Journal of Researches in Mechanics of Agricultural Machinery

Abstract Introduction

Walnut (Juglans regia L.), a member of the Juglandaceae family, is one of the most important temperate nut crops widely cultivated worldwide, particularly in Iran, which is considered one of its primary centers of origin due to high genetic diversity (Aslamarz et al., 2009). Walnut kernels are rich in bioactive compounds, including unsaturated fatty acids (oleic, linoleic, and α-linolenic acids), phenols, tocopherols, phytosterols, and vitamins A, E, and C, contributing significantly to their antioxidant and nutritional value (Venkatachalam & Sathe, 2006; Hamidi et al., 2015). However, the presence of heavy metals such as iron and copper can accelerate oxidation, reducing shelf life (Belitz & Grosch, 1987). Consequently, the drying method plays a critical role in preserving chemical composition and kernel quality (Amini Rastabi & Mirzaey, 2018).

In Iran, walnuts are traditionally sun-dried either with the hard shell intact or as dehulled kernels. Direct exposure to sunlight, varying temperature, and oxygen contact can alter physical and chemical properties, including mineral content (Ozkan & Koyuncu, 2005; Martinez & Maestri, 2008). Previous studies indicate that drying method and kernel color influence mineral elements such as phosphorus, potassium, sodium, iron, zinc, copper, and manganese (Hamidi et al., 2015; Huang et al., 2014). Despite scattered research, comprehensive data on the direct impact of traditional drying methods on walnut mineral changes under Iranian climatic conditions remain limited.

The present study aimed to evaluate the effects of two traditional sun-drying methods (with hard shell vs. dehulled kernels) on mineral element concentrations in walnut kernels and identify the optimal traditional approach for maintaining nutritional and chemical quality.



Material and Methods

Walnuts of a local cultivar from Tuyserkan, Hamadan Province, Iran, were harvested from a single orchard at uniform maturity. Samples included 3 kg of in-shell walnuts and 1 kg of kernels. Drying was performed naturally in open air under direct sunlight for two consecutive days (average daily temperature 27–31°C, relative humidity 25–30%) in September, achieving final kernel moisture of 6–8% (Ozkan & Koyuncu, 2005). Post-drying, kernels were categorized by color into white (grade 1), amber (grade 2), and brown (grade 3).

Residual moisture was removed by oven-drying at 70°C for 24 h (Memmert UN55). Kernels were ground, and 1 g samples were ashed at 500°C for 4 h. Ash was digested in HCl:HNO₃ (1:3), filtered, and diluted to 50 mL. Potassium and sodium were measured by flame photometry (Jenway PFP7); phosphorus, iron, manganese, copper, and zinc by atomic absorption spectrophotometry (Shimadzu AA-7000). All analyses were conducted in triplicate. Data were analyzed using one-way ANOVA and Duncan's test in SPSS version 26 (p < 0.05).

Results and Discussion

Statistical analysis revealed significant effects of drying method on several mineral elements. Drying with the hard shell generally preserved higher mineral concentrations compared to drying dehulled kernels.

Phosphorus content was significantly higher (p = 0.037) in shell-dried walnuts (mean 2023 ± 27 mg/kg) than in kernel-dried samples (1883 ± 26 mg/kg). Similar trends were observed across color grades, with the highest values in amber kernels dried with shell.

Potassium showed the most pronounced difference (p = 0.033), with shell-dried samples averaging 3884 ± 45 mg/kg versus 3435 ± 42 mg/kg in kernel-dried walnuts. This aligns with Ozkan & Koyuncu (2005), who reported that direct exposure to light and air reduces alkali elements like potassium through leaching or oxidation.

Sodium levels exhibited no significant difference overall (p = 0.081), averaging 31.9 ± 0.09 mg/kg (shell-dried) and 24.7 ± 0.07 mg/kg (kernel-dried), indicating relative stability during sun-drying.

Iron concentration was significantly higher (p = 0.044) in shell-dried walnuts (2.83 ± 0.08 mg/kg) than kernel-dried (1.86 ± 0.07 mg/kg), likely due to the protective role of the shell against oxidation (Guinda, 2003).

Zinc showed no significant difference (p = 0.61), with means of 0.41 ± 0.01 mg/kg (shell-dried) and 0.43 ± 0.01 mg/kg (kernel-dried), suggesting greater thermal and light stability (Venkatachalam & Sathe, 2006).

Copper was significantly preserved in shell-dried samples (0.35 ± 0.01 mg/kg vs. 0.26 ± 0.01 mg/kg; p = 0.031), with losses in kernel-dried walnuts attributed to accelerated oxidation of copper-containing compounds (Martinez & Maestri, 2008).

Manganese displayed no significant difference (p = 0.29), though darker kernels tended to accumulate higher levels, consistent with Huang et al. (2014). Coefficient of variation analysis indicated greater variability in kernel-dried samples for most elements, reflecting less uniform mineral retention.

Overall, the hard shell acted as a barrier against direct sunlight, oxygen, and temperature fluctuations, reducing mineral degradation or leaching during traditional sun-drying.

Conclusions

The study demonstrated that potassium was the most abundant mineral in Tuyserkan walnuts, followed by phosphorus, while copper was present in the lowest concentration. Drying walnuts with the hard shell intact under sunlight significantly preserved higher levels of potassium, phosphorus, iron, and copper compared to drying dehulled kernels (p < 0.05), owing to the shell's protective effect against oxidation and direct environmental exposure. Zinc, sodium, and manganese remained relatively stable across methods.

Darker kernels generally contained more manganese and potassium, while lighter kernels were richer in iron. Given its simplicity, accessibility, and superior retention of nutritional quality, sun-drying with the hard shell represents the most effective traditional method under natural conditions. This approach can enhance shelf life, commercial value, and export potential of Iranian walnuts by maintaining superior chemical and nutritional properties.



Acknowledgements

Author Contributions

Conceptualization, methodology, investigation, and writing: research team (as per original Persian study).

Data Availability Statement

The data supporting the findings of this study are available within the article (tables and figures).

Ethical Considerations

This study involved no human or animal subjects; ethical approval was not required.

Conflict of Interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper

Funding Statement

The author(s) received no specific funding for this research.

Abstract An electronic slip control system was designed and installed on MF285 tractor and its performance was evaluated under field conditions. In this system, a rotary encoder was used to measure the ground speed and a proximity sensor to measure the theoretical forward speed for calculation of drive wheel slip. The output signals from the sensors were transmitted to the programmable logic controller (PLC). After calculating the wheel slip and comparing it with the set value, the PLC computed the error value and send a command to a stepper motor, accordingly. The stepper motor rotated the control valve and then the three-point linkage system changed the depth of the plow in order to keep the wheel slip fixed in the allowable range.

The results of sensor calibration showed that the measurement error of slippage was about 2 percent in the field condition. Field experiments were conducted at the two levels of control system (electronic and mechanical), the four levels of ground speed (2.5, 3.5, 4.5 and 6.2 km/h), the three levels of the set values (slippage for electronic system including 1: 10%, 2: 15% and 3:20%, the position levels of draft control lever for mechanical system including: 1: low, 2: medium and 3: high) in a sandy–loam soil with a randomized complete block design. Results of statistical analyses showed that the mean of slippage in the electronic control system was decreased 35, 43 and 49 percent at three set levels of 1, 2 and 3 respectively compared with the mechanical system. The decrease in wheel slip in electronic control system caused reducing of fuel consumption 11, 31 and 36 percent at the set values of 1, 2 and 3 respectively. The results also showed that there was a significant difference between fuel consumption of both systems at the similar speeds.

Introduction

The main purpose of agricultural tractors is to perform traction work, which is defined based on pull and travel speed. Research shows that about 20 to 55 percent of the available tractor energy is wasted at the tire-soil contact surface, due to drive wheel slippage and rolling resistance. When using tillage implements, drive wheel slippage is necessary for the tractor to apply traction force. Many researchers have reported that in order to achieve maximum traction efficiency, in addition to adding appropriate weighting to the tractor, the wheel slippage should be controlled within the range of 8 to 15 percent.

Nowadays, most agricultural tractors, especially domestically manufactured tractors, have a hydro-mechanical draft control system. In this system, the compressive force in the upper arm or the draft force in the lower arms of the tractor is used as a control parameter. The response of hydro-mechanical systems to changes in soil conditions is weak (slow). The reason for this can be stated as follows: the spring experiences hysteresis (backlash) during the loading and unloading process, which leads to different rates of raising or lowering the tools in the soil. Also, there is no mechanism to adjust the lifting sensitivity of the hydro-mechanical system.Therefore an electronic slip control system with appropriate accuracy, low cost, and compatibility with domestically manufactured tractors was designed, developed, and installed on the MF285 tractor.

Material and Methods

An electronic slip control system, using a proportional control valve of the rotary type and with the ability to be controlled by a stepper motor, was designed and installed on a Massey Ferguson model 285 tractor. This system includes theoretical and actual tractor speed sensors, a controller unit, a display, a control valve, a stepper motor, and a stepper motor driver. The programmable logic controller (PLC) consists of a CPU unit model CP1L-J14D and an analog input unit model CP1W-AD041. A touch screen manufactured by Omron, model NB5Q-TW00B, was used to display the measured values and enter the set values. The control valve is installed at the outlet of the hydraulic pump, and its design is such that it provides three modes: liftting, neutral, and lowering. A Sanyo stepper motor with a torque of 13 kg/cm and a two-phase driver was used to rotate the control valve. The fifth wheel method was used to measure the forward speed. A 100-pulse encoder shaft sensor was used to measure the rotational speed of the fifth wheel. A magnetic pick-up sensor sensitive to iron was used to measure the rotational speed of the drive wheels.

Results and Discussion

The results of field experiments showed that there is a significant decrease between the averages of the slippage in the electronic system compared with the mechanical system in all similar set values, and this indicates that the mechanical control system has performed poorly in controlling the percentage of slip compared to the electronic control system. Because there is no supervision on the slippage of the drive wheels in the mechanical control system.

With increasing set values, regardless of the type of control system, the percentage of slip has increased, but the slope of the increase is different in the two systems. Given that increasing the set values causes the required traction force for the plow to increase, increasing the draft force increases the slippage of the drive wheels.

Comparing the average fuel consumption in different systems and speed treatments using the LSD test at the 5% level showed that the mechanical control system has the highest fuel consumption at the minimum travel speed, and the electronic control system has the lowest fuel consumption at the maximum forward speed. It is also clear from the graph that by increasing the speed in both control systems, the amount of fuel consumed has decreased. There is also a significant difference at the 5% level between the averages of two systems at all speeds, and using the electronic system has saved fuel consumption by 20%, 26%, 40%, and 32% at speeds of 2.5, 3.5, 4.5, and 6.2 km/h, respectively. There is a significant difference at the 5% probability level between the fuel consumption values of the two systems in all set values, so that the electronic system has reduced fuel consumption compared to the mechanical system. Also, by increasing the set values, regardless of the type of system, the amount of fuel consumed has increased.

Conclusions

1- The calibration results showed that the sensors of the system for measuring the percentage of slip have good accuracy. Also, there was a linear relationship with a high coefficient of determination between the percentage of slip measured by the system and calculated by the formula on the asphalt surface, and the measurement error in the field was approximately two percent.

2- The average percentage of slip when using the electronic system in all similar set values was significantly reduced compared to the mechanical system, and this led to a decrease in the average fuel consumption of the electronic system in all similar set values.

3- Due to the adjustable up-going and down-going sensitivity in the electronic system, the control system responded promptly to changes in soil conditions, and there was no need for the driver to intervene to control the vehicle. However, in the mechanical system, in some cases, the oprator had to intervene in controlling the plowing depth.

Abstract Accurate prediction of sugarcane yield plays a crucial role in improving farm management and supply chain sustainability. In this research, a precise framework for predicting sugarcane yield in the Dakht-e-Dehkhoda Sugarcane Agro-Industry (Khuzestan Province) has been presented, based on the combination of satellite data and agricultural information. The data used included 2417 records from the agricultural years 1396 to 1403, gathered from farm management records and Sentinel-2 satellite imagery, including standard vegetation indices such as NDVI and EVI. In the first step, farms were categorized into four distinct groups using the K-means clustering algorithm, based on their management and yield characteristics. Subsequently, to enhance model accuracy, engineered features like water use efficiency and fertilizer use efficiency were defined. Following this, two machine learning models of the Random Forest and Gradient Boosting types were trained for yield prediction. Model evaluation was performed by allocating 80% of the data for training and the remaining 20% to an independent test set. To ensure model stability and generalizability, 5-fold cross-validation was employed during the training phase. The results indicated that the Gradient Boosting model achieved the best performance, reaching a coefficient of determination of 0.9924 and a root mean square error of 1.88 tons per hectare. Furthermore, feature importance analysis revealed that water use efficiency, with a share of 87%, was the most effective factor in yield prediction. In conclusion, the findings of this research confirm that combining management data with satellite information and employing feature engineering can serve as a precise tool to support spatial decision-making and resource management in precision agriculture.

Abstract Potato production plays an important role in ensuring food security in the country. One of the methods for cultivating and propagating potato tubers is the use of minitubers. In the country, approximately 30 million minitubers are produced annually, and efforts are made to classify them into different weight groups. However, since the weight of minitubers deviates significantly from the average, their buying and selling is done by count, and therefore accurate counting is particularly important. Manual counting is often time-consuming, costly, and prone to error.Furthermore, the mechanized counting of microtubers, whether by mechanical or electronic methods, requires the tubers to be precisely singulated and aligned at the end of a conveying path. However, very little research has been conducted on this aspect. In this study, the performance of a minituber singulating conveyor equipped with optical counting sensors was investigated. Since the optimal performance of this machine is influenced by factors including the transverse slope of the conveyor, the linear speed of the conveyor, and the input tuber feed rate, the effects of different levels of these factors were compared for two minituber size groups in a factorial experiment based on a completely randomized design with four replications. The proposed levels for the transverse slope of the conveyor were 15 and 18 degrees relative to the horizontal; for the linear speed of the conveyor, 0.25, 0.5, and 0.75 m/s; and for the input tuber feed rate, values of 2, 3, and 4 tubers per second were considered. The results of this experiment showed that the best conveyor performance is achieved by selecting an 18-degree angle for the transverse slope and a speed of 0.25 m/s for the belt."

Abstract Introduction

The occurrence of fertile soils, long growing season and favorable environmental conditions from the rice harvest until its planting in the next year are significant advantages making these paddy fields a good candidate for second crop. The rice‒forage crops system, in which rice is planted as a summer crop and forage crops as a winter crop, is considered a sustainable system with various advantages. The development of forage crops as a second crop after rice harvest in paddy fields can be an effective strategy to provide fodder for livestock, increase land productivity, sustainability of rice production, increase paddy farmers' income, and thus prevent migration of villagers. Efficient use of energy is one of the principal requirements of sustainable agriculture. Continuous demand in increasing food production resulted in intensive use of chemical fertilizers, pesticides, agricultural machinery, and other natural resources. However, intensive use of energy causes considerable greenhouse gas (GHG) emissions and major environmental problems due to global warming. Therefore, it is of great importance to identify production methods that increase energy use efficiency. Achieving the sustainable production of forage crops requires analysing different energy indices and GHG emissions. Therefore, this study aimed to investigate the energy flow and GHG emissions in forage crops production as a second crop after rice harvesting in paddy fields of Guilan province.



Material and Methods

This study was conducted based on a randomized complete block design with three replications at the research fields of Rice Research Institute of Iran in Rasht during two cropping seasons of 2023-2025. The experimental treatments included eight forage plants (berseem clover, forage mustard, barley, triticale, forage safflower, vetch, forage pea and faba bean) and intercropping of triticale + vetch (50% triticale and 50% vetch). In the present research, input energy, different forms of energy, output energy, net energy, specific energy, energy productivity, energy use efficiency and greenhouse gas emission were calculated and evaluated. The greenhouse gas emissions for each treatment were expressed in terms of kilograms CO2 equivalent per hectare (kg CO2 eq. ha-1). The analysis of variance was done by SAS software and the LSD test was used for mean comparison.



Results and Discussion

The results showed that the highest energy input in forage crops production belonged to the triticale, forage mustard and barley with the averages of 24997, 22572 and 22020 MJ ha-1, respectively. The fuel and nitrogen (N) fertilizer showed higher share percentages in forage crops production than the other energy inputs. The share values of direct energy vary from 42.9% for triticale and forage mustard to 59.9 for berseem clover. Among the indirect energies, N fertilizer had more energy consumption for all forage crops. The highest share percentage of indirect energy was observed for triticale and forage mustard (57.1%), followed by forage safflower (46.7%) and faba bean (46.3%). Non-renewable energy showed a much higher share than renewable energy. The highest share of renewable energy belonged to the forage pea with an average of 14.3%, followed by vetch (11.4%) and faba bean (10.7%). While, the crops of mustard, berseem clover and forage safflower showed the largest share of non-renewable energy with the averages of 98.3%, 96.4%, and 95.7%, respectively. Fuel and N fertilizer played the most important role in increasing the share of non-renewable energy in the experiment. The energy output varied in the range of 151551 MJ ha-1 for intercropping of triticale + vetch to 72450 MJ ha-1 for forage safflower. Based on the results, intercropping of triticale + vetch had the highest energy use efficiency (7.64). The vetch with the averages of 7.57 was also ranked next. The lowest energy use efficiency (3.21) was related to forage mustard. The intercropping of triticale + vetch, berseem clover and barley with energy productivity (0.65 kg MJ-1), (0.49 kg MJ-1) and (0.45 kg MJ-1) were ranked first to third, respectively. While, forage mustard with an average of 0.26 kg MJ-1 had the lowest energy productivity. The results of the mean comparison showed that the highest specific energy allocated to forage mustard with an average of 3.81 MJ kg-1, which was separately classified in group a. Intercropping of triticale + vetch with an average of 1.54 MJ kg-1, had also the lowest specific energy. Based on the results, intercropping of triticale + vetch had the maximum net energy (131719 MJ ha-1). The vetch with the averages of 118866 MJ ha1 was ranked next. Among the various energy inputs, fuel and N fertilizer play the greatest role in total GHG emissions for all forage crops.

Conclusions

The results indicated that the triticale and forage mustard had the highest energy input and GHG emissions. The lowest GHG emissions was related to forage pea and vetch, respectively. The intercropping of triticale + vetch had the highest energy use efficiency, energy productivity and net energy. Based on the results of this study, the fuel and N fertilizer showed higher share percentages in energy inputs and GHG emissions. Therefore, decrease fuel consumption, optimal management of N fertilizer and cultivation of forage legumes can help increase energy efficiency and reduce GHG emissions from fodder production in paddy fields of Guilan Province.

Abstract There are several alternatives for drying with superior quality and food preservation, one of which has attracted the attention of researchers is refractivity window drying. The simultaneous exchange of mass and heat in the drying process has made this phenomenon a complex process in terms of mass transfer and moisture removal. In this study, a refractivity window system was used to dry orange slices with three thicknesses of 4, 6 and 8 mm at three temperatures of 60, 75 and 90 degrees Celsius, and the mass and energy transfer parameters including activation energy, convective mass coefficient, specific energy, drying efficiency and specific moisture evaporation rate were calculated using the two models of Denser and Dost and the Crank model. The results showed that the drying rate constant and the effective moisture diffusion coefficient (internal mass transfer coefficient) increased with increasing temperature and sheet thickness. The highest activation energy was obtained by the Denser and Dost method at 13.20 kJ/mol and at 90 degrees Celsius, respectively. The highest convective mass coefficient was calculated to be 7.95×10-7 at 75°C and 6 mm thickness. The lowest and highest specific energy requirements were 2.5 and 17.2 kWh/kg, respectively. Also, the highest drying efficiency and specific moisture evaporation rate (SMER) were 11.8% and 0.167% at 90°C and 8 mm thickness, respectively.

Abstract Wheat Saviq, a traditional product obtained from roasting wheat grains, possesses notable nutritional value and functional properties. This study investigated the effect of infrared roasting on the physicochemical characteristics and sensory attributes of Wheat Saviq. To evaluate the influence of roasting power and time and to optimize the process, a Box–Behnken experimental design and Response Surface Methodology (RSM) were applied. The measured responses included moisture content, color indices (L*, a*, b*, ∆E), water-holding capacity (WHC), and sensory evaluation. Additionally, fuzzy logic was employed to model the relationship between sensory attributes (color, aroma, taste, and texture) and overall product acceptance. The results indicated that increasing power from 250 to 450 W reduced moisture content from 4.65% to 2.09%. Color indices significantly increased with both higher power and longer roasting times. WHC initially improved with increasing power and time but declined at higher levels. Sensory evaluation revealed that medium power levels and intermediate roasting times yielded the highest overall acceptance. The optimal conditions were predicted at an infrared power of 365.24 W and a roasting time of 18.28 min, resulting in predicted values of 3.39% moisture content, L*=81.57, a*=13.57, b*=16.84, ΔE=9.77, WHC=204.69%, and an overall sensory score of 8.27. Overall, infrared roasting a* and b* values increased, while L* and ΔE changed significantly depending on power and time, maintained desirable sensory characteristics, and improved WHC, leading to the highest overall sensory acceptance. Fuzzy logic analysis highlighted that taste and texture are the most influential factors affecting consumer acceptance, while an appealing color and suitable aroma can compensate for minor textural deficiencies. The fuzzy logic-based predictive model showed strong agreement with experimental results. These findings suggest that infrared roasting is an effective method for improving the quality and consumer acceptance of Wheat Saviq and could be a viable approach for commercial production in the wheat product industry.

Abstract In this study, the Adaptive Neuro-Fuzzy Inference System (ANFIS) was used to predict the traction force, wheel slip and tractor fuel consumption in field conditions during plowing operations with a plow and a harrow. Experiments were conducted in the fields of Iran Tractor Sazi Company using a two-tractor method. The independent variables included four levels of tillage depth (10, 20, 30 and 40 cm), three levels of soil moisture (approximately 12, 15 and 19 percent), and four levels of forward speed with heavy gears (1, 2.55, 4.40 and 6.58 km/h). A factorial experiment based on a randomized complete block design (RCBD) with three replications was used. Simultaneous prediction of key tractor performance indicators is one of the fundamental challenges in managing tillage operations and optimizing energy consumption because these indicators are affected by the complex and nonlinear interaction between soil properties and operating parameters, and conventional methods do not have sufficient accuracy to describe these relationships. Therefore, an intelligent framework based on (ANFIS) along with principal component analysis (PCA) was developed to simultaneously predict traction force, wheel slip and fuel consumption. The results showed that increasing soil depth and forward speed increased traction force, slip and fuel consumption, while increasing soil moisture improved this index due to the decrease in soil mechanical strength. The proposed model with high coefficients of determination for traction force (0.99996), slip (0.9997) and fuel consumption (0.99964) and very low RMSE values was able to simulate the real behavior of the soil tractor system with good accuracy. The results showed that the developed model is not only an accurate tool for predicting tractor performance but also can be used as a decision support system in selecting optimal operating conditions to reduce fuel consumption and tillage costs, control slip and improve energy efficiency in precision agriculture.