To determine the effect of the tractor driving system type on the soil compaction and soil behavior a series of tests was conducted using Goldoni 240 tractor with a power rate of 30.8 kW and included four similar tires at three different driving systems (4WD, rear-wheel drive (RWD) and front-wheel drive (FWD)). To evaluate these systems’ effects on soil compaction, tests were conducted at three soil moisture contents (10, 15 and 20% d.b.), three tire inflation pressures (170, 200 and 230 kPa), and three tractor speeds (1.26, 3.96 and 6.78 km/h). Soil bulk density was measured at three average depths of 20, 30 and 40 cm. To evaluate soil compaction, cylindrical cores were used and to assess soil behavior during this process, the soil displacement in a three coordinate system was measured using three displacement transducers. It was found that the 4WD system created the least bulk density of 1155 kg/m³, while the FWD system led to the highest density of 1241 kg/m³. Maximum vertical soil compression of 55 mm occurred for the FWD system and it declined to 43 and 36 mm in RWD and 4WD systems, respectively. Soil displacement in the horizontal and lateral directions was larger for the FWD system in comparison to the other systems. With increment of speed and depth soil compaction decreased. Minimum bulk density of 1109 kg/m³ was occurred at velocity of 6.78 Km/h using the 4WD system, also with this system at the depth 40 cm density was 1127 kg/m³. While at velocity of 1.26 Km/h and depth of 20 cm soil density was 1190 kg/m³.

Peanuts, owing to their composition of complex carbohydrates, plant protein, unsaturated fatty acids, and essential minerals (magnesium, iron, zinc, and potassium), hold significant potential as a vital component of the human diet. Additionally, their low water requirements and nitrogen fixation capacity make them an appropriate choice for cultivation in adverse environmental conditions. The germination ability of seeds profoundly impacts the final yield of the crop; assessing seed viability is of extreme importance. Conventional methods for assessing seed viability and germination are both time-consuming and costly. To address these challenges, this study investigated Visible–Near-Infrared Spectroscopy (Vis/NIR) in the wavelength range of 500–1030 nm as a nondestructive and rapid method to determine the viability of two varieties of peanut seeds: North Carolina-2 (NC-2) and Spanish flower (Florispan). The study subjected the seeds to three levels of artificial aging through heat treatment, involving incubation in a controlled environment at a relative humidity of 85% and a temperature of 50 °C over 24 h intervals. The absorbance spectra noise was significantly mitigated and corrected to a large extent by combining the Savitzky–Golay (SG) and multiplicative scatter correction (MSC) methods. To identify the optimal wavelengths for seed viability assessment, a range of metaheuristic algorithms were employed, including world competitive contest (WCC), league championship algorithm (LCA), genetics (GA), particle swarm optimization (PSO), ant colony optimization (ACO), imperialist competitive algorithm (ICA), learning automata (LA), heat transfer optimization (HTS), forest optimization (FOA), discrete symbiotic organisms search (DSOS), and cuckoo optimization (CUK). These algorithms offer powerful optimization capabilities for effectively extracting relevant wavelength information from spectral data. Results revealed that all the algorithms demonstrated remarkable accuracy in predicting the allometric coefficient of seeds, achieving correlation coefficients exceeding 0.985 and errors below 0.0036, respectively. In terms of execution time, the ICA (2.3635 s) and LCA (44.9389 s) algorithms exhibited the most and least efficient performance, respectively. Conversely, the FOA and the LCA algorithms excelled in identifying the least number of optimal wavelengths (10 wavelengths). Subsequently, the seeds were classified based on the wavelengths selected via the FOA (10 wavelengths) and (DSOS (16 wavelengths) methods, in conjunction with logistic regression (LR), decision tree (DT), multilayer perceptron (MP), support vector machine (SVM), k-nearest neighbor (K-NN), and naive Bayes (NB) classifiers. The DSOS–DT and FOA–MP methods demonstrated the highest accuracy, yielding values of 0.993 and 0.983, respectively. Conversely, the DSOS–LR and DSOS–KNN methods obtained the lowest accuracy, with values of 0.958 and 0.961, respectively. Overall, our findings demonstrated that Vis/NIR spectroscopy, coupled with variable selection algorithms and learning methods, presents a suitable and nondestructive approach for detecting seed viability.

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.

Peanuts, owing to their composition of complex carbohydrates, plant protein, unsaturated fatty acids, and essential minerals (magnesium, iron, zinc, and potassium), hold significant potential as a vital component of the human diet. Additionally, their low water requirements and nitrogen fixation capacity make them an appropriate choice for cultivation in adverse environmental conditions. The germination ability of seeds profoundly impacts the final yield of the crop, assessing seed viability of extreme importance. Conventional methods for assessing seed viability and germination are both time-consuming and costly. To address these challenges, this study investigated Visible-Near Infrared Spectroscopy (Vis/NIR) in the wavelength range of 500-1030 nm as a non-destructive and rapid method to determine the viability of two varieties of peanut seeds: North Carolina-2 (NC-2) and Spanish flower (Florispan). The study subjected the seeds to three levels of artificial aging through heat treatment, involving incubation in a controlled environment at a relative humidity of 85% and a temperature of 50°C over 24-hour intervals. The absorbance spectra noise was significantly mitigated and corrected to a large extent by combining the Savitzky-Golay (SG) and Multiplicative Scatter Correction (MSC) methods. To identify the optimal wavelengths for seed viability assessment, a range of meta-heuristic algorithms were employed, including world competitive contest (WCC), league championship algorithm (LCA), Genetics (GA), Particle Swarm Optimization (PSO), Ant Colony Optimization (ACO), imperialist competitive algorithm (ICA), learning automata (LA), heat transfer optimization (HTS), forest optimization (FOA), discrete symbiotic organisms search (DSOS), and cuckoo optimization (CUK). These algorithms offer powerful optimization capabilities for effectively extracting relevant wavelength information from spectral data. Results revealed that all the algorithms demonstrated remarkable accuracy in predicting the allometric coefficient of seeds, achieving correlation coefficients exceeding 0.985 and errors below 0.0036, respectively. In terms of execution time, the ICA (2.3635 seconds) and LCA (44.9389 seconds) algorithms exhibited the most and least efficient performance, respectively. Conversely, the FOA and the LCA algorithms excelled in identifying the least number of optimal wavelengths (10 wavelengths). Subsequently, the seeds were classified based on the wavelengths selected by the FOA (10 wavelengths) and (DSOS (16 wavelengths) methods, in conjunction with logistic regression (LR), decision tree (DT), Multilayer Perceptron (MP), support vector machine (SVM), k-nearest neighbor (K-NN), and NaiveBayes (NB) classifiers. The DSOS-DT and FOA-MP methods demonstrated the highest accuracy, yielding values of 0.993 and 0.983, respectively. Conversely, the DSOS-LR and DSOS-KNN methods obtained the lowest accuracy, with values of 0.958 and 0.961, respectively. Overall, our findings demonstrated that Vis/NIR spectroscopy, coupled with variable selection algorithms and learning methods, presents a suitable and non-destructive approach for detecting seed viability.

Evaluating the input and output process of energy in agricultural systems is one of the ways to determine the level of sustainability in these systems. For this reason, the purpose of this study was to investigate and predict the amount of input and output energies and also related indicators in cattle breeding. The information needed to conduct this study was collected by referring to Pars Company and extracting the recorded information regarding the amount of consumed inputs and the value of produced products. Also, part of the information was extracted from the financial reports presented by the company and the reports published in the annual meetings of the shareholders. In addition, the information is related to the financial period from 2011 to 2020. Based on the available data, the value of input and output energies, energy indices were calculated. Also, adaptive network-based fuzzy inference system (ANFIS) was used to predict performance and output energy. According to the results of data analysis, the average energy required for raising cattle during a one-year period was 52,604.52 MJ∙cow ⁻¹ . Also, produced milk, produced manure, and born calf were considered as the output energies. The energy equivalent of produced milk during a year was 56,821.39 MJ∙cow ⁻¹ . After examining different models of ANFIS, the best ANFIS model with the input membership function of trimf was selected and the R ² and MAPE values of 0.98 and 0.42 were obtained, respectively.

Seeds can maintain their quality for a limited time; after that, they will lose their germination ability and vigor. Some physiological and physicochemical changes in the structure of the seeds during storage can decrease the quality of the seeds which is known as aging. Therefore, detection of the strong young seeds from the old ones is a vital issue in the modern agriculture. Conventional methods of detection of the seed viability and germination are destructive, time-consuming and costly. In this research, two peanut cultivars, namely North Carolina 2 (NC-2) and Florispan were selected and three artificial aging levels were induced to them. Hyperspectral images (HSI) of the samples were acquired and the seed viability was evaluated using two pre-trained convolutional neural network (CNN) image processing models, AlexNet and VGGNet. The noise of the reflection spectra of the samples was relatively resolved and modified by combining Preprocessing techniques of moving average (MA) and standard normal variate (SNV). Using principal component analysis (PCA), the dimensions were declined and three principal components (PC) were extracted. These PCs were then used as variables in the classification of support vector machine (SVM) and linear discriminant analysis (LDA). The results showed the high capability of CNN architectures such as AlexNet and VGGNet in detection of the seed viability based on the HIS with no pre-processing and feature extraction. The mentioned architectures reached the accuracy of 0.985 and 0.986, respectively. The combination of feature extraction method of PCA with LDA and SVM classifiers showed that the use of a limited number of PCs instead of all wavelengths can decrease the complexity of modeling, while enhancing the efficiency of the models such that LDA and SVM classifiers achieved the accuracy of 0.983 and 0.986 in classification of peanut sees, respectively.