Agricultural Engineering

Abstract Introduction: Phosphate is a structural component of phospholipids, nucleic acids, nucleotides, coenzymes, and phosphoproteins. Phospholipids are important in membrane structure. Nucleic acids of genes and chromosomes carry genetic material from cell to cell. As a monoester, phosphorus provides an essential ligand in enzymatic catalysis. Phytic acid, the hexaphosphate ester of myo-inositol phosphate, is the most common phosphorus reserve in seeds. Inorganic and organic phosphates in plants also serve as buffers in the maintenance of cellular pH. Total phosphorus in plant tissue ranges from about 0.1 to 1%. Phosphorus deficiency suppresses or delays growth and maturity. Although phosphorus- deficient plants are generally stunted in appearance, they seldom exhibit the conspicuous foliar symptoms Phosphorus 55 characteristic of some of the other nutrient deficiencies. Furthermore, appreciable overlap often occurs with the symptoms of other nutrient deficiencies. Plant stems or leaves are sometimes dark green, often developing red and purple colors
Soil salinity is a major problem in arid and semi-arid regions, causing land degradation, desertification, and resulting food insecurity. Salt-affected soils and phosphorus deficiency are common problems in arid and semi-arid regions of the country. Soil salinity limits plant growth by limiting water availability, creating nutritional imbalances, and creating osmotic stress in plants. Quinoa is a plant that is tolerant to environmental stresses and a functional grain with high nutritional value. With the introduction of new quinoa varieties and the production of certified seeds since 2021, this plant has officially entered the cultivation pattern. The economic expansion of the area under cultivation of crops should be based on a correct fertilization program based on soil testing. The aim of this experiment is to determine the critical phosphorus limit for quinoa at different salinities based on soil testing and leaf analysis.

Materials and Methods: An experiment was conducted in a completely randomized design with three factors of soil phosphorus including 10 levels (S1-S10), phosphorus application at two levels (application and non-application) and two levels of irrigation water salinity (6 and 12 dS/m) with three replications in pots. In this experiment, the values calculated manually using the phosphorus fertilizer equivalent formula according to the soil test, phosphorus was added from triple superphosphate at the rates of 25.25, 50.5, 67.32, 92.57, 109.46; 134.71, 151.54, 176.79 and 218.87 mg Kg of soil with 15% moisture content and was incubated for one year due to the process of phosphorus fixation by gypsum and lime in the soil. Then, to apply phosphorus levels (0 and 9 mg phosphorus/Kg) soil was added from a triple superphosphate source. To determine the critical phosphorus level in leaves at the flowering stage, samples were taken from leaves close to the flowers. Biomass, seed weight, and harvest index were measured. The phosphorus content in shoots and seeds was also measured by the dry digestion method.

Results and Discussion: The results showed that severe salinity stress reduced grain yield and biomass by 17 and 24 percent, respectively, but the harvest index increased. Phosphorus fertilizer application increased grain yield and harvest index. Salinity stress increased phosphorus concentration in leaves and shoots and ultimately phosphorus uptake, but grain phosphorus decreased. The greatest positive effect of phosphorus was observed in soils with moderate salinity. The interaction results showed that soil P also played an important role in determining the effect of salinity and phosphorus. Soils with 8 mg/Kg and 13 mg/Kg of available phosphorus showed better performance under moderate stress conditions and phosphorus application, while soil with 16 mg/Kg of available phosphorus had the lowest values under severe salinity conditions. The critical phosphorus limit for soils with moderate and high salinity was 8.9 and 13.8 mg/Kg, respectively, and based on leaf phosphorus, 1.9 and 2.25 g/Kg.

Conclusion: In general, these results showed that optimal management of phosphorus fertilization and reduction of the effects of salinity stress improved growth and yield of quinoa. This is especially important in areas with moderate salinity for sustainable use of soil and water resources. Careful management of phosphorus nutrition under saline conditions is essential to improve phosphorus allocation to seeds and maintain crop yield and quality.

Abstract Introduction: Rice is a staple food for over half of the global population and stands as the second most consumed food product in Iran. Its production is increasing, particularly in Khuzestan province, which nonetheless confronts significant challenges in the post-harvest process. Many rice mills employ traditional technologies that result in substantial losses during the conversion of paddy to white rice, consequently escalating overall costs. These losses inflict considerable economic damage on farmers, and mitigating them-especially given limited water resources- is crucial for boosting domestic output and reducing import dependency. The milling stage, encompassing husking and whitening, is the most sensitive, accounting for the highest rate of grain breakage, which directly affects the product’s economic value. The quality of milled rice, quantified by its whiteness degree, is highly valued in Iran, where the recovered head rice yield is the most critical determinant of paddy price. Modern technologies, such as abrasive whiteners, demonstrate superior efficiency compared to traditional systems, capable of minimizing waste and grain breakage. This research investigates the performance of dominant conversion systems in Khuzestan and proposes practical solutions for waste reduction by focusing on key technical parameters.

Materials and Methods: This study aimed to investigate and compare rice conversion systems operating within Khuzestan province. Three distinct rice milling conversion systems were selected: Type A (Conventional), comprising a grain cleaner, a blade-type husker, two parallel blade whiteners, and a grader; Type B (Simple Imported), consisting of a destoner, grain cleaner, rubber roller husker, paddy separator, abrasive whitener, and polisher; and Type C (Advanced Imported), similar to type B but equipped with two serial abrasive whiteners. The specifications and processing stages of their equipment were examined. Paddy samples of the Anbarbo, the region’s dominant cultivar, were dried to a moisture content of 8.9%, then divided into three batches and processed in the three type mills under study. Sampling was conducted at the output of various machines, and system evaluation parameters, including impurity levels, husking percentage, breakage, conversion efficiency, head rice percentage, whiteness degree, and cooking quality properties, were analyzed in the laboratory. The experimental design followed a randomized complete block design with three treatments and three replications. Data analysis was performed using MSTATC software, employing Duncan and LSD tests at a 5% significance level. This rigorous approach ensured accurate evaluation and comparison of the different milling systems to provide actionable solutions for process improvement and waste reduction.

Results and Discussion: Rice mill type C exhibited the lowest impurity levels due to its two-stage grain cleaning process. Rice mill type A, utilizing the blade-type whitener, registered the highest breakage rate, whereas mill type C with its abrasive system yielded the lowest breakage. The use of abrasive systems is recommended for starchy texture cultivar. No significant difference was found in conversion efficiency among the mills, suggesting either improper use or inappropriate settings of the imported systems for local cultivars. Conversion efficiencies for mills A, B, and C were 67.1%, 68.2%, and 66.8%, respectively. Mills B and C, equipped with polishers, achieved a higher degree of whiteness compared to mill A. The type of mill had no significant effect on amylose content, gelatinization, or grain elongation ratio. The amylose percentage in rice from mills A, B, and C was 22.4%, 21.2%, and 22.1%, respectively. Amylose content significantly influences the physical and chemical properties of rice, ultimately affecting its quality and suitability. The gelatinization degree for rice from mills A and B was 4.6, while it was 4.7 for type C. The grain elongation ratio after cooking was 1.7 for type A and 1.8 for the other two types. Based on these results, utilizing mills B and C for processing the Anbarbo cultivar and similar cultivars in Khuzestan province is preferable to traditional mills. Therefore, for processing Anbarbo rice in this region, the use of rubber roller huskers and abrasive whiteners is recommended over blade huskers.

Conclusion: This research investigated the effect of rice mill type on the quality and quantity of rice in Khuzestan province. The results indicated that the type of mill significantly impacts paddy impurity levels, rice breakage, and the degree of whiteness, whereas conversion efficiency and other key quality characteristics of rice remained unaffected. Mills equipped with superior machinery, such as two-stage grain cleaners and abrasive whiteners, yield higher quality rice. Therefore, the correct selection of rice milling equipment, particularly huskers and whiteners, is crucial for improving rice quality and reducing waste. Specifically, the use of rubber roller huskers is recommended for processing the Anbarbo cultivar.

Abstract Introduction: Iron (Fe) deficiency remains a major constraint in calcareous soils, where high pH and carbonate-rich conditions restrict Fe mobility and convert it rapidly into poorly soluble forms. These limitations reduce Fe availability to plants and highlight the need for strategies capable of modulating Fe transformation pathways within the soil solid phase. Aminochelate-based Fe sources, particularly Fe–amino acid complexes, have gained attention due to their greater resistance to precipitation and their potential to alter Fe fractionation dynamics. Unlike conventional Fe fertilizers such as FeSO₄, which are prone to oxidation and fixation shortly after application, Fe aminochelates may contribute to a more persistent and plant-accessible Fe pool. Considering these characteristics, the present study aimed to determine how seed priming with Fe-glycine [Fe(Gly)₂] and Fe-methionine [Fe(Met)₂] aminochelates influences the distribution of Fe chemical fractions in calcareous soil and to assess how these changes translate into Fe concentration and Fe uptake in sunflower (Helianthus annuus L. cv. Oscar).

Materials and Methods: A field experiment was conducted using a randomized complete block design (RCBD) with three replications. Treatments consisted of seed priming with [Fe(Gly)₂] and [Fe(Met)₂] (2%), FeSO₄ (2%), and a non-primed control. At harvest, soil samples were analyzed for pH, DTPA-extractable Fe, and Fe fractionation using a modified Tessier sequential extraction procedure. Fractions quantified included exchangeable (EXC-Fe), organically bound (ORG-Fe), carbonate-bound (CAR-Fe), Fe/Mn oxide-bound (OX-Fe), and residual (RES-Fe). Fe concentration in seeds and leaves, as well as seed Fe uptake, were also measured.

Results and Discussion: Seed priming with Fe aminochelates markedly influenced soil chemical behavior and plant Fe nutrition. Both [Fe(Gly)₂] and [Fe(Met)₂] lowered soil pH relative to the control and FeSO₄, contributing to improved Fe solubility. DTPA-extractable Fe increased by 29.2% under [Fe(Gly)₂] and 36.1% under [Fe(Met)₂] compared with the control. Similarly, seed Fe concentration increased by 10.4% and 12.6%, and seed Fe uptake by 60.7% and 71.5%, respectively. The overall distribution pattern of Fe fractions followed RES-Fe > OX-Fe > CAR-Fe > ORG-Fe > EXC-Fe. Aminochelate treatments, especially [Fe(Met)₂], altered this pattern by shifting Fe towards more labile forms, specifically EXC-Fe and ORG-Fe, while reducing the contribution of CAR-Fe. Strong correlations were observed between DTPA-extractable Fe and both EXC-Fe (r = 0.86**) and ORG-Fe (r = 0.84**). These two fractions also showed positive associations with seed Fe uptake (r = 0.84** for EXC-Fe; r = 0.80** for ORG-Fe), indicating their decisive role in mediating Fe acquisition by the plant. The pronounced response observed with [Fe(Met)₂] suggests a higher chelation strength and a greater capacity for stabilizing Fe in readily accessible pools, thereby supporting continued Fe mobilization in the soil–plant system.

Conclusion: Overall, the findings demonstrate that seed priming with Fe aminochelates, most notably [Fe(Met)₂], substantially enhances Fe bioavailability in calcareous soils by redirecting Fe from less soluble reservoirs toward more exchangeable and organically bound fractions. This shift results in improved Fe uptake and superior Fe nutritional status in sunflower. Consequently, Fe aminochelates represent an effective and sustainable alternative to conventional FeSO₄ for managing Fe deficiency in high-pH soils and for improving micronutrient efficiency in calcareous agroecosystems.

Abstract Introduction Automatic guidance of agricultural tractors requires accurate estimation of both position and heading, which can be achieved through the integration of multiple sensors. Combining data from an inertial measurement unit (IMU) with satellite-based positioning systems (GPS) using a Kalman filter significantly improves the performance of inertial navigation systems (INS). The GPS module provides global position information, while the IMU measures linear acceleration and angular velocity. By processing IMU outputs, the vehicle’s local position and orientation can also be estimated. Finally, fusing GPS and IMU data through a Kalman filter enables precise determination of the tractor’s position and heading. The aim of this research was to develop an INS for determining the position and heading of agricultural tractors by integrating GPS, IMU, and digital compass data using an Extended Kalman filter algorithm. The resulting outputs can be applied in automatic tractor guidance systems.

Materials and Methods In this study, a NEO-6M GPS module, an MPU6050 IMU module, and a CMPS03 digital compass were used to develop the INS. Data acquisition from the sensors, data fusion, as well as result display and storage, were managed using an Arduino MEGA2560 microcontroller. The implementation of the data fusion algorithm and system programming was carried out in the Arduino IDE environment. In general, IMU outputs contain noise and offset errors. First, the noise was reduced using a median filter, and the offset values were determined by recording the sensor outputs over 200 repetitions, calculating their average, and subtracting the resulting offset from the data. By integrating the gyroscope data, the system rotation angles about the three body axes, namely roll, pitch, and yaw, were obtained. Using the yaw angle derived from the gyroscope data, a rotation matrix was constructed to transform the accelerometer outputs from the body coordinate system to the global coordinate system, thereby obtaining the acceleration components in the global frame. Assuming that the experimental ground surface was flat and level, the gravitational acceleration component was removed only from the vertical acceleration data. The acceleration components in the global coordinate system were then integrated once over time to estimate velocity and integrated a second time to determine the tractor position in three directions. In addition, the tractor pitch and roll angles relative to the global coordinate system were calculated from the acceleration components in the global frame. Finally, the pitch and roll angles obtained from the accelerometer data were fused with the angular velocity measurements around the y- and x-axes, respectively, using an extended Kalman filter to achieve more accurate orientation estimates. Similarly, the digital compass output was combined with the angular velocity around the z-axis to improve the yaw angle estimation. Furthermore, the position estimates obtained from the GPS module were fused with those derived from the accelerometer data to provide a more accurate estimation of the tractor position. After preparation of the hardware and software components and completion of laboratory testing, the developed system was installed on a MF 285 tractor, and its performance in determining tractor position and heading was evaluated. The position and heading values recorded by the system were compared with the actual tractor values, and the corresponding errors were determined. Root mean square error (RMSE) and maximum error (Emax) were used to evaluate system accuracy.

Results and Discussion Laboratory evaluation results showed that gyroscope-derived angle measurements were initially free from noise and offset; however, they gradually deviated over time due to integration drift. In contrast, pitch and roll angles derived from accelerometer data exhibited both noise and offset. Application of the Kalman filter effectively reduced noise and compensated for drift in pitch and roll estimates. Furthermore, fusion of digital compass data with gyroscope-based yaw angles using the Kalman filter successfully eliminated yaw drift. Integration of GPS and IMU data through the Kalman filter also improved positional accuracy. During road tests, the system achieved an RMSE of In the X direction equal to 61.4 m, in the Y direction equal to 50.3 m and in total 4.09 m in estimating tractor position. The maximum error (Emax) in determining position was 11.15 m in the X direction and 11.05 m in the Y direction, resulting in a total maximum positional error of 11.15 m. These errors were mainly caused by interruptions in GPS signal reception at certain points along the test path, which disrupted system performance and resulted in inaccurate position estimation and Kalman filter outputs. Variance analysis of Emax values indicated that tractor forward speed had no significant effect on position determination in the X and Y directions or on total positional accuracy at the 5% probability level. Therefore, the developed system is capable of determining tractor position under different forward speed conditions.

Conclusion The developed INS demonstrated strong potential for application in the automatic guidance of agricultural tractors. To further improve system performance, the use of more accurate GPS and IMU modules is recommended. In addition, alternative noise reduction techniques and advanced versions of the Kalman filter, as well as other data fusion algorithms, should be investigated and evaluated.

Abstract Introduction: Corn (Zea mays L.) is a strategic crop for global food security, feed, and biofuel. Genetic purity of hybrid seeds is vital for high yield and stress resistance. However, different corn varieties often look very similar. Subtle differences in color, texture, shape, and size make traditional identification difficult. Manual inspection is time-consuming, subjective, error-prone, and not scalable. Thus, an automated, non-destructive, and accurate system is needed. Deep learning, especially CNNs, is a powerful tool for agricultural image recognition. Mobile-optimized networks like MobileNet and NASNetMobile offer a good balance between accuracy and efficiency. They are suitable for smartphones and embedded systems. This study compares six deep learning architectures for non-destructive identification of five Iranian corn seed varieties.

Materials and Methods: Six deep learning architectures were evaluated including base MobileNet, DenseNet121, MobileNetV2, MobileNetV3Small, MobileNetV3Large, and NASNetMobile. All models were pre-trained on ImageNet and fine-tuned for corn seed classification. A total of 9,374 high-resolution images of five corn seed classes (KSC201, KSC260, KSC400, KSC703, KSC705) were acquired under controlled conditions. The dataset was split into training (6114 images, 65.2%), validation (1875 images, 20%), and test (1385 images, 14.8%) sets. Models were trained using the Adam optimizer (learning rate 0.0001) for 50 epochs with categorical cross-entropy loss. Data augmentation (rotation, shift, zoom, flip) was applied to enhance generalization. Performance was evaluated using accuracy, precision, recall, specificity, F1-score, ROC curves, AUC, confusion matrices, and training time.

Results and Discussion: MobileNet achieved the best overall performance with 97.04% accuracy, 96.66% precision, 96.70% recall, 99.27% specificity, and 96.67% F1-score. DenseNet121 ranked second with 95.96% accuracy and 95.41% F1-score. MobileNetV3Small (95.02% accuracy, 94.20% F1-score) outperformed MobileNetV3Large (93.43% accuracy, 92.82% F1-score). MobileNetV2 achieved 93.14% accuracy and 92.71% F1-score, while NASNetMobile recorded the weakest performance with 87.08% accuracy and 86.22% F1-score. All models showed high specificity ranging from 96.77% to 99.27%. Class-wise analysis revealed that KSC260 was the most stable class with F1-scores of 98.01% for MobileNet, 98.15% for MobileNetV3Small, 98.13% for DenseNet121, and 96.9% for MobileNetV2. KSC703 also performed well with F1-scores of 98.42% for MobileNet and 98.27% for DenseNet121. In contrast, KSC705 was the most challenging class; MobileNetV2 showed extreme behavior on KSC705 with 99.24% recall but only 77.74% precision, resulting in a low 87.19% F1-score. Persistent misclassifications occurred between class pairs KSC201/KSC705 and KSC400/KSC705 across all models, indicating significant feature overlap. ROC curve analysis showed that MobileNet, DenseNet121, MobileNetV3Small, and MobileNetV3Large achieved an ideal AUC of 1.00, while MobileNetV2 and NASNetMobile recorded excellent AUC values of 0.99 and 0.98, respectively. Regarding computational efficiency, training time varied significantly: MobileNet required 108.32 minutes (highest), DenseNet121 required 104.66 minutes, while MobileNetV3Small required only 62.28 minutes, offering a 46-minute reduction compared to MobileNet while maintaining 95.02% accuracy. MobileNetV2 (60.77 min), MobileNetV3Large (63.96 min), and NASNetMobile (64.04 min) showed moderate efficiency. There is a clear trade-off between accuracy and computational efficiency: MobileNet provides maximum accuracy (97.04%) but requires the most training time (108.32 min), whereas MobileNetV3Small offers a balanced solution with 95.02% accuracy and 62.28 min training time. Therefore, for maximum absolute accuracy without computational constraints, MobileNet is the top choice; for a balanced trade-off between accuracy and efficiency, DenseNet121 is suitable; and for resource-constrained environments such as mobile devices, MobileNetV3Small is the best option.

Conclusion: This study demonstrates that mobile-optimized deep learning architectures, particularly base MobileNet and MobileNetV3Small, possess high potential for accurate, non-destructive, and efficient classification of corn seed varieties. These findings provide a practical reference for researchers and engineers in automated agricultural product classification, offering clear architecture selection guidelines based on specific application constraints.

Abstract Nitrogen (N), phosphorus (P), and potassium (K) are considered among the most important essential macronutrients for plants and play fundamental roles in plant growth, yield, and product quality. The vertical movement of nitrate beyond the root zone, which was simulated to a depth of 30 cm in this study, is one of the primary factors contributing to groundwater contamination and the incidence of eutrophication in surface waters. In addition, improper fertilizer management under localized irrigation systems may intensify nutrient losses and reduce fertilizer use efficiency. In the calcareous soils utilized in this research, characterized by a loam texture, the behavior of these elements under the application of liquid fertilizers and drip irrigation has not been fully elucidated. This study was conducted with the objective of determining the optimal dilution ratio for a complete NPK liquid fertilizer and investigating the vertical dynamics of nitrate, phosphorus, and potassium in calcareous loam soil. The fertilizer used in this research was a base commercial liquid fertilizer under the brand name “Vahishta”.



The experiment was designed as a factorial experiment within a completely randomized design, with three replications, using soil columns (35 cm in height and 2.8 cm in diameter). The study was carried out in the Soil Fertility Laboratory of Sari University of Agricultural Sciences and Natural Resources in 2024 (1403 Solar Hijri). The treatments comprised four dilution levels of the complete NPK liquid fertilizer (Control, 1:5, 1:10, and 1:20 fertilizer to water) and three soil depths (0 to 10 cm, 10 to 20 cm, and 20 to 30 cm). The soil exhibited a loam texture, a pH of 7.7, and a high calcium carbonate content. Irrigation was performed in three stages, with the first stage being the only one accompanied by fertilizer application. Soil moisture was maintained at 60% of saturation in order to simulate conditions close to practical field irrigation management. After each irrigation stage, drainage water was collected and analyzed, and soil samples from different depths were separately evaluated for nutrient distribution.



Following the separate analysis of the water drain from each stage and the soil from each layer, the results indicated that at a 1:5 dilution, 48% of the soluble nitrate was transported to the 30 cm depth during the first irrigation stage. In contrast, this amount decreased to 18% in the 1:10 dilution, equivalent to a 62% reduction compared to the 1:5 treatment. Phosphorus demonstrated very low mobility, with over 92% of it being fixed in the 0 to 10 cm soil layer, which is consistent with the strong adsorption and precipitation reactions of phosphorus in calcareous soils. Potassium exhibited moderate mobility and, unlike nitrate, was associated with a relatively uniform distribution throughout the soil profile (0 to 30 cm). The 1:10 dilution ratio established the best balance between the availability of nutrients for potential plant uptake at depth and the reduction of nitrate efflux from the 30 cm depth. These findings indicate that fertilizer dilution plays a decisive role in controlling nutrient transport and minimizing environmental risks.



Therefore, the application of the complete NPK liquid fertilizer at a 1:10 dilution ratio within a drip irrigation system is recommended as a practical strategy for optimal nutrient management, improved fertilizer efficiency, and reducing the risk of water source pollution in the calcareous soils of Iran’s arid and semi-arid regions.

Abstract Introduction: Land-use change in natural landscapes to agricultural lands is currently a major global concern. Land-use change and agricultural development can lead to soil degradation, erosion, and a reduced soil quality. Agricultural activity, especially rice cultivation and kiwi orchards, is the primary cause of destruction of natural forests in Guilan province. Therefore, due to the environmental importance of forests and the increased risk of degradation and land-use change, this research aimed to investigate the effects of converting forest land to paddy fields (35 years) and to kiwi orchards (7 and 14 years) on some soil physical and chemical properties.

Materials and Methods: Soil samples were collected from two depths (0-20 cm and 20-40 cm) from each land use, including forest, paddy fields, and kiwi orchards (7 and 14 years), with the same parent material. Soil samples were prepared in two categories: the disturbed soil and the undisturbed ones. After becoming air drying, the disturbed samples were sieved by a 2 mm sieve. Soil properties such as soil texture, bulk density, mean weight diameter of aggregates (MWD), pH, electrical conductivity, soil organic carbon, calcium carbonate equivalent, total nitrogen (N), available potassium (K), phosphorous (P), zinc (Zn) and Fe were measured. Also the sensitivity index (SI) and stratification ratio (SR) were calculated. This research was conducted as a factorial experiment in a completely randomized design with two factors: land-use (at four levels) and depth (at two levels), using SAS software version 9.4. All soil properties were tested using one-way analysis of variance and the differences among means were analyzed using Tukey's test at a 5% probability level.

Results and Discussion: The effect of land-use on clay, sand, MWD, pH, calcium carbonate equivalent, and available K was significant. The highest amounts of sand, pH, calcium carbonate equivalent, and available K were observed in the 14 years kiwi orchard, whereas the highest contents of clay and MWD were found in the paddy fields and the forest, respectively. MWD, calcium carbonate equivalent, electrical conductivity, organic carbon, total N, available P, K, and Zn decreased significantly with increasing depth, while the clay percentage increased with depth. Forest exhibited the highest total N content, and the 14 years kiwi orchard showed the highest available P content in both topsoil and subsoil. The highest available Zn content in the topsoil was also associated with the 14 years kiwi orchard. Among the soil properties, the highest SI in the surface depth of the paddy field was related to total N, and in the kiwi orchards, it was related to the MWD.

Conclusion: The results of SI and SR showed that the soil degradation due to land-use change of forest to paddy fields was greater than that of kiwi orchards. The greater sensitivity of total N to land-use change in the topsoil and subsoil indicates that this soil property can potentially reflect long-term changes in soil conditions resulting from the conversion of forest to agricultural land. Therefore, considering the sensitivity of soil properties to forest degradation, preventing land-use change is necessary for sustainable soil management in the study area.

Results and Discussion: The effect of land use on clay, sand, mean weight diameter of soil aggregates, pH, calcium carbonate equivalent, and available potassium was significant. The highest amounts of sand, pH, calcium carbonate equivalent, and available potassium were observed in a 14-years-old kiwi orchard, while the highest amounts of clay and mean weight diameter of soil aggregates were observed in paddy fields and forest, respectively. Mean weight diameter of soil aggregates, calcium carbonate equivalent, electrical conductivity, organic carbon, total nitrogen, available phosphorus, potassium, and zinc decreased significantly with increasing depth, while the percentage of clay increased with increasing depth. Forest land use had the highest amount of total nitrogen, and the 14-years-old kiwi orchard had the highest amount of available phosphorus in both topsoil and subsoil. The highest amount of available zinc in the topsoil was also related to the 14-years-old kiwi orchard.

Conclusion: Stratification ratio greater than 1.5 is more important for detecting the impact of land use change on soil quality. Accordingly, it can be stated that kiwi orchards have performed better than paddy fields in maintaining soil quality. This may be due to the presence of reducing conditions and poor soil drainage in paddy fields, as well as differences in land management practices in the two land uses. Therefore, before any land use change, it is recommended to conduct land suitability assessment studies and protective management.

Abstract Introduction: Drying is one of the most important post-harvest processes for medicinal plants, particularly for heat-sensitive species like peppermint (Mentha piperita). Currently, the common methods of drying medicinal plants in Iran are divided into two main categories: traditional methods (using direct sunlight) and industrial methods (using thermal dryers). Traditional methods face several limitations, including long drying time (4 to 7 days), exposure to environmental pollutants, insect and bird attacks, non-uniform humidity changes, and complete dependence on atmospheric conditions. On the other hand, industrial methods, although they have a higher drying speed, have significant energy consumption (2-3 kWh per kilogram of product) and are associated with greenhouse gas emissions and high operating costs. Traditional drying methods face challenges including high energy consumption, long processing times, and quality degradation. This research comprehensively investigates an innovative hybrid solar-assisted heat pump dryer integrated with flat side reflectors to address these limitations while promoting sustainable energy use in agricultural processing.

Materials and Methods: This research was conducted to evaluate the performance of a combined solar-heat pump dryer equipped with concentrating mirrors for drying mint leaves. The experimental system comprised four main components: a closed-cycle drying unit with heat pump recovery, a three-pass solar air collector, flat side reflectors, and an advanced monitoring and control system. The solar collector featured a black-painted aluminum absorber plate with triple-pass airflow configuration and eight aluminum fins at 48° angle. Two flat glass mirrors (0.96 m² each) were mounted at 60° angle for solar concentration. Comprehensive experiments were conducted using a factorial design with three primary factors: drying temperature (40, 50, 60°C), heat pump application (with/without), and flat side reflectors (with/without). Fresh peppermint leaves (100 g samples) were dried until reaching 15% moisture content (wet basis), with continuous monitoring of thermal parameters, energy consumption, and mass transfer kinetics. Various parameters including drying time, collector inlet and outlet air temperatures, drying chamber temperature, relative air humidity, solar radiation intensity and electrical energy consumption were measured. Performance indices including collector thermal efficiency, drying efficiency, specific moisture absorption rate and heat pump performance coefficient were calculated. The obtained data were analyzed using SPSS statistical software and means were compared using Duncan's multiple range test at a significance level of 5%.

Results and Discussion: Analysis of variance of different experimental factors on the thermal efficiency of the solar collector showed that the effect of temperature, heat pump, flat side reflectors, and the interaction effect of temperature and heat pump, temperature and concentrating mirrors, and temperature and heat pump and mirror on the drying time was statistically significant at the 1% level. The results of the study showed that the use of concentrating mirrors and heat pumps had a significant impact on improving the thermal efficiency of the solar collector. The average efficiency of the solar collector without the use of flat side reflectors was measured at 29.7%, while with the use of concentrating mirrors this value increased to 36%. The use of a heat pump alone also increased the thermal efficiency of the collector by 10%. The highest thermal efficiency was related to the treatment at 40 ° C in the case of using flat side reflectors, which was measured at 49.1 and 48.5% in the two conditions of using and not using a heat pump, respectively. The integrated system demonstrated remarkable performance improvements. Simultaneous application of heat pump and flat side reflectors achieved an 84.5% reduction in drying time (from 240 to 37 minutes) and 65.3% enhancement in collector thermal efficiency. The flat side reflectors individually contributed to 21.2% improvement in thermal efficiency, while the heat pump alone provided 10.1% efficiency gain. Maximum drying efficiency of 6.08% and superior SMER of 0.087 kg/kWh were obtained at 60°C with both subsystems operational. Energy consumption analysis revealed 61.2% reduction under optimal conditions. The enhanced performance is attributed to improved heat transfer through flat side reflectors and efficient energy recovery via the heat pump system, which reduced relative humidity and increased drying potential.

Conclusion: The hybrid solar-heat pump dryer with flat side reflectors presents a technologically advanced and environmentally sustainable solution for medicinal plant processing. The system achieves substantial reductions in drying time and energy consumption while maintaining product quality through controlled temperature conditions. This research contributes to sustainable agriculture by demonstrating the practical integration of renewable energy technologies for food and pharmaceutical applications, with potential for significant economic and environmental benefits in medicinal plant processing industries.























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