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 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

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

Life cycle assessment of biodiesel production from fish waste oil

Biodiesel is a renewable and environmentally friendly alternative to traditional diesel fuel. It can be used in existing diesel engines without any modifications. Produced from various sources like vegetable oils, animal fats, or recycled restaurant grease, biodiesel emits fewer pollutants during combustion compared to petroleum diesel. However, it is crucial to assess its overall environmental impact throughout the production process. This study focuses on conducting a life cycle assessment of biodiesel production from fish waste oil using the trans esterification method. The environmental consequences were evaluated in seven categories using SimaPro software. To produce one kilogram of biodiesel from fish waste oil, the process consumes 344 watt-hours of electricity, 218.42 grams of methanol, and 10 grams of potassium hydroxide. The results of the assessment reveal the environmental impacts associated with the life cycle of producing one kilogram of biodiesel from fish waste oil. These impacts include acidification (119.28 grams of SO2 equivalent), global warming potential (119.28 kilograms of CO2 equivalent), eutrophication (5.25 grams of PO4 -2 equivalent), photochemical oxidation (84.4 grams of non-methane volatile organic compounds), ozone depletion potential (0.050075 metric tons of CFC-11 equivalent), abiotic depletion (416.2 megajoules from fossil fuels), and abiotic depletion (14.6 metric tons equivalent of antimony)

Estimation of Soil Surface Roughness Using Stereo Vision Approach

Soil roughness is one of the most challenging issues in the agricultural domain and plays a crucial role in soil quality. The objective of this research was to develop a computerized method based on stereo vision technique to estimate the roughness formed on the agricultural soils. Additionally, soil till quality was investigated by analyzing the height of plow layers. An image dataset was provided in the real conditions of the field. For determining the soil surface roughness, the elevation of clods obtained from tillage operations was computed using a depth map. This map was obtained by extracting and matching corresponding keypoints as super pixels of images. Regression equations and coefficients of determination between the measured and estimated values indicate that the proposed method has a strong potential for the estimation of soil shallow roughness as an important physical parameter in tillage operations. In addition, peak fitting of tilled layers was applied to the height profile to evaluate the till quality. The results of this suggest that the peak fitting is an effective method of judging tillage quality in the fields