Efficient harvesting and processing of peanuts requires optimal performance of threshing machines, particularly their cleaning systems, to ensure high product quality and minimal grain loss. This study investigates the influence of key operational parameters of sieve slope, sieve movement amplitude, and fan suction speed on the performance of a peanut thresher, with a focus on separation efficiency and loss rate. Experiments were conducted in Ardabil province using an Nc2 peanut threshing machine. Artificial Neural Network-based fuzzy inference systems (ANFIS) were developed to model and predict performance criteria for both threshing (threshing efficiency, pod-root separation rate, and broken pod percentage) and cleaning units (separation efficiency and peanut loss rate) under varying operational settings. Multiple membership functions (triangular, trapezoidal, Gaussian, bell-shaped) were tested, and model accuracy was evaluated using coefficient of determination (R 2) and relative error. The best predictive performance (R 2 >= 0.96, epsilon <= 4%) was achieved using bell-shaped membership functions with fixed output. Results showed that reducing thresher-concave distance and maintaining moderate thresher speed optimized threshing efficiency while minimizing pod breakage. The feed rate exhibited a stronger influence on pod damage than thresher-concave distance. In the cleaning system, suction speed was the dominant factor affecting peanut loss, with excessive speeds leading to sharp increases in loss regardless of sieve slope. Optimal operating conditions were identified that strike a balance between throughput and quality. The findings demonstrate the potential of ANFIS modeling for precision optimization of peanut threshers, enabling improved productivity and reduced post-harvest losses through intelligent parameter adjustment.

Most of tillage research focuses on designing more efficient tools to operate with low energy consumption. Also, developing tools to preserve conservation tillage is a priority for researchers. The aim of the study was to compare a paraplough with and without wings with conventional mouldboard plough in terms of loosened area, fuel consumption, and specific fuel consumption. Field trials were conducted using an MF 285 tractor at three working depths of 10, 20, and 30 cm and at three forward speeds of 2, 5, and 7 km & centerdot;h(-1) for conventional mouldboard plough, paraplough without wings, paraplough with backward-bent wings, and paraplough with forward-bent wings. Two flow sensors made by Oval Company in Japan were used to measure the fuel consumption of the tractor. The highest fuel consumption of 14.58 L & centerdot;h(-1) occurred when using the mouldboard plough and the least fuel consumption of 5.849 L & centerdot;h(-1)was obtained using the paraplough without wings. Increasing travel speed and working depth increased fuel consumption significantly. Adding both backward- and forward-bent wings to the paraplough increased the loosened area by 17.25 and 7.4%, respectively. Also, adding wings increased specific fuel consumption by 54 and 74% in comparison with the paraplough without wings. Both loosened area and specific fuel consumption parameters showed using the backward-bent wing is more effective than the forward-bent wing in terms of energy efficiency. Using the backward- and forward-bent wings decreased specific fuel consumption by 34.1 and 18.7% in comparison with the mouldboard plough, respectively. It was concluded that for soil loosening using tines with wings is more effective in terms of energy saving.

Farming tractors consume the most share of diesel fuels in agricultural sectors. As most engine tests are performed in engine laboratories, the study aims to examine the effect of biodiesel, diesel, and ethanol blends in actual farming conditions. Accordingly, the percentage of ethanol and biodiesel and the gear (forward speed) were considered the independent variables. The output variables were the slippage percentage, traction efficiency (TE), and emission factors. It was found that B2E7 (2% biodiesel including 7% ethanol) provided the lowest slippage percentage in comparison with control (about 15, 17 and 19% respectively for L1 (low gear 1), L2 (low gear 2) and L3 (low gear 3)). On average, increasing the gear level increased the slip percentage by about 8 and 14% for L2 and L3 compared to L1, respectively. Also, increasing forward speed (increasing gear from L1 to L3) reduced the traction efficiency. The maximum traction efficiency was obtained at B2E7, followed by B5E7, which were, on average, about 3 and 3.5% higher than the control, respectively. The optimization results indicated that the highest performance and the lowest emission were obtained using a fuel with a formulation of 0.2% ethanol and 4.1% biodiesel (B4.1E0.2) at L3.

In this study, 60 cherry samples with native varieties were selected from the Hir region in Ardabil province. They were classified into four growth stages, including before the optimal harvest date, the day before the optimal harvest time, the optimal harvest time, and after the optimal harvest date, by a panel of human experts. Next, by combining the feature selection method (relief) and the spectrometry method (vis-NIR), the effective wavelengths were extracted to estimate the soluble solid content (SSC) values and firmness of the cherry product. In the continuation of the process of this method, a list of inputs was formed, and by applying the life cycle assessment method, the environmental effects of the process of estimating SSC values and cherry hardness in the presence of tests and obtained data was performed. In the final stage, with the help of the radial basis function neural network method, a relationship was established between the reflection intensity values in the effective wavelengths and the endpoint effects of the life cycle assessment to estimate the environmental effects. It was found that the radial basis function neural network could estimate the environmental effects of the experimental process with an acceptable accuracy (over 95% on average).

This study evaluates the process of detecting the degree of firmness and pH of rose apples with the help of image processing from the point of view of environmental effects. The process of this study started with image processing. In image processing, the selected samples were photographed with a charge-coupled device (CCD) camera, and red (R), green (G), and blue (B) values were extracted with the image processing algorithm in MATLAB software. Next, the hardness and acidity values of the samples were extracted using laboratory steps. Next, with the inputs of each test, the life cycle assessment (LCA) list was prepared. Then, with the Impact 2002+ method, the list was subjected to life cycle evaluation, and the middle and final effects of the analyses were extracted. Next, the neural network and grey wolf optimizer (GWO) methods were used to predict environmental effects. Based on the results, it was determined that the values of R and G had the highest effect on estimating pH and the values of B and G had the highest effect on estimating the product's hardness. Also, the results of evaluating the accuracy of the artificial neural network combined with the grey wolf optimizer showed that the accuracy of the estimation of environmental effects in the evaluation of pH was about 3-5% higher than that of soluble solid content (SSC). Based on the findings, using the integrated machine learning (ML) system with image processing is a reliable method to estimate the environmental effects of detecting the quality characteristics of Iranian rose apples entirely non-destructively.

Four types of narrow tines with different widths but the same length of 1 m were analysed. Field trials were conducted in clay loam soil in a factorial experiment based on a randomized complete block design with three replications. The soil moisture was 15%, and four tractor forward speeds of 1, 1.5, 1.8, ing depths of 10, 20, 30, and 40 cm were investigated relative to the required draught. In order to investigate the effect of the depth/width ratio and the speed of the narrow tillage blade on the draught force, a three-dimensional model was prepared and tested a significant effect on increasing the stress exerted on the soil, required draught.

Evaluating the input and output process of energy in agricultural systems is one of the ways to determine the level of sustainability in these systems. For this reason, the purpose of this study was to investigate and predict the amount of input and output energies and also related indicators in cattle breeding. The information needed to conduct this study was collected by referring to Pars Company and extracting the recorded information regarding the amount of consumed inputs and the value of produced products. Also, part of the information was extracted from the financial reports presented by the company and the reports published in the annual meetings of the shareholders. In addition, the information is related to the financial period from 2011 to 2020. Based on the available data, the value of input and output energies, energy indices were calculated. Also, adaptive network-based fuzzy inference system (ANFIS) was used to predict performance and output energy. According to the results of data analysis, the average energy required for raising cattle during a one-year period was 52,604.52 MJ∙cow−1. Also, produced milk, produced manure, and born calf were considered as the output energies. The energy equivalent of produced milk during a year was 56,821.39 MJ∙cow−1. After examining different models of ANFIS, the best ANFIS model with the input membership function of trimf was selected and the R2 and MAPE values of 0.98 and 0.42 were obtained, respectively.

The research investigates the impact of farmyard manure application and tire traffic on soil compaction using a combination of soil bin tests and Discrete Element Method (DEM) simulation. Tests were conducted in the controlled condition of soil bin facility utilizing a well-equipped single-wheel tester. An agricultural tractor tire of 220/65R21 manufactured by Goodyear was used. Then DEM was employed to model soil-tire interactions, considering factors such as soil properties, tire design, and tire traffic. The DEM model was calibrated using laboratory experiments, including uniaxial compression test and repose angle measurement. The results revealed a significant and good correlation between laboratory method and the DEM through the examination of soil density variations at different depths and organic matter ratios. A comparison between the DEM and experiments revealed a strong correlation, with an R2 value of 0 9924, a correlation coefficient of 0.996, and P-value less than 0.05. It was found that increasing soil depth leads to decrease in bulk density and the application of manure resulted in loosening of the soil and subsequently significantly reducing its bulk density. DEM method showed a significant increase in density from 1 to 3 passes at the shallow depth of 10 cm, followed by a continuous increase in density from 3 to 16 passes. The DEM simulations provided valuable insights into the mechanisms of soil compaction and the effects of different management practices. This research highlights the potential of DEM as a powerful tool for studying soil-tire interactions and optimizing agricultural practices to minimize soil compaction.

In order to determine the relationships between the soil stiffness constants of cohesive modulus of deformation, friction modulus of deformation and soil constant value and the rolling resistance, a series of tests was conducted using two types of loam and clay loam soil textures at four moisture contents of 10, 20, 30 and 40% and five loading speeds of 1, 2, 3, 4 and 5 mm s -1 . The results showed that all of the independent factors had a significant effect on the soil stiffness constants, so with increases in moisture content and loading speed, the soil stiffness constants of cohesive modulus of deformation, friction modulus of deformation and soil constant value varied significantly. The highest cohesive modulus of deformation and friction modulus of deformation values were obtained at a moisture content of 10% and loading speed of 5 mm s -1 in a clay loam soil. All parameters were significant in calculating the rolling resistance using Bekkers' relationship. With increases in soil moisture content, the rolling resistance increased, while increasing the loading speed reduced the rolling resistance significantly. In general, the highest rolling resistance value of 16 887.1 N was obtained at a moisture content value of 40% and a loading speed of 1 mm s -1 in loam soil.

The effect of coating a flat blade surface with titanium nitride nano coatings (TiN), nano tantalum carbide (TaC), Fiberglass (Glass Fiber-Reinforced Polymer) (GFRP), Galvanized Steel (GAS), and St37 (SST37) was investigated in order to decrease the adhesion of soil on tilling tools, external friction and, ultimately, the draft force. The soil tank, which was filled with soil of the desired conditions, was pulled on the bearing on the rail. A S-shaped load cell was used to measure the draft force. Tests were conducted at a distance of 2 m and speeds of 0.1, 0.2, and 0.3 m·s−1 at a depth of 10 cm. A model based on input factors, including blade travel speed, rake angle, and cohesion and adhesion of soil–blade, was developed in an adaptive neuro-fuzzy inference system (ANFIS), and draft force was the output parameter. To verify the performance of the developed model using ANFIS, a relative error(ε) of 6.1% and coefficient of determination (R2) of 0.956 were computed. It was found that blades coated with Nano (TiN-TaC), due to its hydrophobic surface, flatness, and self-cleaning properties, have considerable ability to decrease adhesion in wet soils and showed a linear relationship with draft force reduction.

This study aims to investigate the effects of manure and vermicompost in terms of soil elasticity modulus changes in loam soil. The uniaxial compression test measures the compressive strength of a soil cylinder without any lateral load. The modulus of elasticity was determined by plotting the strain-stress diagrams and calculating the slope of these diagrams. The increase in moisture and organic matter content significantly improved soil flexibility and decreased its elasticity modulus. It was found that adding animal manure had a greater effect on the flexibility of the soil as compared to vermicompost, while the elasticity modulus of the samples containing animal manure was lower. The interaction effect of moisture and the organic matter content indicated that the increase in humidity was more effective in the soil containing vermicompost as compared to the soil containing manure, such that when the manure rate was around 19%, the moisture increase had no significant impact on the elasticity modulus. It was found that adding organic matter was more effective at the low moisture level of 15.5% than at higher moistures where the effect of adding organic matter did not affect the soil elasticity modulus decrement as strongly.

In-vessel composting machine with the agitating system, circulating aeration system, and heating system on vegetable and food waste with coco peat additives and biochar obtained from coco peat was investigated. The composting process was tested at 55 °C, at three fresh inlet air rates of 20%, 30%, and 50%, three initial carbon-to-nitrogen (C/N) ratios of 18, 22, 26, and the addition of coco peat biochar of 5%, 10% w.b. (wet basis). To predict compost evaluation indicators of Electrical conductivity (EC), pH, C/N & GI, artificial neural network (ANN), and neural-fuzzy inference systems were used. The evaluation of the output parameters of compost showed high efficiency of the process. The amount of EC, acidity, and GI increased for all treatments, and the C/N ratio decreased. Also, the initial C/N ratio of 22 and fresh inlet air (FIA) of 30% were considered as the optimal setting conditions of the device. Treatment containing 5% biochar in the C/N of 22 resulted in the highest germination index of 93.55%. The best values of the coefficient of determination for the output parameters of the compost production process (EC, pH, C/N & GI) in the artificial neural network were 0.9252, 0.9863, 0.9691, and 0.9909 respectively. Moreover, the best values of the coefficient of determination in the fuzzy neural inference system for the output parameters of the compost include EC, pH, C/N and GI were 0.999, 0.999, 0.994, and 0.992, respectively. Also, the lowest values of MAE and RMSE in the fuzzy neural inference system for the output parameters of the compost include EC, pH, C/N, and GI were 0.0308, 0.0001, 0.2420, and 0.003 for MAE; and 0.0021, 3.66E−05, 0.1908 and 0.0041 for RMSE, respectively.

Many factors contribute to soil compaction. One of these factors is the pressure applied by tires and tillage tools. The aim of this study was to study soil compaction under two sizes of tractor tire, considering the effect of tire pressure and traffic on different depths of soil. Additionally, to predict soil density under the tire, an adaptive neuro-fuzzy inference system (ANFIS) was used. An ITM70 tractor equipped with a lister was used. Standard cylindrical cores were used and soil samples were taken at four depths of the soil inside the tire tracks. Tests were conducted based on a randomized complete-block design with three replications. We tested two types of narrow and normal tire using three inflation pressures, at traffic levels of 1, 3 and 5 passes and four depths of 10, 20, 30 and 40 cm. A grid partition structure and four types of membership function, namely triangular, trapezoid, Gaussian and General bell were used to model soil compaction. Analysis of variance showed that tire size was significant on soil density change, and also, the binary effect of tire size on depth and traffic were significant at 1%. The main effects of tire pressure, traffic and depth were significant on soil compaction at 1% level of significance for both tire types. The inputs of the ANFIS model included tire type, depth of soil, number of tire passes and tire inflation pressure. To evaluate the performance of the model, the relative error (ε) and the coefficient of explanation (R2) were used, which were 1.05 and 0.9949, respectively. It was found that the narrow tire was more effective on soil compaction such that the narrow tire significantly increased soil density in the surface and subsurface layers.

Developing models that can accurately predict the soil bulk density using other soil parameters is of great importance given the arduous task of determining the soil bulk density. We conducted the factorial experiment based on the randomized complete block design with five replications to determine the factors that affect the soil bulk density (BD) in three types of soil texture: loam, sandy loan, and loamy sand. Artificial neural network (ANN) and adaptive neuro-fuzzy inference system (ANFIS) were applied to predict and model the soil bulk density using several independent parameters that affect it, including the soil cone index (CI), moisture content (MC), and electrical conductivity (EC). The analysis indicated that the developed ANN using the Bayesian tuning algorithm with R-2 = 0.93 is the most suitable model compared to other models that were created. We also performed the soil bulk density modeling using the three effective parameters of soil by ANFIS (adaptive neuro-fuzzy inference system) applying the hybrid method. The coefficient of determination for the ANFIS model was 0.988 (R-2 = 0.98), which indicates the correct choice of the parameters affecting the soil bulk density. The comparison between the artificial neural network models and the neuro-fuzzy model developed in this study shows the complete advantage of ANFIS systems in predicting the soil bulk density as supported by the statistical parameters The results showed that ANN and ANFIS are highly capable to predict the soil bulk density in agricultural lands.

Due to the importance of uniform seed cultivation in agricultural yields, the efficiency of two types of pneumatic cylindrical distributors were compared using multi-planting index, non-planting index, and grain deviation from the planting path for soybean cultivation in the laboratory tests by a grease belt machine. The effects of forward velocities of 0.8, 1, 1.2 and 1.5 m/s and vacuum pressures of 2, 3 and 4 kPa on the performance of both systems were evaluated. It was found that by increasing vacuum pressure inside the cylinder in a vacuum distributor, the force against the grain surface and the amount of grain adhesion to the pores increased. In a low vacuum, as the force decreased, the grain absorption in the orifice decreased and the amount of multi-plant sowing increased. By increasing speed at a constant vacuum pressure, the required force for the grain to adhere to the pores decreases, but because the difference between vacuum and gravity causes the grain to fall into the pipe, the accumulation of grains increases by hitting the pipe body and comes out of the fall pipe in batches, thus, increasing the amount of multi-plant sowing. In a pressurized cylindrical distribution, by increasing the pressure inside the cylinder, the grain falls into the falling pipe, and due to increase in air flow velocity, it leaves the falling pipe in a short time, and some multi-sowing will be reduced. Also, at constant pressure, the rate of progression increases due to the irregularity of the entry of grains into the external pipe in the multi-sowing cylinder. The amount of sowing is proportional to the reduction in the amount of filling the distributors and vice versa. The deviation of the grains from the path is significantly dependent on the geometric shape of the fall pipe and the smoothness amount of the fall pipes??? inner surface. Due to the use of the same fall pipe, no significant changes were observed in this study.

Optimal design of conveying path can be lead to decrease the reverse flow and blockage in the conveying processes. In the present study, the effects of four different initial pipe lengths of 190, 380, 570, and 760 mm in the larvae killing machine were evaluated, comprehensively. The numerical calculations were conducted by computational fluid dynamics. Also, the Discrete Phase Model (DPM) and Reynolds Stress Model (RSM) were utilized to model the solid-gas interactions and turbulence of the flow, respectively. According to the results, the minimum pressures drop resulted in configuration III with 159 Pa. A negative pressure field was created in the interior radius of the second elbow, which is due to the enhancement of velocity in this area and an enhancement in dynamic pressure. Moreover, the maximum and minimum vorticity magnitudes were obtained in configurations I and III, respectively. Finally, considering all conditions, configuration III was chosen as the best configuration. Practical Application The larvae killing machine is utilized to eliminate larvae from whet during conveying. The appropriate design of the piping system is a critical subject in this field. One of the powerful methods for designing the conveying system is computational fluid dynamics. The results of the study can reduce the pressure drop and increase the efficiency of the system and provide uniform conveying in this machine.

Cyclone is often used in the Industry due to its low maintenance costs, simple design, and ease of operation. This work presents both experimental and simulation evaluation on the effect of inlet velocity and mass flow rate on the performance of a wheat conveying cyclone. According to the great importance of the pressure drop and separation efficiency on the separation phenomenon in the cyclone, a comprehensive study has been conducted in this regard. A computational fluid dynamics (CFD) simulation was realized using a Reynolds stress turbulence model, and particle-air interactions were modeled using a discrete phase model. The result showed a good agreement between the measured value and CFD simulation on the pressure drop and tangential velocity with a maximum deviation of 6.8%. It was found that the separation efficiency increased with inlet velocity up to 16 m s−1 but decreased slightly at a velocity of 20 m s−1. The pressure drop increased proportionally with inlet velocity. However, optimum performance with the highest separation efficiency (99%) and acceptable pressure drop (416 Pa) was achieved at the inlet velocity of 16 m s−1 and mass flow rate of 0.01 kg s−1.

In this research, the interactions of a dual sideway-share subsurface tillage implement with soil were modeled by discrete element method (DEM) and evaluated in an indoor linear soil bin. Spherical particles with Hertz-Mindlin contact model and parallel bond between particles were used to simulate agricultural soil aggregates and their cohesive behaviors. In soil bin experiments, soil cutting resistance and soil disturbance characteristics resulting from the tillage tool operating at 0.25 m s-1 and 150 mm cutting depth, were measured. The calibration of bond stiffness was performed through comparing the draft forces of a simple soil engaging tool simulated with DEM and those estimated with an analytical tillage modeling results using a trial-and-error method. The ratio of soil bin measured vertical force to draft force was used in calibration of the most sensitive model parameter, particle shear modulus. Its calibrated value for a sandy clay loam soil was 50 MPa. The calibrated model was validated using the soil forces as well as the soil disturbance characteristics of the tool measured in the soil bin. When comparing the model to the experimental results, the relative error was -2.0% for the average draft force, 2.5% for the average vertical force, -7.7% for the average rupture distance, and 11.7% for the average disturbed area. Therefore, good correlations were achieved between the soil mechanical behaviors obtained by the experiments and the DEM simulations. It can be concluded that in DEM simulations of some tillage tools such as subsurface tillage implement, accurate predictions of the vertical forces as well as the draft forces, could be achieved by calibrating the particle shear modulus using the ratio of vertical force to the draft force applied to the tool.

Torsional torque is considered as one of the loads that the crankshaft is constantly transferring. This force causes torsional stress (torsional stress due to torsion) in the fixed crankshaft bearings and shear stress in the movable bearings. Due to the effect of different parameters in the balancer design, e.g. inertial forces, the mass of weights, offcenter, and distance from the axis of the crankshaft the need for extensive research in this field is sensed. The research investigates the effect of the balancer on reducing engine vibration. The main objective was to study the kinetics of the engine using Adams engine software by application experimental data. And finally comparing test results with software results to confirm created simulation. By analyzing the output signal, the root mean square (RMS) value of experimental data, the balancer reduced the amount of RMS value of engine vibration at 750 rpm in no-load mode by 40.46%, and 12.73% at the rotational speed of 2400 rpm. Accordingly, using the balancer with no-load was more effective at lower speeds. In the case of no-load mode, the use of a balancer reduced vibrations by 28.83%. It was found that in full load condition, the RMS of the engine vibrations increased with increasing engine speed. Experimental and simulated by software results were highly consistent with experimental results, which confirms the validity of the created simulation.

In the present study, imperative parameters including centrifugal force, erosion, streamline, strain rate, and wall shear are evaluated in a cyclone separator. The flaw of the cyclone surface due to erosion is an acute problem in the industry. According to the great importance of the centrifugal force on the separation phenomenon, a comprehensive study is conducted. A computational fluid dynamics (CFD) simulation is realized by applying a Reynolds stress turbulence model (RSM), and particle–air interactions were modeled using a discrete phase model (DPM). The result shows a good agreement between the experimental data and CFD simulation on the tangential velocity and pressure drop. The maximum deviation of the validation process is 6.8%. It is found that the centrifugal force within the cyclone is increased with an enhancement in the inlet velocity. The separation efficiency indicates an increase–decrease treatment in various inlet velocities with inlet velocity up to 16 m⋅s−1 but decreases slightly at a velocity of 20 m⋅s−1. The pressure increases proportionally with inlet velocity. The best performance with the highest separation efficiency (99%) and pressure drop (416 Pa) obtains at the inlet velocity of 16 m⋅s−1 and mass flow rate of 0.01 kg⋅s−1. In addition, the maximum erosion rate was created in the entrance and conical part of the cyclone.