest actual field capacity, respectively. The manual harvesting method required more labors than other methods and had the highest cost of harvesting operations. The results of using the TOPSIS method also showed that considering all indicators, the pull type mechanized harvesting system with a value of CL* = 0.79 is in the first place of options and manual harvesting is in the last option of this study. Semi-mechanized and self-propeller systems were also ranked second and third, respectively. Despite the fact that the pull type mechanized harvesting system with a value of CL* = 0.79 was ranked first in harvesting systems, but its difference of 0.21 with the ideal option, indicates that to improve this system, a lot of work is needed, including quantitative and qualitative decline should be reduced, which needs to be seriously considered by researchers in this field.

Life cycle assessment (LCA) quantifies and interprets environmental impacts across a wide range of products or processes used throughout the system. Despite the need to understand regional differences in the life cycle assessment of agricultural systems, the LCA method is poorly defined due to regional heterogeneity modeling in agricultural production and transportation. Therefore, there is an opportunity to help develop LCA protocols by adapting methods commensurate with crop production and supply chain changes. The annual balance in soil organic carbon is a function of three main processes: stabilization in plant biomass, plant, soil, rooting, and long-term storage in soil. According to Kozyakov, a comprehensive review of the carbon cycle, approximately half of the total carbon dioxide absorbed from the atmosphere by the wheat plant is converted to biomass. This study aims to evaluate the impact of using wheat waste for biogas production on the environment and greenhouse gas emissions. This research's life cycle inventory includes the production of wheat waste and its use in biogas production. Many studies have been devoted to discovering the effects of these various variables, particularly in terms of global warming and nitrogen fertilizer leaching, and based on such comprehensive information, a Life Cycle Assessment has been proposed to assess the cumulative environmental impacts of agriculture. The challenge for these tools is to be relevant both on a global scale, ie for global impact categories such as climate change, and on a smaller scale, i.e. for regional impact categories such as water eutrophication. The International Organization for Standardization and the Society for Environmental Toxicology and Chemistry provided an overview of the LCA method. However, the main issues under discussion are life cycle assessment methods, aggregation methods for different impact groups, and the final calculation of a summary environmental indicator. Next, inventory analysis continues with impact assessment.

This case study examines regional changes in field straw production and crop transportation for wheat using life cycle assessments and compares the results with carbon cycle estimates. Wheat is used as an agricultural product because it is the third-largest crop in terms of volume and value and grows in almost many parts of Iran. Life cycle assessment is different from previous studies and emphasizes the use of fuel, fertilizer, yield, and transportation throughout the country in the emission of greenhouse gases in agricultural methods and tries to spatially and different types of production. Wheat is independent. This case study can be partially supported and pursued by research centers for life cycle assessment, which are interested in conducting environmental studies in wheat production. Many of the wheat's share is imported, influencing domestic food systems policy.The study area is Iran, with a share of about 2.2 percent of world wheat production, is ranked 14th among twenty wheat-producing countries. According to the Food and Agriculture Organization of the United Nations (FAO), China ranks first in 2019 with 131 million tons of wheat production. India follows it with 99.7 million tons, Russia with 72.1 million tons, the United States with 51.3 million tons, and France with 38.5 million tons. It is noteworthy that among the countries in the region, Pakistan, with a production of 25.1 million tons (1.8 times more than Iran), in the seventh place in the world, and Turkey with a production of 20 million tons of wheat (1.5 times more than Iran) in the eleventh place in the world are located. Figure 2 shows the complete map of wheat production in Iran. According to statistics, the highest wheat production (rainfed and irrigated) is related to the cities of Shousha, Marvdasht, and Ahvaz, with an approximate share of 2.3% of the total 84% of wheat production. Bilesvar city in Ardabil province is ranked eighth in the country with a share of 1.5 percent of total wheat production.This study aims to determine the amount of biogas production from wheat waste. Due to climatic conditions, the study area can produce biogas from wheat waste (as carbon waste). The amount of wheat waste used in biogas production varies. For biogas production, the combined presence of nitrogen and carbon is necessary to regulate C / N, which is an essential parameter in biogas production. Various studies have been conducted in the field of biogas production. In a study conducted by Jafari et al. (2019), in the conventional biogas production method, the share of wheat waste in the total amount of substrate was considered to be about 45% of the total material. According to the findings, the highest impact is related to the Aquatic ecotoxicity index with 24.63. The lowest level is related to the Ozone layer depletion index with a value of 0.0000000264. Table 3 presents the damage caused by consuming one kilogram of wheat straw for biogas production on the final indicators. According to the final indicators, it can be concluded that the lowest impact on the final indicators for the consumption of wheat straw as a substrate for biogas production is related to the Human health index with an approximate value of 0.000000342. The highest impact is related to the index related to Resources with The value is approximately equal to 3.26.

In this study, the effects of fermentation of wheat waste for biogas production were investigated in a comparative format for environmental indicators. In general, it can be claimed that the lowest impact on the final indicators for the consumption of wheat straw as a substrate for biogas production is related to the Human health index with an approximate value of 0.000000342, and the highest effect is related to the Resources index with an approximately equal value. In general, the environmental effects of wheat consumption as a substrate for biogas production are higher than the impact of wheat straw and grain production on some intermediate indicators. Another scenario could be proposed to prevent methane from entering the atmosphere directly. This procedure is also accurate in the final indicators. The release of methane into the air increases the environmental impact because, according to various sources, the impact of methane on global warming is several times greater than the impact of carbon dioxide on global warming. Therefore, there is a need for a consumption scenario after biogas production to prevent methane from entering the atmosphere.

This research aimed to investigate co-digestion and the effects of alkaline pretreatment conditions on substrate degradability and methane production from municipal solid waste (MSW) and sewage sludge (SS) in a pilot and experimental approach in non-continuous reactors with a retention time of 30 days. For this purpose, co-digestion of the organic fraction of MSW and SS with 5 mixing ratios of raw materials was evaluated to evaluate the biogas and methane production from it in with mesophilic temperature conditions (37 °C). Therefore, the results showed that the increase of SS (up to 40% by weight of raw materials) in feed led to increased methane yield, while longer retention times were obtained by increasing the percentage of MSW. So, the ratio of MSW (60%) and SS (40%) (60:40) was determined as the preferred ratio for optimal biogas production. Based on the preferred ratio, the effect of different concentrations of alkaline pretreatment (2, 6 and 10% NaOH) for different time periods (1.5, 3 and 6 hours) on the biodegradability of MSW, biogas and methane production was evaluated. The results showed that the best improvement was with the treatment at a concentration of 6%NaOH for 3 hours, which led to a 30% improvement in methane production. Other treatments were also effective, in which 6%NaOH treatment for 1.5 hours resulted in a 17% improvement in methane yield compared to the control sample. Therefore, alkaline pretreatment significantly improves the decomposition of solid organic waste and thus increases methane production.

Livestock waste is the most suitable material for biogas production, but it is not always the best material. If cows are kept on a farm with a burial system, collecting their feces for biogas is very easy. While usually, in Iran, farms are traditional and even live in rangeland in some months of the year. As a result, it is likely to have to be collected from the ground to obtain fertilizer, which increases the workload for biogas production. Otherwise, the substrate remains in the new range. Other parts of the plant can also be used, such as wheat straw, unless they have a high lignin content. Development in rural areas has suffered from armed conflict for the past 100 years, and people have fled to neighboring cities or countries. In some villages in northwestern Iran, the gas pipeline network is not extensive. Liquefied natural gas transportation is a solution for transferring energy to remote villages. Extensive use of firewood for energy purposes also severely impacts local forests. This phenomenon is one of the main reasons for increasing greenhouse gas emissions, deforestation, and land degradation. It is estimated that replacing these heat generation methods with biogas digesters can significantly contribute to the production of pollutants, land and forest degradation, and the prevention of environmental pollution. The situation is similar in arid and semi-arid regions of Asia. There are many benefits to using biogas as an energy source. Efficient, reliable, cost-effective, and environmentally friendly. In most rural areas, home cooking is cooked on a conventional stove or traditional stove, where the biomass is burned directly, leading to further emissions of carbon monoxide and hydrocarbons. Indoor air pollution primarily affects women and children because they are usually responsible for cooking and spend much time in a smoke-contaminated environment, leading to serious health problems such as lung cancer, pneumonia, or other lung diseases. Be. This study aims to set up three different possible scenarios for a small-scale biogas digester in different regions of northwestern Iran with low technology. The use of ubiquitous biomass such as cow manure as a base, kitchen waste (medium fat), potato peel, or wheat straw helps obtain a constant gas throughout the year without the need for ample storage as substrates. These resources are provided daily and are available for a long time. In addition, greenhouse gas emissions are calculated for each scenario to show the difference in carbon footprint compared to using firewood or liquefied petroleum gas for cooking instead of biogas. Finally, the usefulness of the by-product, fermented litter, is also an important point to check its usability as a plant fertilizer. This study helps to map out the future of small-scale biogas digesters in rural, mountainous areas.

In order to calculate the specific emissions of biogas emissions, the standard method of life cycle assessment (LCA) was used using the SIMA PRO tool. This study aims to provide special effects on global warming/greenhouse gas reduction in production and the use of biogas as an energy source. Therefore, helpful energy was used as the functional unit of one megajoule. The system boundaries are shown in Figure 1. For biogas systems, life cycle inventory (LCI) is collected from the LfL biogas database.The selected beds represent the typical waste of rural households in northwestern Iran. Most farmers in these areas have dairy cows, so choosing cow manure as the primary substrate is a logical consequence. This substrate can be removed directly from the farm or must be collected from the pasture ground and then mixed with water 50:50 to obtain a homogeneous liquid to feed the biogas digester. Where people cook, waste is generated; here, we look at medium-fat kitchen waste as an input option for the fermenter. A unique waste product of daily cooking is potato skin because potatoes are considered the leading food in northwestern Iran and other parts of Iran. The common substrate for the third scenario is wheat straw after harvest and wheat processing. Instead of not using it or using it poorly, a more efficient way is to extract the biogas by cutting it into small pieces and fermenting it. In short, all materials are available all year round, and if they are not used to produce biogas, they have no or little use.

According to the presented results, significant differences in the gas output of the three modes are detectable and are shown in Figure 2. The second scenario with 70% cow manure and 30% potato skin shows the lowest biogas production per year with 69.6 square meters. With an annual biogas production of 76.3 square meters, the first scenario is currently more productive than Scenario II, but the third scenario has the highest biogas output. This modulation, including 60% cow manure and 40% wheat straw, produces 94.2 square meters of biogas per year and is the most efficient of the three scenarios. The first scenario emits the lowest CO2 equivalent with 0.236 kg, and the third scenario emits the highest CO2 equivalent with 0.333 kg CO2 equivalent. Because biogas emissions are pretty similar (0.151-0.182 kg equivalent to CO2), the value of machinery and equipment varies between 0.070-0.155 kg equivalent to CO2 in three scenarios. However, unlike the fossil fuel source, which often uses LPG and NG, biogas scenarios do much less damage to the climate than this fuel. Greenhouse gas emissions are 1.59 kg, equivalent to CO2. Which is much larger than biogas; Figure 2 shows the total gas production, while the critical part is the methane content because methane represents the energy content of the gas. Most digesters must be built for direct gas use and direct gas combustion in northwestern Iran for cooking purposes. Therefore, methane analysis is a significant concern in this area. The blue bars in the diagram (Figure 2) show the total biogas production of each scenario. Therefore, the direct use of methane produced by the fermentation process and biogas production (rather than releasing it into the air) will be an essential factor in reducing the environmental impact of the biogas produced.Biogas has already proven its potential to become a successful model for sustainable energy production in other countries. Its environmental, social, and economic benefits are visible to users and all countries. In northwestern Iran, employment in rural communities can be created by establishing local cooperatives to install and maintain biogas production systems. Women become empowered because they no longer suffer from air pollution. Because fuel and fertilizer costs are reduced entirely or at least in large part, biogas means financial support for families in the long run.However, budget is a significant constraint on progress in implementing biogas in rural areas. In most countries, installing a digester for subsistence farmers without subsidies is neither cost-effective nor sustainable. More solutions and options to reduce the cost of digestive systems must be developed for each country to reach the goal of 2030, namely green energy for all. Evaluating carbon emissions trading or other sustainable subsidy mechanisms can help make biogas more cost-effective. Asset costs prevent the spread of biogas systems and the lack of knowledge. Therefore, investing in education is very important to increase the interest in biogas among the people. Rural communities need to be more involved with this technology to understand it as a good energy source for their families. More research needs to be done because there is almost no biogas database in Iran.

This study was conducted to investigate the effect of different tillage methods, different levels of previous crop residues and different cultivars of soybean on yield and yield components of this plant. This experiment was conducted in the form of a factorial design with a completely randomized block base with three replications and during the growing season of 1399-1400 in one of the farms of Moghan Agriculture and Industry and Livestock Company located in the northeast of Ardabil province. In this study, two types of tillage systems (conventional and non-tillage) and two levels of wheat crop residues (complete and no residues) on the yield of two types of soybeans (Williams cultivar and L17 cultivar) were evaluated. The results of the effect of the mentioned systems on plant height, number of plants, 1000-seed weight, crop yield, percentage of protein and fat of soybean showed that the type of tillage had the most effect and the amount of residues had the least effect on the mentioned parameters. According to the results of the study of the effect of different tillage systems, the amount of residues and the type of plant planted on the agronomic characteristics of soybean at different levels can be expressed that by planting cultivar L17 cultivar soybean The highest crop yield can be achieved with conventional tillage method and complete amount of previous crop residues.

Wheat is one of the most important grains in the human food basket, which is known as a major source of energy, protein and fiber due to its valuable nutrients. The post-harvest stage of the wheat crop is explained in two ways: either it is sent to food processing factories or it is stored in silos for sale at regular intervals. Various parameters represent the quality of wheat grain that the percentage of purity of the mass is one of the main factors affecting the purchase price of the product. Several types of non-wheat grains, including germinated grains, broken grains, legumes, weed seeds, insect-damaged grains, foreign matter (pebbles, straw), etc. are the main sources of impurities in wheat. Researchers have always tried to develop computer-based solutions for impurities in wheat grain to be able to develop automated wheat grain separators. Image processing based on morphology, color and texture characteristics of grains has been used for various applications in the grain industry, including grain quality assessment and wheat classification. Various grading systems based on image processing have been studied. The presence of various impurities at the time of wheat harvest is one of the important factors in reducing the quality of wheat, so it seems necessary to detect impurities in wheat grain. The quality of wheat has a significant effect on its marketability. In addition, if wheat is used as a crop seed, the impurities in the mass will be a determining factor in the yield of the future crop.

In statistical analysis of data, situations are sometimes encountered in which the relationship between problem variables is very complex. This makes it difficult to analyze and process the data, so that sometimes no definite relationship can be found between the variables. In these cases, instead of purely theoretical research, applied research is done. Artificial neural networks are one of the solutions that, by processing experimental data, discover the knowledge or law behind the data, and transfer it to the network structure. In this study, the possibility of identifying wheat in wheat grain mass and estimating the amount of impurities in the mass, based on video processing using two types of artificial neural network (ANN) algorithms and hybrid genetic algorithm (GA) has been investigated. For this study, the code related to the artificial neural network with two hidden layers and the number of different neurons in each layer was written in MATLAB software. This code was used to identify and classify each component in the wheat grain mass. The main task of ANN is to learn the structure of the model data set. To achieve this, the network is trained with examples of related outcomes to generalize the capability. Multilayer artificial neural networks (MLPs) are the most common ANN models. In the present study, to reduce the computational load and increase the accuracy of the results, as well as to save time, some parameters that can be changed in the genetic algorithm were extracted as a fixed number using trial and error method. Among these parameters is the number of layers in the main structure of the neural network.

A hidden layer with a number of neurons 2 to 12 was used as an even number. It should be noted that the number of neurons above this amount of computational time increased dramatically and did not have much effect on classification accuracy. Another parameter in this field is the Max Reproduction factor (Max Generation) which according to the results of trial and error for this factor, the results showed that increasing this value more than 30 has little effect on classification accuracy and decreases the mean squared error. And only increases the computation time, so a constant value of 30 was considered for this parameter. 4 values of 50, 100, 150 and 200 were used for the Pop Size parameter. Values above 200 dramatically increased computational volume and processing time, so values over 200 were omitted. Values less than 50 also reduced classification accuracy, and values less than 50 were excluded from the analysis process. After preparing the video of mass movement on the conveyor belt, using MATLAB software and image processing toolbox, 17 shape features, 12 color features and 6 texture features were extracted from each grain sample in the image. The data obtained from the image processing section were classified into five categories: wheat, barley, oats, straw and weed seeds. Two types of artificial neural network (ANN) algorithms, feeder (newff) and feeder (newcf), and hybrid genetic algorithm (GA) were used to achieve the highest classification accuracy and minimum error.

Techniques related to image segmentation were used to separate objects within the image. In this stage of image processing, an attempt is made to separate interconnected objects using a variety of morphological and color methods in the image. In fact, the purpose of separating interconnected objects in the image is to make it possible to examine the individual objects in the image separately and extract the different characteristics of each of them. The results showed that from 36 different artificial neural network (ANN) structures, the 5-4-10-35 structure for the newff algorithm with 100 and 89.74% accuracy for training and testing conditions, respectively, with a processing time of 10.39 seconds and the structure 5-8-10-35 for newcf algorithm was obtained with 100% accuracy for training conditions and 87.17% for test conditions with a processing time of 44.94 seconds. On the other hand, the results of the hybrid GA algorithm showed the highest classification accuracy with 95.55% and 86.66% for training and testing, respectively, in a structure in which 8 neurons in the hidden layer with a population size of 200 were used. Was obtained. According to the obtained results, the use of video processing using ANN artificial neural network and newff algorithm due to high accuracy and lower computation time is a powerful tool for detecting impurities in wheat grain mass. Therefore, the use of artificial neural network with the help of video processing has the ability to classify wheat grains and can be used in a practical way. Given the importance of grain mass velocity in the discussion of industrial application, it is suggested that higher grain mass velocities be investigated in a similar way.

The four-wheel drive and rear-wheel drive tractors are commonly used in agricultural operations. In order to investigate the effect of a type of driving system a series of tests were performed usin the three driving systems of foour wheel drive, rear wheel drive and front wheel drive in different axle loads of 0, 150 and 300 kg, tire inflation pressures of 170, 200 and 230 kPa and travel speeds of 1.26, 3.96 and 6.78 km/h. Bulk density was measured as an indicator of soil compaction at different depths of 10, 20, 30 and 40 cm. Also, under the different conditions, the drive wheel slip was measured. To carry out the tests, the four-wheel tractor of Goldoni 240 was used which has the ability to work with mentioned driving systems. The experiments were carried out under controlled conditions in a soil channel  with the length of 3 m and a width and depth of 1 and 0.6 m, respectively. Test were conducted in completely randomised block design with three repetations and results were analysied using SPSS 22 software. The results showed that by changing the driving system from 4WD to RWD and FWD, there was a significant increase in soil density, with the lowest density associated with 4WD system and the highest density related to FWD. The reason for increasing the density by changing the driving system can be attributed to different slip levels in each of these systems due to the lower slip percentage of the 4WD system than the other two systems.  Increasing axial load increased soil boulk density. Of note that with increasing the axial load, the stress was transferred from the surface soil to the subsoil layers. As the axial load on tire increases, the subsoil density was closer to the surface layer. Increased axial load on tire and decreasing tire pressure reduced wheel slip. Stepwise regression model with determination coefficient of 0.92 and according to calculated standard coefficients showed that axial load, soil depth, type of driving system, tractor speed, and finally tire pressure, have the greatest effect on soil bulk density, respectively.

Bioenergy recovery and pollution control through anaerobic digestion (AD) of organic wastes is a promising greenhouse gas mitigation option and is considered to be a sustainable waste treatment practice. Since methane-rich biogas is the main end product of AD, methane production must be improved to maximize revenues from energy generation and hence, to make digestion facilities more profitable. The total solids (TS) content in association with the organic loading rate is also one of the key factors that affect the performance, cost, and stability of AD systems. It has been reported that the TS content affects the following parameters: rheology and viscosity of the digester contents, fluid dynamics, clogging, and solid sedimentation that can directly influence the overall mass transfer rates within the digesters. Since the TS content is an important parameter, two main types of AD processes have been developed: liquid and high-solids AD. Liquid AD (L-AD) systems typically operate with 5-10% TS, while high-solids AD (HS-AD) refers to a process that generally operates at 15-40% TS. The purpose of this study was to determine the optimal mixing ratio of co-digestion municipal solid waste and sewage sludge. Then, the effect of high-solids and liquid anaerobic digestion on the mixing ratio was investigated using pilot-scale anaerobic non-continuous bioreactors.

Increasing energy consumption has created an energy crisis in the world. Fossil fuels are limited and depleting. Biogas is considered a fuel that has attracted the attention of researchers. To increase biogas production, different pretreatments have been utilized. The purpose of this study was to investigate the optimal mixing ratio of Municipal Solid Waste (MSW) and Sewage Sludge (SS), as well as the effects of various conditions of alkaline pretreatment on biodegradability of wastes and the amount of biomethane production.

This study was done in a laboratory digester with 1 L volume at 37 °C with different concentrations of NaOH in a completely randomized design. Biogas volume, methane volume, and changes in pH were measured daily. Measurement parameters in the anaerobic digestion including total solids, volatile solids, and carbon and nitrogen content in the feedstock were determined according to the APHA standard methods.

The optimal mixing ratio of MSW to SS was 60:40 with the highest methane yield of 254.87 mL/g VS. Next, the effects of 2, 6, and 10% NaOH concentrations were evaluated on the amount of gas produced, indicating that 6% NaOH concentration significantly improved waste decomposition. Methane production, VS, and TS removal were compared to the control treatment, and there were increases of 30, 27.94, and 27.25%, respectively.

The results showed that the mixing ratio of MSW to SS at 60:40 with 6% NaOH improves the decomposition of organic wastes and increases biomethane production. 

Stereo vision is an approach to 3D information from multiple 2D views of a scene. The 3D information can be extracted from a pair image, as known stereo pair by estimating the relative depth of points in the scene.Soil aggregate size distribution is one of the most important issues in the agriculture sector which highly affects energy consumed for preparing the field before planting. Mean weight diameter of clods is a standard metric for determining clod (big aggregates) size. Conventional methods are based on sieving soil samples to calculate the MWD. However, they are faced with several challenges in larger scales and practical applications. Furthermore, due to inherent limitations of soil environment and also being a tedious work, traditional methods would beuse to estimate the metric higher or lower than actual value.As new methods, researchers are using computer vision techniques as virtual sieve so that the size of clods can be determined via processing digital images which have been taken from soil surface. Although, image-based methods have solved many of previous problems, their accuracy is not so high due to the complexity of soil environment and overlapping colds, and needs to be improved. In order to overcome the mentioned challenges, in the current study stereo vision method was developed so that it is possible to extract the third dimension information as height of clods which helps us to categorize clods into their own class.

In this study, the W3-Fujifilm stereo camera equipped with two 10-megapixel CCD sensors for both left and right lenses, and baseline spacing of 7.5 cm was used. The distance between the camera lens and the ground was also set to 60 cm.In order to get three components of soil clods including (x, y, z), point cloud was investigated. For this, local features were extracted using a SIFT feature detector. The SIFT algorithm is robust against scale, rotation and illumination changes, so that these specifications have made it as a strong tool in the field of stereo vision. Then, the extracted features (keypoints) were matched between two stereo pair images by means of Brute Force algorithm and the location of all corresponding points were determined and point cloud was obtained.At the final stage, three features including length, width and height of all six classes of soil clods were entered into a linear classifier entitled discriminant analysis. This classifier as a linear separator classified these six classes based on appropriate functions in a 5 dimensional space.

Results of classification model showed that the height (thickness) of clods have more distinguishing different soil clods. The reason for this refers to the event of overlapping, because most of clods were touched each other after sieving. Consequently, the length and width of clods had not significant effect in soil aggregates classification.In order to analysis the result of soil aggregate classification, confusion matrix was calculated and the overall classification accuracy was achieved 83.7%. The lowest and highest accuracy were obtained for class 1 (the littlest class) and class 6 (the biggest class), respectively due to their low and high height from the soil surface.

In this research, the basic geometrical features including length, width and height were extracted from stereo pair digital images via stereo vision techniques to classify six classes of soil clods. This aim was reached by 3-D reconstruction of image data, so that the height of each image as the third component of (x,y,z) was obtained as well as the length and width. The results of classification indicated that the stereo vision technique had the satisfactory performance in determining the aggregate size distribution which is one of the most important indices for tilled soil quality.

Soil saturated hydraulic conductivity (Ks) can be estimated from surrogate data such as soil texture, bulk density and organic carbon and CaCO3 contents using regression (Reg-PTFs) and artificial neural networks (ANN-PTFs) pedotransfer function (PTF). Saturated hydraulic conductivity was measured by falling head method in 100 soil samples that obtained from Ardabil plain, Iran. After performing physicals and chemicals analysis on soil samples, the data were divided into two sets of training (80 samples) and validation data (20 samples). Regression models were created by SPSS software, stepwise method and neural networks models were created by Neurosolution software. Statistics criteria such as coefficient of determination (R2), root mean square deviation (RMSE) and Akaike information Criterion (AIC) were determined. Input variables in the best regression models were sand, silt and bulk density. The best neural network models were obtained from the input variables that include bulk density, geometric mean and standard deviation of soil particle size distribution. The values for R2 and RMSE in training and testing data set for the Reg-PTF were 0.53, 0.074 and 0.51, 0.052 and for the ANN-PTF they were 0.84, 0.04 and 0.73, 0.06, respectively. In this research all independent variables such as bulk density, particle density, CaCO3, geometric mean and standard deviation of the particle size distribution included as inputs for development of Reg-PTFs and ANN-PTFs. The amount of R2 and RMSE for training and testing data set equal 0.87, 0.036 and 0.58, 0.076, respectively. Results showed that the ANN-PTF (R2= 0.84) performs better than the Reg-PTF (R2= 0.53) in this case. It was also found that when all independent variables were used as inputs in the neural ANN-PTF the values of R2 and RMSE (0.87 and 0.036) have been improved in the training data set.

Considering the low cost of the wind power production and its relatively good compatibility with the environment, wind farms have shown extensive growth in the past few years. Considering the importance of using the wind power and its advantages, the careful planning is needed to identify the available generation potentials in a region or a country to facilitate its increased use. By the end of 2009, the capacity of wind turbines installed in the wind farms of Iran was 92 MW, which demonstrates the significant potential for additional wind farms in the country and suggests investments in the wind power industry are likely cost effective. The main purpose of this research is to assess the potential of wind power for the city of Pars Abad in northwestern Iran.
In order to measure wind power density and wind energy potential, wind speed data collected every 3 hours at a height of 30 m above the ground for 11 consecutive years are analyzed; the data are provided by the Iranian Meteorological Organization and are used in the assessment of electricity production potential in the area chosen for the wind turbines installation. To determine the wind energy potential at a site and estimate the energy output from this site, statistical functions like probability functions are used. There are many probability functions but the Weibull distribution function is usually considered the most useful function for wind speed data analysis due to its simplicity and good accuracy. The Weibull probability density function is defined with two parameters of k and c as follows: (1) f (v) = k/(c ) 〖( v/c )〗^(k-1) exp (- 〖( v/c )〗^k )
After calculating the Weibull function parameters, status of a location for wind energy potential can be assessed. A good way to assess the available wind resources is by calculation of the wind power density. This parameter indicates how much energy can be converted to electricity at a site and can be calculated as follows: (2) P/A=1/2 ρc^3 Г ( (k)/k)
Wind energy density expresses the wind power density for a given time period T.The wind energy density for a definite site and in a given time period (one month or one year) (T) can be calculated as: (3) E/A=1/2 ρc^3 Г ((k)/k) T
In this study, wind speed data collected in Parsabad, Iran, over a ten-year period (2005-2015) are analyzed, and the Weibull distribution parameters c and k, average wind speed, and average wind power and wind energy densities are determined.
According to Table 1, the minimum and maximum standard deviations of the average monthly indicators during 11 years in November and July are 0.63 and 2.51, respectively, and the minimum and maximum wind speeds of the average monthly indicators during 11 years in November and June are 2.09 ms-1 and 4.87 ms-1, respectively. The average annual Weibull scale parameter (c) is 3.84 while the average annual Weibull shape parameter (k) is 2.61. The average annual wind power density (P/A) during 11 years is 45 Wm-2, while the average annual wind energy density (E/A) during 11 years is 389 kWhm-2/year. Pars Abad in terms of generation potential of wind energy and based on quantitative classification for wind resource is located in weak to average region.
Pars Abad with an average wind power density of 45 Wm-2 and average wind speed of 3.41 ms-1 is not a good candidate for wind power plants and it is just suitable for off-grid electrical and mechanical applications such as charging batteries and pumping water for agricultural and livestock uses.

IntroductionToday, most of the agricultural machines for doing agricultural operations and covering the entire farm, must move in the farm, and travel a certain distance without doing anything useful. Common agricultural machines are controlled by human beings using habits, machinery models, and personal experiences without using computer-based tools. This trend leads to repetitive patterns and affect farm efficincy. Therefore, applying optimization techniques in determining the optimum pattern and track for on-farm machinery would be very effective.
One of the main problems of conventional movement patterns on farms is the time wasted on moving towards the end of the field, which will have a big impact on field efficiency. The purpose of this study is to reduce the useless distance traveled by agricultural machines using genetic algorithm while moving on the farm and going from one track to the next, and, consequently, increase farm efficiency.
Materials and MethodsIn this study, the rectangle farm that was 80 meters wide and had an arbitrary length was selected for simulation, and different types of turning methods were tested. The calculations and simulation were based on genetic algorithm using the MATLAB 2013 software. In this case, the minimum traveled distance was set as solution evaluation criterion. The solutions were applied and simulated according to these assumptions: Each gene was considered a track number, and the algorithm’s chromosomes were produced by connecting all the tracks to each other,. The width of each track was considered equal to the width of the machine, and based on reproduction parameters such as population size and the number of repetitions, a percentage of the children were produced through point intersection and another percentage were produced through mutation. In determining the distance between the tracks, Ω or T or U were used for two adjacent tracks, U was used for two tracks that had a track between them, and a longer U was used for tracks that had more than one track between them.
Based on the number of the initial population, the chromosomes that were supposed to be parents at the beginning were selected. The children produced new population was created and the above steps were repeated. During the last repetition, the best child chromosome was introduced as the answer.
In order to calculate the effects of different methods of turning on the non-working distance covered during agricultural operations, the non-working distance traveled during 5 orders of movement, including 4 traditional orders (continuous, spiral, all-around, and blocked) and 1 smart order were compared to each other.
In the continuous pattern, because movement continues in the next track at the end of each track, all the turnings are inevitably done in the Ω way, and thus a greater distance is travelled compared to the U way. In the spiral pattern, the distance travelled in turnings between different tracks on the farm is equal. In the all-around pattern, movements are done from the sides and the operation is concluded at the center of the farm; therefore, the long U method of movement is used at the end of all the tracks, and Ω turning is used for the last track at the center of the farm. In the blocked pattern, the farm is devided into two or more blocks, and the all-around movement pattern is used in each block as an independent farm. In the smart movement pattern, the beginning and ending of the agricultural operations are considered in the vicinity of the hypothetical road which, in addition to facilitating access to the road, had a significant impact on reducing the useless distance traveled on the farm.
Results and DiscussionThe final optimum pattern was compared to traditional patterns in the form of charts. The optimum pattern of movement which uses smart genetic algorithm and avoids long turning methods (such as, Ω and T) leads to reduced wasted time and distance traveled by agricultural machines and increased field efficiency and also, decreasing the non-working traveled distance and waste time approximately, 45 % and 47 % respectively. This is due to avoiding turning methods of Ω and T (compared to the U method). Also, the fatigue resulting from these approaches and their wasted time is greater than U method used in the genetic algorithm pattern.
ConclusionsThe optimum pattern of movement which uses smart genetic algorithm was compared to conventional patterns that showed significant saving in non- working distance and waste time in farm. This optimum pattern can be implemented in automatic navigation but there is the possibility of its implementation by operators in common navigation.

IntroductionSoil compaction is one of the most destructive effects of machine traffic. Compaction increases soil mechanical strength and reduces its porosity, plant rooting and ultimately the yield. Nowadays, agricultural machinery has the maximum share on soil compaction in modern agriculture. The soil destruction may be as surface deformation or as subsurface compaction. Any way machine traffic destructs soil structure and as result has unfavorable effect on the yield. Hence, soil compaction recognition and its management are very important. In general, soil compaction is the most destructive effect of machine traffic. Modeling of ecological systems by conventional modeling methods due to the multitude effective parameters has always been challenging. Artificial intelligence and soft computing methods due to their simplicity, high precision in simulation of complex and nonlinear processes are highly regarded. The purpose of this research was the modeling of soil compaction system affected by soil moisture content, the tractor forward velocity and soil depth by multilayer perceptron neural network.
Materials and MethodsIn order to carry out the field experiments, a tractor MF285 which was equipped with a three-tilt moldboard plough was used. Experiments were conducted at the Agricultural research field of University of Mohaghegh Ardabili in five levels of moisture content of 11, 14, 16, 19 and 22%, forward velocity of 1, 2, 3, 4 and 5 km/h, and soil depths of 20, 25, 30, 35 and 40 cm as a randomized complete block design with three replications. In this study, perceptron neural network with five neurons in the hidden layer with sigmoid transfer function and linear transfer function for the output neuron was designed and trained.
Results and DiscussionField experiments showed three main factors were significant on the bulk density (P

Growth of population, deficiency of resources, environmental hazards, fast spatial science progress and relevant subjects have resulted in significant effects of enhanced accuracy and modern technologies in agricultural technology and management methods. One of the modern technologies’ utilities in production and nondestructive tests is determination of product characteristics (such as product height), using electronic sensors at different stages of plant growth. In recent years, electric sensors improved widely in farm science. Regarding to wide performance of sensors, from simple sensors such as thermo, light and moisture sensors, to complex ones such as GPS and lidar, also the ability of electronic sensors to exact identification and measurement of special farm properties, makes these sensors to an important part of precision agriculture. The subject of this study is to identify and measure the height of the product using ultrasonic technology to automate control of breeding and harvesting operations. Suitable price and noise and dust resistance of ultrasonic sensor, make it an attractive subject in biosystem industries and farm operations.
Plant height measurement Ultrasonic sensor includes an ultrasonic transmitter and receiver with more than 20 kHz frequency. As other waves, ultrasonic waves diffuse constantly from a source by mechanical distracting in a gas, liquid or solid environment. The distance between sensor and object is a function of the wave passing time from generation point to receive point. Plant height calculated by estimating this distance and minus it from sensor height. The sensor used in this research had a diffuse angle of 40 degrees to center axis of source. The sensor ability to height measurement depends on leaves angle, leaves surface, plant aggregation in area and plant height. Leaves angle is the most important factor in recognization ability of ultrasonic sensor.
Electronic system design: The height measurement electronic system includes: 40 kHz Ultrasonic transmitter with diameter of 10 mm, 67 db ultrasonic receiver, Signal amplifier circuit (op-amp), AVR Microcontroller, (atmega 128) and a 64×128 pi LCD. Electronic part of system produces 40 kHz pulse initially and locates on one of the outlet bases of microcontroller. Then, this pulse is amplified and sent to ultrasonic sensor transmitter for maximum performance of the transmitter. The received pulse has low power so it shoud be amplified by an amplifier to be recognizable by the microcontroller. The received signal transmitted to digital signal by a high-speed 128 AVR atmega microcontroller. The sensor calibrated in the first phase using artificial barriers, the data analyzed by linear regression and paired mean comparison test in SPSS and EXCEL software.
Corn height measured by designed system in a test by 100 plots and 10 blocks. Thus, the blocks had a dimension of 1m length and 10cm width. System output recorded in first block and the block length passed by system with 10cm distances. Actual measurement accuracy comprised as pixels to data from manual measurement. The results didn’t show any significant difference between means. The regression coefficient of model was calculated 99%. The operating phase continued in a lab to measure maize height. The results showed high linear correlation between ultrasonic output voltage and manual measurement. This linear correlation led to present a linear regression model with the regression coefficient of 95%. Correlated mean comparison used for all of data too, i.e. the data obtained by the two measurement methods were compared by t-paired test. So it’s defensible that with 99% confidence, sensor can estimate the real value of height with high accuracy.
Utilization of measurement technologies and accuracy enhancement in agricultural production systems are unavoidable. In this research, corn height was measured accurately by ultrasonic technology. According to the results, identifying the presence or absence of plant, precision control of the operations (e.g. spraying) and measuring the height of the plants (to set the cutting height at combine harvester). Obviously, the produced device can identify plant height with precision, and can use in different phases of precision agriculture such as seeding row identification, machinery path determination to minimize plant’s loss, poison optimization and harvesting.

In recent years, development of automatic guidance systems in machines led to exceeding enthusiasm for route planning using computational tools for artificial intelligence. Farms have different and various shapes and plans. Since the route length is not equal throughout a triangle-shaped field, it is more difficult to achieve a proper moving pattern in comparison with tetrahedral fields. The aim of this study is to simulate the optimal online route planning to reduce the unhelpful distance and time during performing the operations. In this paper, using genetic algorithms and Matlab 2013 software, an optimal model for moving unmanned farm machinery in rectangular farm simulated and finally this optimal pattern was compared with traditional patterns in the form of diagram. The simulation results showed that the optimal model with avoiding long turning methods is able to save approximately 51% in non-working distance and 54% in wasted time compared to the traditional traffic patterns.

Penetration resistance is one of the criteria for evaluating soil compaction. It correlates with several soil properties such as vehicle trafficability, resistance to root penetration, seedling emergence, and soil compaction by farm machinery. Direct measurement of penetration resistance is time consuming and difficult because of high temporal and spatial variability. Therefore, many different regressions and artificial neural network pedotransfer functions have been proposed to estimate penetration resistance from readily available soil variables such as particle size distribution, bulk density (Db) and gravimetric water content (θm). The lands of Ardabil Province are one of the main production regions of potato in Iran, thus, obtaining the soil penetration resistance in these regions help with the management of potato production. The objective of this research was to derive pedotransfer functions by using regression and artificial neural network to predict penetration resistance from some soil variations in the agricultural soils of Ardabil plain and to compare the performance of artificial neural network with regression models.
Disturbed and undisturbed soil samples (n= 105) were systematically taken from 0-10 cm soil depth with nearly 3000 m distance in the agricultural lands of the Ardabil plain ((lat 38°15' to 38°40' N, long 48°16' to 48°61' E). The contents of sand, silt and clay (hydrometer method), CaCO3 (titration method), bulk density (cylinder method), particle density (Dp) (pychnometer method), organic carbon (wet oxidation method), total porosity(calculating from Db and Dp), saturated (θs) and field soil water (θf) using the gravimetric method were measured in the laboratory. Mean geometric diameter (dg) and standard deviation (σg) of soil particles were computed using the percentages of sand, silt and clay. Penetration resistance was measured in situ using cone penetrometer (analog model) at 10 replicates. The data were divided into two series as 78 data for training and 27 data for testing. The SPSS 18 with stepwise method and MATLAB software were used to derive the regression and artificial neural network, respectively. A feed forward three-layer (8, 11 and 15 neurons in the hidden layer) perceptron network and the tangent sigmoid transfer function were used for the artificial neural network modeling. In estimating penetration resistance, The accuracy of artificial neural network and regression pedotransfer functions were evaluated by coefficient of determination (R2), root mean square error (RMSE), mean error (ME) and Akaike information criterion (AIC) statistics.
The textural classes of study soils were loamy sand (n= 8), sandy loam (n= 70), loam (n= 6) and silt loam (n= 21). The values of sand (26.26 to 87.43 %), clay (3.99 to 17.34 %), organic carbon (0.3 to 2.41 %), field moisture (4.56 to 33.18 mass percent), Db (1.02 to 1.63 g cm-3) and penetration resistance (1.1 to 6.6 MPa) showed a large variations of study soils. There were found significant correlations between penetration resistance and sand (r= - 0.505**), silt (r= 0.447**), clay (r= 0.330**), organic carbon (r= - 0.465**), Db (r= 0.655**), θf (r= -0.63**), CaCO3 (r= 0.290**), total porosity (r= - 0.589**) and Dp (r= 0.266*). Generally, 15 regression and artificial neural network pedotransfer functions were constructed to predict penetration resistance from measured readily available soil variables. The results of regression and artificial neural network pedotransfer functions showed that the most suitable variables to estimate penetration resistance were θf, Db and particles size distribution. The input variables were n and θf for the best regression pedotransfer function and also Db, silt, θf and σg for the best artificial neural network pedotransfer function. The values of R2, RMSE, ME and AIC were obtained equal to 0.55, 0.89 MPa, 0.05 MPa and -14.67 and 0.91, 0.37 MPa, - 0.0026 MPa and -146.64 for the best regression and artificial neural network pedotransfer functions, respectively. The former researchers also reported that there is a positive correlation between penetration resistance with Db and a negative correlation between penetration resistance with θf and organic carbon.
The results showed that silt, standard deviation of soil particles (σg), bulk density (Db), total porosity and field water content (θf) are the most suitable readily available soil variables to predict penetration resistance in the studied area. According to the RMSE and AIC criteria, the accuracy of artificial neural network in estimating soil penetration resistance was more than regression pedotransfer functions in this research.

Energy management is one of the main ways of the efficient use of energy resources. The prediction of crop yields based on energy inputs can help farmers and policymakers to estimate the level of production. Required data for study were randomly collected from 70 broiler farms in North West of Iran. The input energies were included human labour, machinery, fuel, feed and electricity and the output produced energies were considered as output variables. The multi-layer perceptron (MLP) and the radial basis function (RBF) neural networks were applied for prediction of output energies of broiler production. According to the comparison results obtained from the indices of the coefficient of determination (R2), root mean square error (RMSE) and the mean absolute error (MAE) performance of the ANN-RBF model better than ANN-MLP model was estimated. In evaluating the effects of inputs on outputs of production, the production of fossil fuel showed the highest sensitivity among the production inputs in both models.

Production systems in bio-Industries rest on resources management and conversion of various forms of energy. This research is investigated and modelled the energy of broiler production in a non-parametric form. The studied samples consist of 70 broiler productions in Ardabil province which were randomly selected from statistical society of the region. In this study, the equivalent energy consumption was estimated in the mentioned industry and the energy indices were calculated, then the equivalent amount of output energy and performance of system were modelled and estimated, using the artificial neural network models. Based on the obtained results, the total equivalent energy of input and output in broiler production were calculated as 153.79 and 27.45 GJ per 1000 birds, respectively. The most consumable input energy in the broiler production of region belongs to the fossil fuel with 61.48% of the total equivalent energy. Based on the results of artificial neural network, the best structure for modelling of energy consumption of broiler production was estimated 5-14-2 structure with five inputs, one hidden layer with 14 neurons and one output layer with two output parameters. The determination coefficient of the best weight combination for estimating the equivalent energy of broiler and manure production for testing data were obtained 93% and 91%, respectively and for validation data were obtained 98% and 95%. In assessing the effectiveness of inputs on the outputs, the fossil fuel showed the highest sensitivity among the production inputs that reveals the needs for revision of the energy resources more than ever.

Direct measurement of soil physical properties is time consuming, costly and sometimes unreliable because of soil heterogeneity and experimental errors. Stability of aggregates could be estimated from surrogate data such as soil texture, bulk density, organic carbon and CaCO3 using pedotransfer function (PTF).The objective of this research was to present regression PTFs and artificial neural network models to predict mean weight diameter (MWD) of aggregate from limited sets of soil properties and to assess the efficiency of the presented models to predict the MWD with the statistical criteria including the coefficient of determination (R2) and root mean square deviation (RMSE). In total, 100 soil sample were collected from Ardabil plain and analyzed for their physicals and chemicals properties. Soil samples were divided into two groups, so that, 80 samples were used for the development and remaining 20 samples for the validation of PTFs. The values of R2 and RMSE for regression PTFs and artificial neural networks were, respectively, 0.88, 0.42 for neural networks and 0.81, 0.054 for regression PTF. Results showed that two methods could be applied to predict the MWD in Ardabil plain. However, artificial neural networks performed better than regression model in this study.

Farm information management systems provide proper decisions by collecting precise locative data of the farm in a non-destructive and immediate form at any time. Nowadays, laser is used in a wide range of industries such as target equipment’s and exact instruments. The research was designed and performed and evaluated to identify and measure crop plants height in a non-destructive form using the laser technology. After designing and building, the laser system was tested in two field and laboratorial stages. The first stage of measurement was performed on the rice in a laboratorial environment which the collected data were analyzed by linear regression method (repression coefficient %95) and average comparison after calibration that represented sufficient care of the system in identifying the product height. In the second stage, it was used the laser system to measure the wheat height on the farm which according to the obtained results, it wasnt observed significant statistical difference between the height data measured in meter and the system data. According to the obtained results, the laser sensor system is recommended as a suitable and with high precision method for kinds of product locative identification operations and identifying the height of various products in precise agriculture.