This research aimed to develop a rapid, non-destructive, and accurate method for detecting adulteration in turmeric using Visible-Near-Infrared (UV/Vis and NIR) spectroscopy combined with machine learning algorithms. Spectral data from the samples were collected and analyzed in two ranges: 170-870 nm (UV/Vis) and 900-2170 nm (NIR). Four supervised learning algorithms, including Support Vector Machine (SVM), Linear Discriminant Analysis (LDA), the Multilayer Perceptron (MLP) neural network, and Decision Tree, were evaluated for modeling. To quantitatively assess model performance, we employed not only the accuracy metric but also complementary performance indicators including precision, recall, and the F1-score to provide a more comprehensive evaluation of classification effectiveness. The models developed in the 900-2170 nm spectral range demonstrated highly significant performance, with most models achieving 100% accuracy on the independent test set. To reduce data dimensionality and enhance computational efficiency, a hybrid feature selection method combining SVM with five algorithms-League Championship Algorithm (LCA), Genetic Algorithm (GA), Particle Swarm Optimization (PSO), Ant Colony Optimization (ACO), and Imperialist Competitive Algorithm (ICA)-was employed. Upon evaluation of each method, the SVM-LCA was selected as the optimal feature selection technique. This algorithm successfully extracted the most effective wavelengths with the highest correlation and lowest error, which maintained or improved the accuracy of the classification models. This study confirms the high potential of UV/Vis and NIR spectroscopy as rapid, non-destructive, and precise tools for detecting adulteration in turmeric. The findings can pave the way for the development of intelligent quality control systems in the food and pharmaceutical industries, playing a crucial role in ensuring consumer health and safety.

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

This study aimed to non-destructively identify quality parameters in Javadi peaches. In this context, a capacitive array (CCD) spectroradiometer ranging from 350 to 1150 nm was employed for the nondestructive prediction of pH, titrable acid (TA), soluble solid-state (SSC), total phenol (TP), extract anthocyanin (Prance and Nesbitt). Utilizing multivariate partial least squares (PLS) regression models, both reference and degradation measurements were considered. Various spectral data processing techniques such as Savitsky-Goley smoothing (SG), first derivative (D1), incremental diffusivity correction (MSC), standard normal variate (SNV), and Baseline correction were applied individually and in combination (SG + MSC + D1) and (SG + Baseline) to forecast peach quality attributes. Model performance was assessed using the root mean square error of prediction (RMSEP), correlation coefficient (rp), and standard deviation ratio (SDR). Optimal models showed high accuracy for pH (raw spectra: RMSEP = 0.15, rp = 0.94, SDR = 3.0), moderate for TA (first derivative: RMSEP = 0.07, rp = 0.86, SDR = 2.29) and SSC (MSC: RMSEP = 1.17, rp = 0.85, SDR = 1.88), but lower for TP and EA (SDR < 1.5). This non-destructive approach offers rapid, cost-effective quality assessment compared to destructive methods.

The evaluation of energy input and output processes in agricultural systems is a crucial method for assessing sustainability levels within these systems. In this research, the investigation focused on the input and output energies and related indices in sunflower farms in Khoy County during the agricultural year 2017–2018. Data were collected from 140 sunflower producers through specialized questionnaires and face-to-face interviews. Additionally, artificial neural networks (ANNs), specifically the multilayer perceptron, were employed to predict the output energy. The results revealed that a substantial portion of the total input energy was attributed to chemical nitrogen fertilizer (43.98%), consumable fuel (25.74%), and machinery (8.42%). The energy efficiency (energy ratio) in these agroecosystems was relatively low, measured at 1.57 for seed and 7.96 for seed and straw. These values should be improved. The energy efficiency in seed production was computed at 0.06 MJ·ha−1, while, for the combined seeds and straw, it was 0.57 MJ·ha−1. In particular, seed energy efficiency represents approximately 11% of the overall biological energy efficiency, highlighting that a substantial 89% of the produced energy is associated with straw. The proper use of this straw is crucial, as its improper handling could lead to a drastic decrease in overall efficiency. Furthermore, the explanatory coefficient (R2) and the mean absolute percentage error (MAPE) to predict the output energy with the best neural network were 0.94, and 1.77 for the training data, 0.97 and 1.55 for the test data, and 0.9 and 2.08 for the validation data, respectively; additionally, 0.97 and 0.42 were obtained by an ANFIS.

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.

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.

In order to accurately determine and evaluate the odour of rice, it is necessary to identify the substances that affect that odour and to develop methods to determine their amounts. For more than three decades, researchers have been studying the factors that produce and influence the aroma of rice. An electronic nose can be used to detect the volatile compounds of rice, while an olfactory machine is capable of classifying and detecting the variety, origin, and storage time of rice with a high degree of efficiency. This study aimed to investigate the efficacy of electronic noses and other chemometric methods such as principal component analysis, linear discriminant analysis, and the Artificial Neural Network as a cost-effective, rapid, and non-destructive method for the detection of pure and adulterated rice varieties. Therefore, an electronic nose equipped with nine metal oxide semiconductor sensors with low power consumption was used. The results showed that the amount of variance accounted for by PC1 and PC4 was 98% for the samples used. Also, the classification accuracy of the linear discriminant analysis and Artificial Neural Network methods were 100%, respectively. The Support Vector Machines method (including Nu-SVM and C-SVM) was also used, which, in all its functions except the polynomial function, produced 100% accuracy in terms of training and validation.

E-nose device, data from GC-MS (measured data), and statistical and mathematical analytic techniques like PCA, PLSR, LDA, and ANN was used in this study and then a GEP programing model developed to estimate caffeine content of samples. Various samples of coffee beans were tested, when caffeine was used as the reference data, R2 for the PLSR and ANN models were 0.9577 and 0.9634, respectively. R2 for the LDA model were identical to 0.9714. Additionally, R2 of the PLSR and ANN models for palmitic acid respectively, was reported 0.893 and 0.9388. Caffeine calibration data produced the greatest results for identifying, according to the information gathered, also GEP model R2 was reported 0.9581.

This paper presents a comprehensive analysis of the application of visible–near-infrared (Vis/NIR) spectroscopy for the estimation of various chemical attributes of pear fruit. Specifically, the paper investigates how pH, titratable acidity (TA), soluble solids content (SSC), and Vitamin C change as the pear undergoes different storage durations and temperatures. To obtain the most accurate prediction models, we applied a variety of pre-processing techniques to the acquired spectra. Notably, the combination of Savitzky-Golay (S.G.), Multiplicative Scatter Correction (MSC), and second derivatives (D2) emerged as the most effective method for predicting the fruit’s pH, with an impressive rp = 0.95 and SDR = 4.9. In contrast, combining S.G., MSC, and first derivatives (D1) yielded the most accurate predictions for TA, with a robust rp = 0.98 and SDR = 9.6. The research further delved into understanding how the storage period and temperature can significantly influence the pear fruit’s chemical properties. Our findings established that as the storage duration and temperature rise, the pH of the fruit also escalates, while TA sees a decline. The research further elucidates that prolonged storage periods and elevated temperatures lead to the pear fruit shedding its intrinsic qualities, resulting in a reduction in soluble solids and Vitamin C content. To summarize, this paper underscores the immense potential of Vis/NIR spectroscopy as a non-destructive and expedient tool for monitoring the chemical attributes of pear fruit during storage, especially when subjected to diverse temperature and time conditions. These insights not only add to the existing body of knowledge but also align with earlier research on how storage conditions can affect fruit quality.

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.

Coffee is one of the most popular and frequently consumed beverages on the planet. Coffee has a significant commercial value, estimated to be in the billions of dollars and consumption has risen steadily over the last two decades. Near-infrared spectroscopy is one of the non-destructive optical technologies for the evaluation of agricultural products to identify food adulteration. Thus, it is an interesting and worthwhile subject to research and study. In this research, a near-infrared spectroscopy approach along with statistical methods of principal component analysis (PCA), partial-least-squares regression (PLSR), latent dirichlet allocation (LDA), and artificial neural network (ANN) as a fast and non-destructive method was used with to detect and classify coffee beans using reference data obtained by gas chromatography–mass spectrometry (GC–MS). Results showed that the accuracy of PLSR, LDA, and ANN while our reference data was palmitic acid, respectively were 97.3%, 97.92%, and 97.3% and while reference data was caffeine, accuracy results were 94.71%, 95.83%, and 98.96%, respectively.

This study analyzed regional differences in productivity of agricultural machinery sector using data envelopment analysis (DEA) approach. In this research, we identify areas with high productivity in agricultural machinery based on data envelopment analysis method. The data source was a database in the National Statistics Office of Iran (Agricultural Time Series Database). Concerning the performance approach in this study, data envelopment analysis should be used with regarding to input-based process. In order to analyze data from DEA method with input-based approach, different provinces of Iran were selected as the decision making unit in a particular crop year. The efficiency of provincial centers has been evaluated during 2013 and 2016. Based on the results, the centers with performance index of 1 deemed as efficient centers. These centers having high technical performance include Ardebil, Hamedan, Jiroft, Kurdistan, Ilam and Isfahan. Other provincial centers have been technically inadequate or efficient Pareto Kopman.

Abstract Visible–near‐infrared spectroscopy is known for its rapid and nondestructive characteristics designed to predict leaf chlorophyll content (LCC) of winter wheat. It is believed that the nonlinear technique is preferable to the linear method. The canopy reflectance was applied to generate the LCC prediction model. To accomplish such an objective, artificial neural networks (ANN), along with partial least squares regression (PLSR), nonlinear, and linear evaluation methods have been employed and evaluated to predict wheat LCC. The wheat leaves reflectance spectra were initially preprocessed using Savitzky–Golay smoothing, differentiation (first derivative), SNV (Standard Normal Variate), MSC (Multiplicative Scatter Correction), and their combinations. Afterward, a model for LCC using the reflectance spectra was developed by means of the PLS and ANN. The vis/NIR spectroscopy samples at the 350–1400 nm wavelength were preprocessed using S. Golay smoothing, D1, SNV, and MSC. The preprocessing with SNV‐S.G, followed by PLS and ANN modeling, was able to achieve the most accurate prediction, with the correlation coefficient of 0.92 and 0.97, along with the root mean square error of 0.9131 and 0.7305 receptivity. The experimental findings also revealed that the suggested method utilizing the PLS and ANN model with SNV‐S. G preprocessing was practically feasible to estimate the chlorophyll content of a particular winter wheat leaf area according to the visible and near‐infrared spectroscopy sensors, achieving improved precision and accuracy. The nonlinear technique was proposed as a more refined technique for LCC estimating.

The energy crisis and depleting fossil fuel resources have always been the focus of researchers. Fuel consumption of agricultural tractors is not an exception. Researchers have used different methods to predict fuel consumption. With the development of artificial intelligence in the last decade, all re-searchers’ attention has been directed towards it. Deep learning is a subset of machine learning, which was inspired by the data processing patterns in the human brain. The deep learning method has been used in research due to the advantages of high accuracy and generalization. So far, no research has used this method to predict fuel consumption. In this research, field experiments were carried out in sandy clay loam and clay soils to model the temporal fuel consumption and specific fuel consumption of an agricultural tractor using a convolutional neural network (CNN), while having some parameters such as the soil type, soil conditions, tool parameters, and operation pa-rameters. The experiments were conducted within each soil texture in a factorial manner based on the randomized complete block design (RCBD) with three replicates. For each soil texture, various moisture levels (8–17% for dry and 18–40% for moist soils), tractor forward speeds (1.2, 1.6, 1.8, and 2.2 km h−1), working depths (30 and 50 cm), the number of passes (2 and 6), and tire inflation pressure (20 and 25 psi) were selected, and cone index, dynamic load, and moisture content were measured in each experimental section. The designed networks used to predict the instant fuel consumption were of a CNN type. The results indicated that the network developed based on the Sgdm algorithm outperformed the others, and thus it was selected for modeling purposes. The network was evaluated based on R2 and MSE criteria. For the temporal fuel consumption, the best results were obtained while using 8-510-510-1 architecture with R2 = 0.9729 and MSE = 0.0049. The 8-100-95-1 architecture also led to the best prediction of the specific fuel consumption with R2 of 0.9737 and MSE of 0.0054. The high prediction accuracy and low error in this research compared to previous studies indicate the superiority of this method in order to predict fuel consumption. It was also observed from the results that the input parameters, which include soil, tool, and operational parameters, are all effective on fuel consumption. Proper management of some parameters, such as working depth, tire inflation pressure, and forward speed, can help to optimize fuel consumption.

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.

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

Potato is an important agricultural product, ranked as the fourth most common product in the human diet. Potato can be consumed in various forms. As customers expect safe and high-quality products, precise and rapid determination of the quality and composition of potatoes is of crucial significance. The quality of potatoes may alter during the storage period due to various phenomena. Soluble solids content (SSC) and pH are among the quality parameters experiencing alteration during the storage process. This study is thus aimed to assess the variations in SSC and pH during the storage of potatoes using an electronic nose and Vis/NIR spectroscopic techniques with the help of prediction models including partial least squares (PLS), multiple linear regression (MLR), principal component regression (PCR), support vector regression (SVR) and an artificial neural network (ANN). The variations in the SSC and pH are ascending and significant. The results also indicate that the SVR model in the electronic nose has the highest prediction accuracy for the SSC and pH (81, and 92%, respectively). The artificial neural network also managed to predict the SSC and pH at accuracies of 83 and 94%, respectively. SVR method shows the lowest accuracy in Vis/NIR spectroscopy while the PLS model exhibits the best performance in the prediction of the SSC and pH with respective precision of 89 and 93% through the median filter method. The accuracy of the ANN was 85 and 90% in the prediction of the SSC and pH, respectively.

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

. Maize ( Zea mays ) is one of the key crops in the world, taking third place after wheat and rice in terms of cultivated area. This study aimed to demonstrate the potential of non-destructive hyperspectral imaging in the wavelength range of 400-1000 nm to discriminate between and classify maize kernels in three cultivars by using non-destructive hyperspectral imaging in the wavelength range of 400-1000 nm. Three cultivars of maize kernels were exposed to hyperspectral imaging with 20 rep -lications. Predictor variables included 28 intensities of reflection wave for spectral imaging and 4 variables in terms of the weight, length, width, and thickness of a single kernel. The classification was successfully performed through Linear Discriminant Analysis and Artificial Neural Network methods, taking into account 32, 15, and 5 predictor variables. According to the results, Linear Discriminant Analysis with 32 predictor variables is characterized by a high degree of accuracy (95%). The most important predictor variables included the reflection wave intensity of the third peak, the wavelength intensity of 490 nm, the wavelength intensity of 580 nm, and the weight and thickness of a single kernel.