Parameterization of Food Wastes to Develop an Automatic Recycling System for Livestock and Poultry Feed

Food wastes are known as one of the big concerns in urban management because of grain consumption gain, environmental pollution, and traditional waste management methods. The reuse of restaurant waste can reduce the cost of producing animal food production. This study attempts to find related parameters to use in the development of an automatic recycling machine and also a suitable method for food waste management (wastes of restaurants in universities and other academic environments) to use in various animal diets. Determination of various parameters including the percentage of dry matter (using a dryer), protein (using Kjeldahl test), fat (using Soxhlet extractor), and energy (using Calorimeter bomb test) were done in this research. Relevant parameters were also extracted from common diets used in livestock and poultry feed and then compared with the parameters obtained from the wastes. The results showed the average value of dry matter in different diets by 82.89% is three times more than this parameter in extracted food by 29.42%. The protein percentage, fat percentage, and energy value in extracted food (25.59%, 13.26%, and 4.41 cal/Kg, respectively) is sufficient to use in different diets. The average value of the protein percentage, fat percentage, and energy value in different diets is 23.75%, 4.27%, and 3.50 cal/Kg, respectively. The archived results indicated that it is possible to use processed food waste in livestock and poultry diets and these substances can be a good alternative to some of the diets. The output of this research will use in developing a sustainable waste material recycling system. Finally, the extracted parameters are used in designing a recycling system

Energy use pattern in production of Sugar Beet in west Azerbaijan province of Iran

With regard to the limitation of energy resources, especially non-renewable sources and increasing trend of energy consumption in agriculture, energy management in this sector is important. The purpose of this study was assessing energy productivity, input and output energy, energy efficiency and output-input energy ratio of sugar beet production in west Azerbaijan province of Iran. To achieve these objectives, statistical data about cultivation area, sugar beet yield in 2010 were acquired from the agricultural research center of west Azerbaijan province. Also data about cultivation methods, implements and machinery in use were obtained from sugar beet farmers by questionnaire. According to the results, total energy consumption in sugar beet production was 52268.72 MJ/ha, output energy was 722400 MJ/ha, energy output-input ratio was 13.8, net energy was 670131.28 MJ/ha and energy productivity was 0.82 Kg/MJ. The major energy consumers were chemical fertilizers with 34% of total input energy, irrigation (22%), implements and irrigation equipment manufacturing (12.84%) and spraying (7%), respectively. Approximately 29.48% of total input energy used in sugar beet production was direct energy and the remaining of 70.42% was indirect

Energy efficiency improvement for broiler production using non-parametric techniques

The goal of this study was to evaluate the sustainability and efficiency of broiler production with regard to energy consumption in Ardabil province, Iran. To reach the goal, linear programming model and Data Envelopment Analysis (DEA) were employed.  Data were collected from the farmers using a face–to–face questionnaire performed in September–December 2014 period.  The DEA application results showed that the average values of technical, pure technical and scale efficiency scores of producers were 0.949, 0.988 and 0.960, respectively.   Also, energy saving target ratio for broiler production was calculated as 8.33%, indicating that by following the recommendations resulted from this study, about 12316.85 MJ/(1000 bird) of total input energy could be saved while holding the constant level of broiler production.  The results of linear programming model revealed that by using of optimum energy, producers could increase average yield by 17.6%.   Also the results indicated that the existing productivity level could be achieved even by reducing the existing energy use level by 13.89%.  Diesel fuel, natural gas and electricity energy inputs had the highest potential for saving energy in two methods; so, if inefficient producers would pay more attention towards these sources, they would considerably improve their energy productivity

Weed Classification for Site-Specific Weed Management Using an Automated Stereo Computer-Vision Machine-Learning System in Rice Fields

Producción CientíficaSite-specific weed management and selective application of herbicides as eco-friendly techniques are still challenging tasks to perform, especially for densely cultivated crops, such as rice. This study is aimed at developing a stereo vision system for distinguishing between rice plants and weeds and further discriminating two types of weeds in a rice field by using artificial neural networks (ANNs) and two metaheuristic algorithms. For this purpose, stereo videos were recorded across the rice field and different channels were extracted and decomposed into the constituent frames. Next, upon pre-processing and segmentation of the frames, green plants were extracted out of the background. For accurate discrimination of the rice and weeds, a total of 302 color, shape, and texture features were identified. Two metaheuristic algorithms, namely particle swarm optimization (PSO) and the bee algorithm (BA), were used to optimize the neural network for selecting the most effective features and classifying different types of weeds, respectively. Comparing the proposed classification method with the K-nearest neighbors (KNN) classifier, it was found that the proposed ANN-BA classifier reached accuracies of 88.74% and 87.96% for right and left channels, respectively, over the test set. Taking into account either the arithmetic or the geometric means as the basis, the accuracies were increased up to 92.02% and 90.7%, respectively, over the test set. On the other hand, the KNN suffered from more cases of misclassification, as compared to the proposed ANN-BA classifier, generating an overall accuracy of 76.62% and 85.59% for the classification of the right and left channel data, respectively, and 85.84% and 84.07% for the arithmetic and geometric mean values, respectively

Determining and analyses Economic useful Life for Agricultural tractors in the West Azerbaijan province

Estimating useful life of agricultural machines is the main factor in applications of these devices. Decision making about replacement of used farm equipment is one of the important items in farm machinery management. Repair and maintenance costs are related to tractor age and using hours. The useful life for all agricultural machines set was produced the tables in developed countries. This study was conducted to estimate useful life for agricultural tractors to estimate the repair and maintenance cost and suitable mathematical model in the West Azerbaijan province. The popular model in this area includes three models Massey Ferguson 285, Massey Ferguson 399 and universal U650. Obtained data was including, repair and maintenance costs, annual usage in the hours and initial purchase price. Amount of accumulated depreciation and capital and profits as accumulated capital costs and maintenance tractors calculated for each model and economic life of tractor, respectively

Estimating Penetration Resistance in Agricultural Soils of Ardabil Plain Using Artificial Neural Network and Regression Methods

Introduction: 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. Materials and methods: 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. Results and discussion: 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. Conclusion: 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 use pattern and optimization of energy required for broiler production using data envelopment analysis

A literature review shows that energy consumption in agricultural production in Iran is not efficient and a high degree of inefficiency in broiler production exists in Iran. Energy consumption of broiler production in Ardabil province of Iran was studied and the non-parametric method of data envelopment analysis (DEA) was used to analyze energy efficiency, separate efficient from inefficient broiler producers, and calculate wasteful use of energy to optimize energy. Data was collected using face-to-face questionnaires from 70 broiler farmers in the study area. Constant returns to scale (CCR) and variable returns to scale (BCC) models of DEA were applied to assess the technical efficiency of broiler production. The results indicated that total energy use was 154,283 MJ (1000 bird)−1 and the share of fuel at 61.4% was the highest of all inputs. The indices of energy efficiency, energy productivity, specific energy, and net energy were found to be 0.18, 0.02 kg MJ−1, 59.56 MJ kg−1, and −126,836 MJ (1000 bird)−1, respectively. The DEA results revealed that 40% and 22.86% of total units were efficient based on the CCR and BCC models, respectively. The average technical, pure technical, and scale efficiency of broiler farmers was 0.88, 0.93, and 0.95, respectively. The results showed that 14.53% of total energy use could be saved by converting the present units to optimal conditions. The contribution of fuel input to total energy savings was 72% and was the largest share, followed by feed and electricity energy inputs. The results of this study indicate that there is good potential for increasing energy efficiency of broiler production in Iran by following the recommendations for efficient energy use

Modeling of Soil Compaction Beneath the Tractor Tire using Multilayer Perceptron Neural Networks

Introduction Soil 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 Methods In 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 Discussion Field experiments showed three main factors were significant on the bulk density (

Potential Assessment of Wind Power as a Source of Electricity Production in the City of Parsabad, Iran

Introduction 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. Materials and Methods 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+3)/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+3)/k) T Results and Discussion 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. Conclusions 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