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Abstract The presented study explored the impact of He-Ne laser light stimulation on basic, selected mechanical properties of alfalfa seeds. The following parameters were considered: destructive force (F), destructive force work (W), and absolute longitudinal deformation (dL). Moreover, the single seed mass (m) was measured prior to the compression test, and the ratios of destructive force to absolute longitudinal deformation (F/dL) and destructive force work to single seed mass (W/m) were calculated. Subsequently, the mass of 1000 seeds was determined using RADWAG XA110.3Y scales, and the geometric dimensions of seeds were measured using a Keyence VHX-2000 microscope. Prior to the compression test, the seeds were subjected to laser light stimulation (λ = 632.8 nm, surface force density = 6 mW·cm− 2) with the exposure time of, respectively: 0 s (control – no stimulation – C), 30 s (L30s), 1 min (L1), 2 min (L2), and 3 min (L3). We observed an increase in most of the mechanical parameters measured under the influence of all the laser light stimulation variants. The only exception was the F/dL ratio, which decreased after 2 and 3-minute exposure, as compared to the control. The results evidenced increased mechanical strength of the stimulated seeds, which may prove significant to the cultivation of new, more resilient cultivars.

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.

Energy consumption in the drying industry has made drying an energy-intensive operation. In this study, the drying time, quality properties (color, shrinkage, water activity and rehydration ratio), specific energy consumption (S.E.C), thermal, energy and exergy efficiency of corn drying using a hybrid dryer convective-infrared-rotary (CV-IR-D) were analyzed. In addition, the energy parameters and exergy efficiency of corn were predicted using the artificial neural network (ANN) technique. The experiments were conducted at three rotary rotation speeds of 4, 8 and 12 rpm, drying temperatures of 45, 55 and 65 °C, and infrared power of 0.25, 0.5 and 0.75 kW. By increasing drying temperature, infrared power and rotary rotation speed, the drying time, S.E.C and water activity decreased while the Deff, energy, thermal and exergy efficiency increased. In addition, the highest values of rehydration ratio and redness (a*) and the lowest values of shrinkage, brightness (L*), yellowness (b*) and color changes (ΔE) were obtained at an infrared power of 0.5 kW, air temperature of 55 °C and rotation speed of 8 rpm. The range of changes in S.E.C, energy, thermal and exergy efficiency during the corn drying process was 5.05–28.15 MJ/kg, 3.26–29.29%, 5.5–32.33% and 21.22–55.35%. The prediction results using ANNs showed that the R for the drying time, S.E.C, thermal, energy and exergy data were 0.9938, 0.9906, 0.9965, 0.9874 and 0.9893, respectively, indicating a successful prediction.

Designing an efficient muffler for single-cylinder IC engines is not an easy task due to the limitation in designing a large muffler and the possibility of engine suffocation. In this regard, this research aims to experimentally investigate the feasibility of inexpensive muffler modification of a small portable two-stroke engine to reduce noise pollution. In experiments, the default muffler as a control treatment and a modified muffler together with a redesigned muffler have been examined to investigate the effect of muffler internal geometry and muffler design on sound emission characteristics. Based on the obtained results, the sound pressure level (SPL) of the modified and redesigned mufflers decreased by 5.31 and 6.86 percent compared to the default muffler, respectively. Meanwhile, installing the modified and redesigned mufflers increased the transmission loss (TL) equal to 8.62 % and 10.08 % compared to the default muffler, respectively. Also, the outcome of Discrete Fourier Transform (DFT) revealed that the intensity and amplitude of output frequencies from the redesigned muffler was significantly reduced in the frequency range of 2–6 kHz. As a general conclusion of this study, by installing the redesigned muffler, the maximum working times of the operator increased by 12.1 h at 5000 rpm.

Spectroscopic analysis was employed to evaluate the quality of three bell pepper varieties within the 350–1150 nm wavelength range. Quality parameters such as firmness, pH, soluble solids content, titratable acids, vitamin C, total phenols, and anthocyanins were measured. To enhance data reliability, principal component analysis (PCA) was used to identify and remove outliers. Raw spectral data were initially modeled using partial least squares regression (PLSR). To optimize wavelength selection, support vector machines (SVMs) were combined with genetic algorithms (GAs), particle swarm optimization (PSO), ant colony optimization (ACO), and imperial competitive algorithm (ICA). The most effective wavelength selection method was subsequently used for further analysis. Three modeling techniques—PLSR, multiple linear regression (MLR), and artificial neural networks (ANNs)—were applied to the selected wavelengths. PLSR analysis of raw data yielded a maximum R2 value of 0.98 for red pepper pH, while the lowest R2 (0.58) was observed for total phenols in yellow peppers. SVM-PSO was determined to be the optimal wavelength selection algorithm based on ratio of performance to deviation (RPD), root mean square error (RMSE), and correlation values. An average of 15 effective wavelengths were identified using this combined approach. Model performance was evaluated using root mean square error of cross-validation and coefficient of determination (R2). ANN consistently outperformed MLR and PLSR in predicting firmness, pH, soluble solids content, titratable acids, vitamin C, total phenols, and anthocyanins for all three varieties. R2 values for the ANN model ranged from 0.94 to 1.00, demonstrating its superior predictive capability. Based on these results, ANN is recommended as the most suitable method for evaluating the quality parameters of bell peppers using spectroscopic data.

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.

Abstract The study analyses the impact of alternating (magnetic induction B = 30 mT for t = 60 s) and constant magnetic fields (B = 130 mT for t = 17 h) and alternating electric fields (electric current E = 5 kV/cm for t = 60 s) on various growth parameters of soy plants: the germination energy and capacity, plants emergence, the fresh mass of seedlings, protein content (Kjeldahl’s method), and photosynthetic parameters (with MINI-PAM 2000 WALTZ Photosynthesis Yield Analyser and a SPAD-502 Chlorophyll Meter). Four cultivars were used: MAVKA, MERLIN, VIOLETTA, and ANUSZKA. Moreover, the advanced Machine Learning processing pipeline was proposed to distinguish the impact of physical factors on photosynthetic parameters. The use of electromagnetic fields had a positive impact on the germination rate in MERLIN seeds. The best results in terms of germination improvement were observed for alternating magnetic field stimulation in all cultivars (p > 0.05). For the VIOLETTA cultivar an increase (p > 0.05) in the emergence and overall number of plants as well as fresh mass was observed after electromagnetic field stimulation. For the MAVKA and MERLIN cultivars, the concentration of proteins in the leaves was noticeably higher in plants grown from seeds stimulated using a constant magnetic field.

Index-based insurance is an innovative concept for evaluating agricultural risks and payouts, which uses an index instead of traditional on-site loss assessment. Area yield insurance, as an index-based approach, is an effective strategy to mitigate moral hazard and adverse selection issues. This study aims to develop area yield insurance as a new insurance plan in Iran for two major crops: wheat and barley. It utilized kernel and joint kernel distributions to price the insurance and assessed producer welfare benefits by comparing the certainty equivalence (CE) of farmers’ utility with and without the policy. Data were collected from East Azerbaijan Province, including county-level yield data for irrigated and rainfed wheat and barley from 1975 to 2019 and 446 individual-level yield data from 2015 to 2019. A two-stage method was used to model yield risk: the first stage fits a trend model, while the second estimates the yield distributions with the detrended data. The results showed a significant difference in premiums calculated by the two distributions, with joint kernel distribution offering the best empirical fit and reasonable premiums. The findings indicate that area yield crop insurance provides positive welfare benefits and should serve as a viable alternative or complement to existing yield insurance plans. The successful implementation of this policy in various countries suggests it can be a suitable risk management program for developing countries like Iran.

Abstract The aim of the study was to measure the tensile strength of 4-year-old alfalfa leaves cultivated from seeds exposed to pre-sowing stimulation with He–Ne laser light for 1 or 5 min (designated respectively as F1 and F5) and alternating magnetic field with the exposure time of 1 or 5 min (respectively, L1 and L5). The leaves were measured in terms of blade length and width as well as petiole thickness prior to the tensile test. Measurements were also conducted to determine the chlorophyll fluorescence lifetime and content of photosynthetic pigments (chlorophyll a, b, a + b and carotenoids). The observed tensile strength was between 1.59 and 2.45 N. In the test group, the observed strength was lower in leaves collected from the top and central section of the stem but higher in the bottom part of the stem as compared to the control. The maximum increase of the tearing tensile force relative to the control (C) was observed for the L1 and F1 stimulation samples in leaves collected from the top and bottom parts of the stem, while the maximum decrease for that force was recorded for L5 leaves from the middle and top part of the stem. Chlorophyll fluorescence lifetimes and the overall content of photosynthetic pigments (chlorophyll a and b and carotenoids) were noticeably decreased in the leaves subjected to the stressors/stimulants applied. The results obtained for F1, L5 and, L1 stimulation revealed a decrease in fluorescence lifetimes. The content of photosynthetic pigments was also decreased under the influence of laser light stimulation (L1). This was a clear indication of plant ageing.

On a global scale, millions of tons of diverse agricultural residues are generated annually. Most of these wastes are burned or dumped in landfills, which causes environmental pollution. Addressing environmental issues arising from agricultural waste materials, in addition to mitigating heating and cooling expenses in the construction sector, is an interesting challenge for researchers. The utilization of agricultural wastes in different parts of construction is producing positive findings day by day, and investigating research in this field is a curiosity for researchers. This short study reviewed the most recent achievements in using agricultural wastes as a substitute or additive material for construction. Using these wastes as aggregate, ash (as a supplementary for cement), or fibers for foam concrete, insulation materials, etc. has been reviewed. This review has focused on very recent published papers. Several studies have demonstrated the effective influences of agro-waste materials in construction, like retaining the compressive strength (155 MPa) of concrete at standard levels and reducing heat losses in buildings (69% energy savings for brick insulated using wheat straw), as well as sound insulation. The use of agro-waste materials for insulation positively improved thermal conductivity, costs, and energy savings. However, some wastes did not provide a high added value, which shows that more investigations still need be performed to fill this gap in the research. Considering the global scale of agricultural waste generation and the potential benefits to both the environment and construction industry, continued research in this area is essential.

Fereydounshahr experiences growing electricity shortfalls and an overreliance on polluting diesel generators. This analysis models renewable hybrid systems to electrify a local dairy operation using HOMER Pro 3.11 software. Despite higher upfront costs, adding just 5% solar photovoltaics maintained low net present costs while increasing clean energy penetration versus diesel generators alone. Furthermore, a combined diesel-solar-wind system reduced carbon dioxide and nitrogen oxide emissions by over 1%. Although diesel generators had the shortest payback period at 48 years, the capital recovery factor for a diesel–solar combination reached 62 years. Thus, tailored hybrid renewable systems could provide an affordable, low-emissions electricity solution for the Fereydounshahr livestock facility. With suitable wind and solar resources, the right policy incentives could also unlock substantial local renewable capacity to meet rising demand and mitigate dependence on imported, climate-harming fossil fuels.

The study pertained to environmentally-friendly methods in the cultivation of cereal, and aimed to manage the quality of rye and triticale seeds and determine the impact of seed conditioning using effective microorganisms (EM), red light (RL), and a combination of the two (RL × EM) on the yield of grain and straw. Moreover, the seeds were sown into soil with and without EM conditioning, designated as 0 and PEM. The yield fluctuations depended mostly on the studied rye and triticale cultivars. The best effects we observed in soil without EM conditioning for Dańkowskie Złote rye and Moderato triticale. The increase in grain and straw yields was, respectively: 26% (RL), 31% (RL × EM), 40% (EM), and 17% (RL × EM), 23% (RL), 32% (EM) – Dańkowskie Złote, 27% (RL × EM), 44% (EM), 46% (RL), and 17% (RL × EM), 51% (EM), 78% (RL) – Moderato. In turn, seeds exposed to the experimental treatment and sown into soil containing EM produced reduced yields in both of the above cultivars. It is noteworthy that the conditioning methods employed in the field experiment can facilitate increased yields but most importantly contribute to the resilience of agrosystems and can therefore have environmental benefits. It is noteworthy that the conditioning methods employed in the field experiment can facilitate increased yields but most importantly contribute to the resilience of agrosystems and can therefore have environmental benefits.

Abstract Two different drying methods were applied for dehydration of apple, i.e., convective drying (CD) and microwave drying (MD). The process of convective drying through divergent temperatures; 50, 60 and 70 °C at 1.0 m/s air velocity and three different levels of microwave power (90, 180, and 360 W) were studied. In the analysis of the performance of our approach on moisture ratio (MR) of apple slices, artificial neural networks (ANNs) was used to provide with a background for further discussion and evaluation. In order to evaluate the models mentioned in the literature, the Midilli et al. model was proper for dehydrating of apple slices in both MD and CD. The MD drying technology enhanced the drying rate when compared with CD drying significantly. Effective diffusivity (Deff) of moisture in CD drying (1.95 × 10−7–4.09 × 10−7 m2/s) was found to be lower than that observed in MD (2.94 × 10−7–8.21 × 10−7 m2/s). The activation energy (Ea) values of CD drying and MD drying were 122.28–125 kJ/mol and 14.01–15.03 W/g respectively. The MD had the lowest specific energy consumption (SEC) as compared to CD drying methods. According to ANN results, the best R2 values for prediction of MR in CD and MD were 0.9993 and 0.9991, respectively.

In this research, a convective/infrared (CV/IR) dryer was used to dry pumpkin slices. For optimization of the drying conditions, the influence of three levels of independent variables including air temperature (40, 55, and 70 °C), air velocity (0.5, 1, and 1.5 m/s), and IR power (250, 500, and 750 W) were assessed by response surface method (RSM) through a face-centered central composite design. Analysis of variance (non-fitting factor and R2 value) was employed to determine the desirability of the model. Response surfaces and diagrams were also utilized to show the interactive influence of the independent variables with the response variables (drying time, energy consumption, shrinkage, total color variation, rehydration ratio, total phenol, antioxidant, and vitamin C contents). According to the results, optimal drying conditions involved a temperature of 70 °C, air velocity of 0.69 m/s, and IR power of 750 W. At the mentioned conditions, response variables of drying time, energy consumption, shrinkage, color, rehydration ratio, total phenol, antioxidant, and vitamin C contents were 72.53 min, 24.52 MJ/kg, 23%, 14.74, 4.97, 617.97 mg GA/100 g dw, 81.57%, and 4.02 mg/g dw, with a confidence level of 0.948, respectively.

In this study, drying characteristics, kinetic modelling, energy and exergy analyses of a convective hot air dryer are presented for water yam. The drying experiments were carried out at temperature levels of 50, 60, and 70°C and slice thicknesses of 3, 6, and 9 mm. The effects of drying variables on the drying rate (DR), moisture diffusivity (Deff), activation energy (Ea), energy utilization (EU), energy utilization ratio (EUR), exergy loss (EXL), exergy efficiency (EXeff), improvement potential (IP), and exergetic sustainability index (ESI) were investigated. The results showed that increasing air temperature increased the DR, Deff, EU, EUR, EXL, EXeff, IP, and ESI, while increasing the slice thickness increased Deff and Ea, but decreased the DR. The highest Deff and Ea values were 4.2 × 10−8 m2/s, and 53 KJ/mol, respectively. EU and EUR varied from 10 to 150 J/s and 0.39 to 0.79, respectively. EXL and EXeff varied between 2 and 12.5 J/s and 58 to 75 %, respectively. Midilli’s model had the best performance in predicting the moisture ratio of water yam with coefficient of determination (R2 = 0.9998), root mean square error (RMSE = 0.0049), and sum of square error (SSE = 0.0023).

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.

Seedless barberry fruit is native small fruit in Iran. To examine the impact of various drying methods and storage on the biochemical attributes (Vitamin C, Anthocyanin, Phenol, pH, TA), color index (a*, b*, L*, ab, and Chroma), drying time, and fruit microstructure (by SEM) of seedless barberry (Berberis vulgaris var. asperma), and effective moisture diffusivity coefficient (Deff), specific energy consumption (SEC), energy efficiency (EE) of the dryers, this experiment was performed. Drying treatments include microwave (100, 170, and 270 W), oven (60 and 70 °C), cabinet (50 and 70 °C), shade, sun, and fresh samples (control) and storage 6 months after drying (in polyethylene packaging and at a temperature of 5–10 °C). Results showed minimum and maximum drying times (50 min and 696 h), were related to microwave (270 W) and shade methods, respectively. The highest color values were observed in fruits treated with control, shade and sun treatments and the lowest values were observed in cabinet (70 °C) methods. According to the SEM results, microwave significantly affected surface structure of the dried sample compared to others. The findings indicated that the use of artificial drying methods than natural methods (sun and shade) cause a more significant reduction in color indexes, while vitamin C, soluble solids, and anthocyanin were significantly maintained at a high level. Storage reduced anthocyanin content of fruits almost 12%. Moreover, it was discovered EE and SEC values varied in the range of 1.16–25.26% and 12.20–1182 MJ/kg, respectively. Deff values were higher in microwave 270 W.

Introduction Noise is one of the most important physical factors occurring in private farmers’ working environment. Hazardous noise exposures and hearing loss have been documented among farmers and farm workers for many years. In most cases, reducing the amplitude of vibratory motion of elements in a machine will reduce the noise generated by the machine element. The authors present the results on studies performed on a grain crusher with the aim of optimizing their acoustic behaviour. Materials and method The dynamic behaviour of a grain crusher was investigated by identification of its natural frequency and damping parameters. The study was conducted using the experimental modal analysis technique. The excitation was generated at one single point by a vibration exciter, and the response signals were acquired by an accelerometers fixed at different points of the grain crusher. The modal parameters were determined from a set of frequency response measurements between a reference point and a number of measurement points of the structure. Results The introduced structural modifications reduced the noise level by 3dB (A)5 dB[lin]) for the hopper component frequencies related to the vibration of the hopper (180 Hz) and the crusher support structure (240 to 480 Hz). The level of these components determines the level of noise at the operator’s work station for the average conditions of filling the hopper with grain. The total noise level at the crusher operator was reduced by 2.6 dB (A). Conclusions Reducing the amplitude of vibratory motion of the elements in a machine will reduce the noise generated by the machine element. The obtained results confirm that structural dynamic modification is an effective tool for changing the dynamical properties of vibrating systems.

Drying as an effective method for preservation of crop products is affected by various conditions and to obtain optimum drying conditions it is needed to be evaluated using modeling techniques. In this study, an adaptive neuro-fuzzy inference system (ANFIS), artificial neural network (ANN), and support vector regression (SVR) was used for modeling the infrared-hot air (IR-HA) drying kinetics of parboiled hull. The ANFIS, ANN, and SVR were fed with 3 inputs of drying time (0–80 min), drying temperature (40, 50, and 60 °C), and two levels of IR power (0.32 and 0.49 W/cm2) for the prediction of moisture ratio (MR). After applying different models, several performance prediction indices, i.e., correlation coefficient (R2), mean square error index (MSE), and mean absolute error (MAE) were examined to select the best prediction and evaluation model. The results disclosed that higher inlet air temperature and IR power reduced the drying time. MSE values for the ANN, ANFIS tests, and SVR training were 0.0059, 0.0036, and 0.0004, respectively. These results indicate the high-performance capacity of machine learning methods and artificial intelligence to predict the MR in the drying process. According to the results obtained from the comparison of the three models, the SVR method showed better performance than the ANN and ANFIS methods due to its higher R2 and lower MSE.

The evaluation of a production system to analyze greenhouse gases is one of the most interesting challenges for researchers. The aim of the present study is to model almond nut production based on inputs by employing artificial neural networks (ANNs) and adaptive neuro-fuzzy inference systems (ANFIS) procedures. To predict the almond nut yield with respect to the energy inputs, several ANN and ANFIS models were developed, evaluated, and compared. Among the several developed ANNs, a network with an architecture of 8-12-1 and a log-sigmoid, and a linear transfer function in the hidden and output layers, respectively, is found to be the best model. In general, both approaches had a good capability for predicting the nut yield. The comparison results revealed that the ANN procedure could predict the nut yield more precisely than the ANFIS models. Furthermore, greenhouse gas (GHG) emissions in almond orchards are determined where the total GHG emission is estimated to be about 2348.85 kg CO2eq ha−1. Among the inputs, electricity had the largest contribution to GHG emissions, with a share of 72.32%.

Fruits provide various vitamins to the human body. The chemical properties of fruits provide useful information to researchers, including determining the ripening time of fruits and the lack of nutrients in them. Conventional methods for determining the chemical properties of fruits are destructive and time-consuming methods that have no application for online operations. For that, various researchers have conducted various studies on non-destructive methods, which are currently in the research and development stage. Thus, the present paper focusses on a non-destructive method based on spectral data in the 200–1100-nm region for estimation of total soluble solids and BrimA in Gala apples. The work steps included: (1) collecting different samples of Gala apples at different stages of maturity; (2) extracting spectral data of samples and pre-preprocessing them; (3) measuring the chemical properties of TSS and BrimA; (4) selecting optimal (effective) wavelengths using artificial neural network-simulated annealing algorithm (ANN-SA); and (5) estimating chemical properties based on partial least squares regression (PLSR) and hybrid artificial neural network known as the imperialist competitive algorithm (ANN-ICA). It should be noted that, in order to investigate the validity of the methods, the estimation algorithm was repeated 500 times. In the end, the results displayed that, in the best training, the ANN-ICA predicted the TSS and BrimA with correlation coefficients of 0.963 and 0.965 and root mean squared error of 0.167% and 0.596%, respectively.

The study targeted towards drying of cantaloupe slices with various thicknesses in a microwave dryer. The experiments were carried out at three microwave powers of 180, 360, and 540 W and three thicknesses of 2, 4, and 6 mm for cantaloupe drying, and the weight variations were determined. Artificial neural networks (ANN) and adaptive neuro-fuzzy inference systems (ANFIS) were exploited to investigate energy and exergy indices of cantaloupe drying using various afore-mentioned input parameters. The results indicated that a rise in microwave power and a decline in sample thickness can significantly decrease the specific energy consumption (SEC), energy loss, exergy loss, and improvement potential (probability level of 5%). The mean SEC, energy efficiency, energy loss, thermal efficiency, dryer efficiency, exergy efficiency, exergy loss, improvement potential, and sustainability index ranged in 10.48–25.92 MJ/kg water, 16.11–47.24%, 2.65–11.24 MJ/kg water, 7.02–36.46%, 12.36–42.70%, 11.25–38.89%, 3–12.2 MJ/kg water, 1.88–10.83 MJ/kg water, and 1.12–1.63, respectively. Based on the results, the use of higher microwave powers for drying thinner samples can improve the thermodynamic performance of the process. The ANFIS model offers a more accurate forecast of energy and exergy indices of cantaloupe drying compare to ANN model.

Soil compaction management relies on costly annual deep tillage. Variable-depth tillage or site-specific tillage modifies the physical properties of the soil at the required zones for the growth of crops. In this study, a depth control system was designed for the subsoiler of the tillage at various depths. For this purpose, an algorithm was written to investigate the subsoiler location and soil compaction. A program was also developed to implement this algorithm using Kinco Builder Software to control the subsoiler depth, which was evaluated on the experimental platform. In this study, four compression sensors were used at a distance of 10 cm up to a depth of 40 cm on the blade mounted at the front of the tractor. The data of these sensors were used as the input and compared with the pressure baseline limit (2.07 MPa), and with the priority to select the greater depth, the depth of subsoiler was determined. At all three modes of sensor activation (single, collective, and combined), this system was able to operate the hydraulic system of the tractor and place the subsoiler at the desired depth through the use of the position sensors.

This study was carried out to optimize a computational model of a new underground passive solar greenhouse to improve thermal performance, storage, and saving of heat solar energy. Optimized and conventional passive solar greenhouse were compared in regards of indoor air temperature, irradiation, and energy demand. Six different materials were used in the conventional model. In addition, TRNSYS software was employed to determine heat demand and irradiation in the greenhouse. The results showed that the annual total heating requirement in the optimized model was 30% lower than a conventional passive solar system. In addition, the resulting average air temperature in the optimized model ranged from −4 to 33.1 °C in the four days of cloud, snow, and sun. The average air temperature in the conventional passive solar greenhouse ranged from −8.4 to 24.7 °C. The maximum monthly heating requirement was 796 MJ/m2 for the Wtype87 model (100-mm lightweight concrete block) and the minimum value was 190 MJ/m2 for the Wtype45 model (50-mm insulation with 200-mm clay tile) in a conventional passive solar greenhouse while the monthly heating requirement estimated 126 MJ/m2 for the optimized greenhouse model. The predictability of the TRNSYS model was calculated with a coefficient of determination (R2) of 95.95%.

In this study, the possibility of non-destructive detection of tomato pesticide residues was investigated using Vis/NIRS and prediction models such as PLSR and ANN. First, Vis/NIR spectral data from 180 samples of non-pesticide tomatoes (used as a control treatment) and samples impregnated with pesticide with a concentration of 2 L per 1000 L between 350–1100 nm were recorded by a spectroradiometer. Then, they were divided into two parts: Calibration data (70%) and prediction data (30%). Next, the prediction performance of PLSR and ANN models after processing was compared with 10 spectral preprocessing methods. Spectral data obtained from spectroscopy were used as input and pesticide values obtained by gas chromatography method were used as output data. Data dimension reduction methods (principal component analysis (PCA), Random frog (RF), and Successive prediction algorithm (SPA)) were used to select the number of main variables. According to the values obtained for root-mean-square error (RMSE) and correlation coefficient (R) of the calibration and prediction data, it was found that the combined model SPA-ANN has the best performance (RC = 0.988, RP = 0.982, RMSEC = 0.141, RMSEP = 0.166). The investigational consequences obtained can be a reference for the development of internal content of agricultural products, based on NIR spectroscopy.

Introduction and objective Many people are exposed to vibration (WBV) in their occupational lives. The biodynamic responses of the human body in sitting conditions have been widely measured under whole-body vibration (WBV). The measures are most often expressed in terms of force-motion relations at the driving-point, namely, mechanical impedance, apparent mass and absorbed power, and flow of vibration through the body, such as seat-to-head and body segments vibration transmissibility. The absorbed power is a measure of the energy absorbed by the subject due to the external forces applied to the system. Material and Methods The body behaves like a vibrating physical system with distributed energy-storage elements (masses, springs) and energy-dissipation elements (dampers). The total quantity of power can be divided into 2 components – one real and one imaginary. The real component reflects the energy-absorbing part of the system, due to the transformation of friction into heat within the tissues. The imaginary component reflects the energy-storing part of the system which does not consume any vibration energy. Results The seated human is modeled as a series 4-DOF dynamic models. After introduction of the excitation, the response in particular segments of the model can be analyzed. As an example, the vibration power dissipated in an operator’s body segments has been determined as a function of the agricultural combination operating speed 1.4 – 2.75 ms -1 . Conclusions The concept of absorbed power as a measurement for evaluation of WBV exposure opens a new area for research. The important character of absorbed power is that it has physical significance and therefore can be measured as well as computed analytically. The absorbed power relates to dissipation of energy attributed to relative motions of the visco-elastic tissues, muscles and skeletal system, which under prolonged exposures could lead to physical damage in the musculoskeletal system. A structural model of the human operator allows determination of the dynamic characteristics of the model, and study of the energy flow between the elements of the model.

The study aimed to determine the impact of laser light (L), magnetic stimulation (p) and silver nanocolloid (AgNC) on pumpkin seeds on the germination rate and content of photosynthetic pigments as well as the efficiency of photosynthesis and greenness index in the plant’s development stages. Seeds germinated after the use of various combinations of different refining techniques. The best results were observed for the alternating magnetic field, where the germination energy increased significantly by 20% relative to the control. A similar effect was observed in terms of germination capacity which increased by 4%. A decrease in terms of emergence rate was observed in all study groups. Leaves grown from seeds soaked in nanocolloidal silver on platters were characterised by a significantly higher content of chlorophyll a and b by, respectively, 53 and 11%, as well as 79% higher carotenoid content. The leaves of potted plants contained 42 and 43% more chlorophyll a in groups p and AgNC. In addition, 66 and 81% more carotenoids in groups L and p. At the stage of the onset of flowering of pumpkin, an improvement in terms of photosynthetic efficiency and greenness index was observed in all study groups. The highest improvement was recorded for seeds soaked in silver and reached 23% (intensity of photosynthesis) and 11% (greenness index SPAD).

The aim of the study was to determine the impact of red light (RL) and effective microorganisms (EMs) on the wheat yield of grain and straw, as well as the quality (protein, carbohydrates, gluten, index of sedimentation (SDS index), germination capacity). Moreover, the experiments measured the granulometric composition and pH of soil, as well as its content of microelements and major nutrients, including heavy metals. The field experiment was conducted in 2017, 2018, and 2019 at the experimental station (Felin) of the University of Life Sciences in Lublin (Poland). The best results in terms of overall yield, protein content, and SDS index were obtained after the red light treatment (RL). The three-year application of effective microorganisms (EMs) in the soil had a positive impact, relative to the control, on the grain yield, straw yield, starch, SDS index, and germination capacity. A slight decrease was observed in terms of the protein content. After the application of effective microorganisms (EMs) in soil, changes were observed in the soil’s granulometric composition, pH, humus, and microelements.

This paper presents the results of a study on the effects of red light treatment of sweet corn seeds on the quantity and quality of the subsequent yield (total and marketable), as well as the length of cobs and thousand kernel weight. The respective groups of kernels were exposed to light: 1—once (K1), 2—twice (K2), and 3—thrice (K3); the control seeds were untreated. The light treatment had a positive impact on the analyzed values. Relative to the control, we observed a yield increase of approx. 5% for the Basin cultivar and approx. 25% for the Shaker and Powerhouse cultivars under the K2 mode. We also recorded an increase in the percentage of marketable yield of approx. 20%, 45%, and 48%, respectively for the Shaker, Powerhouse, and Basin cultivars. Positive effects were also observed in terms of cob length and thousand kernel weight for all treatment modes and cultivars analyzed. The obtained results support the inclusion of this method in the scope of recommendable agrotechnical practices aimed at improving the quantity and quality of yields of various crops and vegetables.

This study was focused on analyzing the effects of dietary inclusion of raw chickpea seed as a replacement of soybean meal as a primary protein source on bone structure in broiler chickens. Broiler chickens (n = 160) received in their diet either soybean meal (SBM) or raw chickpea seeds (CPS) as a primary protein source throughout the whole rearing period (n = 80 in each group). On the 42th day randomly selected chickens from each group (n = 8) were slaughtered. Collected tibiotarsus were subjected to examination of the biomechanical characteristics of bone mid-diaphysis, microstructure of the growth plate and articular cartilages; the analysis of mineral content and crystallinity of mineral phase, and the measurements of thermal stability of collagen in hyaline cartilage were also carried out. The inclusion of chickpea seeds resulted in increase of bone osteometric parameters (weight, length and mid-diaphysis cross-sectional area) and mechanical endurance (yield load, ultimate load, stiffness, Young modulus). However, when loads were adjusted to bone shape (yield and ultimate stress) both groups did not differ. Mineral density determined by means of densitometric measurements did not differ between groups, however the detailed analysis revealed the differences in the macro- and microelements composition. The results of FT-IR and XRD analyses showed no effect of diet type on mineral phase crystallinity and hydroxyapatite nanocrystallites size. In trabecular bone, the increase of real bone volume (BV/TV) and number of trabeculae was observed in the CPS group. Total thickness of articular cartilage was the same in both groups, save the transitional zone, which was thicker in the SBM group. The total thickness of the growth plate cartilage was significantly increased in the CPS group. The area of the most intense presence of proteoglycans was wider in the SBM group. The structural analysis of fibrous components of bone revealed the increase of fraction of thin, immature collagen content in articular cartilage, trabeculae and compact bone in the CPS group. The dietary inclusion of CPS affected the thermal stability of collagen, as decrease of net denaturation enthalpy was observed. This study showed a beneficial effect of CPS on the skeletal development, improving the overall bone development and the microarchitecture of cancellous bone. It suggests that CPS can be a promising replacement for SBM in broilers feeding in the aspect of animal welfare related to the development of the skeletal system.

Drying is one of the most common and effective techniques for preserving the quantitative and qualitative characteristics of medicinal plants in the post-harvest phase. Therefore, in this research, the effect of the new refractance window (RW) technology on the kinetics, thermodynamics, greenhouse gasses, color indices, bioactive properties, and percentage of mint leaf essential oil was investigated in five different water temperatures in the form of a completely randomized design. This process was modeled by the methods of mathematical models and artificial neural networks (ANNs) with inputs (drying time and water temperature) and an output (moisture ratio). The results showed that with the increase in temperature, the rate of moisture removal from the samples increased and as a result, the drying time, specific energy consumption, CO2, NOx, enthalpy, and entropy decreased significantly (p < 0.05). In addition, the drying water temperature had a significant effect on the rehydration ratio, color indices, bioactive properties, and essential oil percentage of the samples (p < 0.05). The highest value of rehydration ratio was obtained at 80 °C. By increasing temperature, the main color indices such as b*, a*, L*, and Chroma decreased significantly compared to the control (p < 0.05). However, with the increase in temperature, the overall color changes (ΔE) and L* first had a decreasing trend and then an increasing trend, and this trend was the opposite for the rest of the indicators. The application of drying water temperature from 50 to 70 °C increased antioxidant, phenol content, and flavonoid content, and higher drying temperatures led to a significant decrease in these parameters (p < 0.05). On the other hand, the efficiency of the essential oil of the samples was in the range of 0.82 to 2.01%, and the highest value was obtained at the water temperature of 80 °C. Based on the analysis performed on the modeled data, a perceptron artificial neural network with 2-15-14-1 structure with explanation coefficient (0.9999) and mean square error (8.77 × 10−7) performs better than the mathematical methods for predicting the moisture ratio of mint leaves. [ABSTRACT FROM AUTHOR]