نشریه ماشین های کشاورزی
سال سیزدهم شماره 4 (پیاپی 30، زمستان 1402)

Environmental crises and resource depletion have adversely affected environmental resources and food security in the world. Therefore, with the global population growth in the coming years and the rising need to produce more food, attention must be given to environmental issues, energy consumption, and sustainable production. The purpose of this study is to evaluate the pattern of energy consumption, environmental impacts, and optimization of the studied energy indicators in dairy cattle breeding industrial units in Khuzestan province, Iran.

This research was conducted in Khuzestan province, located in the southwest of Iran. Energy indicators including energy ratio, energy efficiency, specific energy, and net energy were used to determine and analyze the relationships between the output and input energy. Additionally, the life cycle assessment methodology was used to assess the environmental impact. Life cycle assessment includes a goal statement, identification of inputs and outputs, and a system for assessing and interpreting environmental impacts, and can be a good indicator for assessing environmental issues related to production. The life cycle assessment method used in this study was CML-IA baseline V3.05, which includes the four steps of (1) selecting and classifying impact categories, (2) characterizing effects, (3) normalizing, and (4) weighting. Overall, 11 impact groups were studied. The Data Envelopment Analysis (DEA) method with the Anderson-Peterson model was used for optimization. This method identifies the most efficient production unit and makes it possible to rank all of the farms in the region. In this study, each production unit (farm) was considered a decision-making unit (DMU), and its production efficiency was determined based on two models. Namely, the Charnes, Cooper, and Rhodes (CCR) model also known as Constant Return to Scale (CRS), and the Banker, Charnes, and Cooper (BCC) model also known as Variable Return to Scale (VRS).

The results showed that the input and output energies per cow per day were 173.34 and 166 MJ, respectively. Livestock feed and electricity accounted for 65.47% and 27.2% of the input energy, respectively, while the oil used for tiller-scraper lubrication of fertilizer collection accounted for only 0.01%, making it the lowest input energy. Energy efficiency, specific energy, and net energy were calculated as 0.95, 0.13 kg MJ-1, 7.51 MJ kg-1, and -7.20 MJ per cow, respectively. In the abiotic depletion impact group, animal feed, machinery, and livestock equipment had the highest environmental impacts. The results showed that animal feed had the highest environmental emissions in all impact groups except for abiotic depletion of fossil fuels where electricity had the greatest effect. CRS model determined that 7 units were efficient; with an average efficiency of 0.78. In the BCC model, 20 production units were calculated as highly efficient, and the average efficiency was computed to be 0.78.

In dairy farms in Khuzestan province, animal feed and electricity were found to have the highest energy consumption. In most impact groups, animal feed had the highest environmental effects. Specifically, in the abiotic depletion impact group, animal feed, livestock machinery, and equipment had the highest environmental effects. Considering the length of the heat period and the intensity of the solar flux, the installation of solar panels on the farm's roof to generate electricity can help reduce the consumption of non-renewable energy and mitigate radiation intensity under the roof.

The world’s growing population has led to an inevitable increase in energy demand, and this, in addition to the depletion of non-renewable energy sources, can lead to several environmental issues. Wind power has proven to be a reliable and sustainable source of electricity, particularly in light of the pressing need to mitigate environmental impact and promote the use of renewable energy. The purpose of this research is to investigate and compare the environmental effects of electricity production from two wind power plants, Aqkand and Kahak, using wind turbines with a capacity of 2.5 megawatts for a period of three different lifetimes (20, 25, and 30 years).

The present study investigates the environmental effects of electricity generation during the life cycle of wind farms (Kahak and Aqkand) during the construction and operation of these power plants and the cumulative exergy demand index. The specifications of the wind turbines used in the current research are: turbine capacity of 2.5 MW, rotor diameter of 103 meters, rotor weight of 56 tonnes, three blades, each blade is 50.3 meters long and weighs 34.8 tonnes. The turbines are manufactured by Mapna and used in dry conditions. A functional unit of one kilowatt of electricity was selected and the data were analyzed in SIMAPRO software using IMPACT2002+ method with 15 midpoint indicators and four final indicators.

The results showed that the stage of raw materials and production has the highest impact on the creation of midpoint indicators, which is due to extraction, manufacturing, and production of parts such as steel casting using non-renewable energy and activities such as high-temperature welding. The total environmental index of Aqkand and Kahak wind power plants for 1 kWh of generated electricity was 5.84 and 4.45 μPt respectively, more than half of which belongs to the damage to human health category. The investigation of the ionizing radiation index showed that the use of diesel fuel in the installation phase resulted in the highest amount of emissions in both of the power plants, so the share of pollutant emissions in the raw materials and production phase is more than 40%, and in the installation phase due to diesel fuel consumption was more than 48%. The investigation of the eutrophication index showed that the raw materials and production stage accounted for more than 95% of the damage to the ecosystem quality category, and in the meantime, copper and electrical components had the highest amount of contribution to the raw materials and production stage. Additionally, diesel fuel accounted for the largest part of the result in the installation stage, and the transportation and maintenance stage included less than 1% of this result. The investigation of the renewable energy consumption index showed that the stage of raw materials and turbine production in the Aqkand power plant with a share of 68% and the Kahak power plant with a share of 70% had the greatest effect on the category of resource damage. Also, the installation and commissioning phase was the second most effective factor in the category of resource damage due to the use of diesel fuel. The study of the cumulative exergy demand index showed that non-renewable-fossil resources had the largest share in exergy demand (0.15 MJ) to produce one kilowatt of electricity generated from power plants.

In this study, the results showed that in both plants, about 70% of various respiratory effects, 60% of human health issues, and 25% of acidification and global warming are caused in the raw materials and manufacturing phase. Furthermore, the installation phase is responsible for 17% and 16% of climate change in the Aqkand and Kahak power plants respectively, and between 14% and 26% of other environmental factors.

With the emergence of new automation and mechanized technologies in the production and processing of agricultural products in Iran, which aim to accelerate the food supply process, adopting appropriate management models in the field of maintenance becomes inevitable. This is crucial to maintain and enhance the operational reliability of agricultural machinery, tools, and equipment. Furthermore, proper management of various physical assets in the agricultural industry, including operation and maintenance, is one of the most important requirements. This is due to their crucial role in ensuring readiness and high availability during the seasons of planting, cultivating, and harvesting agricultural products. These needs differ from that of other continuous production processes. 

To achieve an efficient model in the field of maintenance, the following steps have been investigated:a) Reviewing and identifying the most important criteria and sub-criteria driving the maintenance management. This is based on the previous literature and the experts’ opinion.b) Evaluating and prioritizing the main criteria and the interactions between their sub-criteria using the Best-Worst Method (BWM).c) Providing improved solutions for maintenance management of Iranian agro-industries.We decided to employ BWM because, compared to similar methods, it (i) provides more reliable pairwise comparisons, (ii) reduces the possible anchoring bias that may occur during the weighting process by respondents, (iii) is the most data-efficient method, and (iv) provides multiple optimal solutions which increase flexibility when accessing the best weight point. The process of weighting by BWM is summarized in five steps:1) Determine a set of evaluation criteria identified by the experts or decision-makers.2) Identify the most important (Best) and the least important (Worst) criteria according to the experts or decision-makers, each of which may have their own Best and Worst.3) Determine the preference of the Best criterion over all the other criteria using a number from 1 to 9 (where 1 represents equal importance and 9 represents extremely more important).4) Determine the preference of all the decision criteria over the Worst criterion.5) Compute optimal weights. 

According to the preliminary surveys, the most important criteria in the excellence maintenance model were identified as “organizational management”, “human-related factors”, and “organizational aspects”, respectively. The results of the BWM revealed that sub-criteria such as "top management support," "fund allocation and inventory resource management," and "appropriate maintenance strategies" had the greatest impact on maintenance management in agro-industries, with global weights of 0.108, 0.075, and 0.067, respectively. Additionally, these findings were compared to previous research conducted in the field of agricultural and production system maintenance models. 

The findings of this study could assist managers in revising and developing maintenance management models in the agro-industries. Future studies could consider calculating the interactions among the criteria that were omitted in this study to simplify the evaluation process which might improve the accuracy of weighing criteria. This can be achieved through the combination of the Decision Making Trial and Evaluation Laboratory (DEMATEL) and structural equation modeling.

Nowadays, machine vision systems are extensively used in agriculture. The application of this technology in the field can help preserve agricultural resources while reducing manual labor and production costs. In the field of agricultural automation, accurately detecting crop rows is recognized as a crucial and challenging issue for weed identification and the automatic guidance of machines. Therefore, it is necessary to explore practical solutions to optimize this process. Hence, the purpose of this study is the precise identification of basil cultivation rows to enable the automatic navigation of robots in the cultivation field.

In the first stage of this research, six images from each growth period of basil plants (third, fourth, and fifth week) were taken and weeds were removed from the area between the crop rows using three different methods of area opening, dimensional removal, and masking. In the next stage, six images of crop rows without weeds were examined by performing image processing operations and implementing several routing algorithms, namely, Hough transform, wavelet transform, Gabor filter, linear regression, and an additional algorithm proposed in this study. The output of each of these algorithms was compared with the ideal path identified by the user. For this purpose, after capturing an image, green areas were extracted from it by performing the segmentation process. By applying each of the routing algorithms to the image, plant cultivation lines were identified and their equations were determined. Finally, the performance of the designed robot was evaluated using the most appropriate routing algorithm.

Examining the performance of three different methods of weed removal in three periods of plant growth (third, fourth, and fifth week) showed that during this interval, the masking method had the lowest error rate compared to the ideal path and the shortest average operation time of 1.64 seconds, followed by the dimensional removal and the area opening methods. Comparing the routes detected by different routing algorithms with the ideal routes and according to the results of the t-test at 5% probability level, the order of the studied routing methods from the most superior is as follows: the proposed algorithm, Gabor filter, linear regression, Hough transform and wavelet transform algorithm. Overall, the proposed algorithm had the highest rate of adaptation to the ideal path (with an average error of 3.65 pixels) and the shortest operation time (4.79 seconds) and was selected as the most appropriate routing algorithm and the performance of the designed robot was evaluated using it.

A reliable crop row detection algorithm can reduce production costs and preserve the environment. In this study, the masking method was used for removing weeds from the images. The new proposed routing algorithm has superior performance when compared with common routing algorithms such as the Gabor filter, linear regression, Hough transform, and wavelet transform. Additionally, it was shown that the designed robot using the proposed algorithm (with an average error of 3.65 pixels) has the desired performance.

The development of mechanization and machine technology can have positive and negative effects on the economic, social, and environmental conditions of a region. Conflicts in these areas complicate the selection and optimization of sustainable mechanization systems. One of the basic questions in the selection of a sustainable agricultural mechanization system is how and with what methodology would it be possible to propose the closest mechanization model that will overcome the simultaneous contradictions between the three pillars of sustainability; taking into account the natural and technical limitations in agricultural production. What is the appropriate approach considering the economic, environmental, and social aspects? The current research aims to provide a framework for an optimal mechanization model to achieve the goals of agricultural sustainability so that it can be implemented and applied practically. It is possible to provide a model that addresses the conflicting economic, social, and environmental aspects by quantitatively optimizing the level of mechanization.

In this study, a framework is applied whereby contradictory goals of agricultural sustainability can be achieved simultaneously. After selecting the indices and data collection, by combining Shannon entropy and TOPSIS, the similarity index was obtained for each objective. The similarity indices and values of the Benefit-Cost Ratio calculated for each system were considered as coefficients of three objective (economic, social, and environmental) functions in multi-objective optimization. The multi-objective optimization model was applied to achieve sustainable mechanization patterns and was solved using the NSGA-II algorithm. For framework validation, paddy production mechanization systems in the Ramhormoz region located in southwestern Iran were analyzed with constraints: land, water, and machinery. The five mechanization systems of paddy production included puddled transplanted, un-puddled transplanted, water seeded, dry seeded, and, no-till.

Pareto-optimal solutions of different scenarios with water and machine constraints showed that this framework cannot only meet the sustainable goals, but also the optimal allocation of mechanization systems is identified and the effect of different scenarios under different constraints can be examined. The sustainability goals between the no-tillage and planting with puddling systems are highly contradictory. The no-tillage system has the highest score in the environmental aspect and the lowest score in the social and economic aspects. This modern system was developed in Ramhormoz three years ago and has faced technical, economic, and social challenges ever since. The cultivated area using this system was 43 hectares in 2019. Despite the speed and ease of planting with this system, and its direct environmental benefits, the possibility of fungal outbreaks is raised due to the presence of wheat residues from previous cultivation and the warm and humid environment of cultivation. Additionally, weed outbreaks caused by periodic irrigation have greatly affected the satisfaction and profitability of this system, leading to the highest amount of pesticides consumed among the studied systems. The results of multi-objective optimization of sustainable rice mechanization systems in Ramhormoz city showed that the total surface area of optimal point systems is in the range of 2700 to 3200 hectares, which is close to the area under rice cultivation in Ramhormoz (3310 hectares) and it indicates that the output of the model is according to the applied restrictions and close to reality. The limitation of machinery and water has made the two planting systems of un-puddled transplanting and dry-seeding better than other systems. Removing only the machinery restriction can lead to an increase in the area under rice cultivation by about 700 hectares. This means that the requirement for the development of sustainable rice cultivation in Ramhormoz is to strengthen and support modern mechanized systems of no-tillage, dry-seeding, and planting with puddling, with a focus on systems with less water consumption which are the systems with higher levels of mechanization. Without water limitation, if the model is subject to the current machinery limitations, the optimal mechanization systems are the more traditional ones such as transplanting without puddling and wet-seeding.

One of the most fundamental challenges in the development of mechanization is identifying systems that can best balance the economic, social, and environmental aspects of sustainability and minimize environmental damage whilst maximizing economic and social benefits. Using the framework for sustainable mechanization will not only accomplish sustainable goals in identifying the optimum agricultural mechanization level, but it will also allow researchers and implementers in the agricultural sector to examine the outcome of various scenarios under different constraints. This framework can be used to find the optimal model for mechanization of all stages of tillage, planting, harvesting, and post-harvest in diverse geographical areas.

Remote sensing is defined as data acquisition about an object or a phenomenon related to a geographic location without physical. The use of remote sensing data is expanding rapidly. Researchers have always been interested in accurately classifying land coverage phenomena using multispectral images. One of the factors that reduces the accuracy of the classification map is the existence of uneven surfaces and high-altitude areas. The presence of high-altitude points makes it difficult for the sensors to obtain accurate reflection information from the surface of the phenomena. Radar imagery used with the digital elevation model (DEM) is effective for identifying and determining altitude phenomena. Image fusion is a technique that uses two sensors with completely different specifications and takes advantage of both of the sensors' capabilities. In this study, the feasibility of employing the fusion technique to improve the overall accuracy of classifying land coverage phenomena using time series NDVI images of Sentinel 2 satellite imagery and PALSAR radar imagery of ALOS satellite was investigated. Additionally, the results of predicted and measured areas of fields under cultivation of wheat, barley, and canola were studied.

Thirteen Sentinel-2 multispectral satellite images with 10-meter spatial resolution from the Bajgah region in Fars province, Iran from Nov 2018 to June 2019 were downloaded at the Level-1C processing level to classify the cultivated lands and other phenomena. Ground truth data were collected through several field visits using handheld GPS to pinpoint different phenomena in the region of study. The seven classes of distinguished land coverage and phenomena include (1) Wheat, (2) Barley, (3) Canola, (4) Tree, (5) Residential regions, (6) Soil, and (7) others. After the preprocessing operations such as radiometric and atmospheric corrections using predefined built-in algorithms recommended by other researchers in ENVI 5.3, and cropping the region of interest (ROI) from the original image, the Normalized Difference Vegetation Index (NDVI) was calculated for each image. The DEM was obtained from the PALSAR sensor radar image with the 12.5-meter spatial resolution of the ALOS satellite. After preprocessing and cropping the ROI, a binary mask of radar images was created using threshold values of altitudes between 1764 and 1799 meters above the sea level in ENVI 5.3. The NDVI time series was then composed of all 13 images and integrated with radar images using the pixel-level integration method. The purpose of this process was to remove the high-altitude points in the study area that would reduce the accuracy of the classification map. The image fusion process was also performed using ENVI 5.3. The support Vector Machine (SVM) classification method was employed to train the classifier for both fused and unfused images as suggested by other researchers.To evaluate the effectiveness of image fusion, Commission and Omission errors, and the Overall accuracy were calculated using a Confusion matrix. To study the accuracy of the estimated area under cultivation of main crops in the region versus the actual measured values of the area, regression equation and percentage of difference were calculated.

Visual inspection of classified output maps shows the difference between the fused and unfused images in classifying similar classes such as buildings and structures versus regions covered with bare soil and lands under cultivation versus natural vegetation in high altitude points. Statistical metrics verified these visual evaluations.  The SVM algorithm in fusion mode resulted in 98.06% accuracy and 0.97 kappa coefficient, 7.5% higher accuracy than the unfused images.As stated earlier, the similarities between the soil class (stones and rocks in the mountains) and manmade buildings and infrastructures increase omission error and misclassification in unfused image classification. The same misclassification occurred for the visually similar croplands and shallow vegetation at high altitude points. These results were consistence with previous literature that reported the same misclassification in analogous classes. The predicted area under cultivation of wheat and barley were overestimated by 3 and 1.5 percent, respectively. However, for canola, the area was underestimated by 3.5 percent.

The main focus of this study was employing the image fusion technique and improving the classification accuracy of satellite imagery. Integration of PALSAR sensor data from ALOS radar satellite with multi-spectral imagery of Sentinel 2 satellite enhanced the classification accuracy of output maps by eliminating the high-altitude points and biases due to rocks and natural vegetation at hills and mountains. Statistical metrics such as the overall accuracy, Kappa coefficient, and commission and omission errors confirmed the visual findings of the fused vs. unfused classification maps.

Precision agriculture is a modern approach to farming that ensures the crops and soil receive exactly what they need for optimum health and productivity. Precision agriculture offers the potential to automate and simplify the collection and analysis of information. It allows management decisions to be quickly made and implemented in small areas of larger fields. Measuring acoustic signals with a cone penetrometer is an advanced and inexpensive method that provides a lot of information about the soil within the shortest amount of time and with the lowest cost. The texture of the soil determines the percentage of the constituents of the mineral part of the soil such as sand, silt, and clay.In this study, an acoustic penetrometer is developed to provide an accurate method for determining the soil texture. This system uses a microphone to record the sound produced by the cone-soil contact and correlates this data with the soil texture.

An acoustic cone penetrometer (ACPT) was designed to determine if there is a relationship between the sound produced at the cone-soil contact and soil particle size. Three types of cones with angles of 30, 45, and 60 degrees, diameter of 20.27 mm, and rod length of 300 mm according to ASAE standard S313.3 FEB1999ED (R2013) were used to determine the relationship between sound and soil texture and to choose the best angle. A microphone (20-20,000 Hz) suitable for fast dynamic responses was used to record the audio signals produced from the soil. Audio signals were stored online through the oscilloscope section of Matlab software. To create the controlled vertical movement of the cones, a mechanical mechanism with electronic controllers was designed. This mechanism can be connected to the rails of the soilbin available in Urmia University, Iran, and is made of a 5 hp electric motor with a gearbox, an inverter for controlling the rotational speed of the electric motor, and a digital ruler for recording vertical movement. Soil samples were tested in 19-liter bins.Acoustic signals received from the microphone were processed in the time-frequency domain using wavelet transform. In this research, Daubechi function type 3 is used to analyze acoustic signals. It is not possible to use the processed acoustic signals directly for statistical analysis. Therefore, the relevant features should be extracted from them.  From the 30 features of time domain signals, the most effective and main features include: SUM, Max, RMS, average, Var, kurtosis, and Moment4. They were ranked using the feature selection section of WEKA 3.9.2 software to avoid increasing the volume of calculations, increase processing speed, and reduce errors. The characteristic vector of the sub-signals of several different soil samples was analyzed to distinguish the soil type and constituents namely sand, silt, and clay.

The best type of cone was selected using WEKA software. The number of features in the d1 sub-signals was higher for the 45-degree cone, and it can be concluded that with this cone, the soil type can be better recognized.The average values of characteristics in clay, loam, and sand had an increasing trend, respectively, and were statistically significant with a probability of 1% and 5%.Acoustic signals for clay soil, which has a heavy texture and small particles, have minimum amplitude, and for loamy and sandy soils, they were observed as medium and maximum, respectively. This will cause the values of the selected features of clay soil to be low, and as a result, the average values, variance, and standard deviation are also low. They would be higher for loamy and sandy soil which have larger particles. It can be deduced that, as the size of the soil particles increases, the particles hitting the cone wall would become heavier and would affect the frequency and amplitude of the signal. This will result in the increase of signal amplitude values and, the sum, max, and mean values as well.

Among the sub-signals, the maximum effect of soil texture type changes was related to d1 sub-signals for the 45̊ cone, and these signals had more potential to identify the soil texture type. Among the features, the sum, average, VAR, and RMS were significant at 1% probability levels. Therefore, these features have more potential to detect the type of soil texture in the mentioned sub-signal. Additionally, the effect of soil texture change on Moment and Kurtosis characteristics was significant at 5% probability levels.

Anaerobic bacteria break down organic materials like animal manure, household trash, plant wastes, and sewage sludge during the anaerobic digestion process of biological materials and produce biogas. One of the main issues in using biogas is hydrogen sulfide (H2S), which can corrode pipelines and engines in concentrations between 50 and 10,000 ppm. One method for removing H2S from biogas with minimal investment and operation costs is biofiltration. Whether organic or inorganic, the biofilter's bed filling materials must adhere to certain standards including high contact surface area, high permeability, and high absorption. In this study, biochar and compost were used as bed particles in the biofilter to study the removal of H2S from the biogas flow in the lab. Afterward, kinetic modeling was used to describe the removal process numerically.

To remove H2S from the biogas, a lab-sized biofilter was constructed. Biochar and compost were employed separately as the material for the biofilter bed. Because of its high absorption capacity and porosity, biochar is a good choice for substrate and packed beds in biofilters. The biochar pieces used were broken into 10 mm long cylindrical pieces with a diameter of 5 mm. Compost was used as substrate particles because it contains nutrients for microorganisms. Compost granules with an average length of 7.5 mm and 3 mm in diameter were used in this study. For the biofilter reactor, each of these substrates was put inside a cylinder with a diameter of 6 cm and a height of 60 cm. The biofilter's bottom is where the biogas enters, and its top is where it exits. During the experiment, biogas flowed at a rate of 72 liters per hour. Mathematical modeling was used to conduct kinetic studies of the process to better comprehend and generalize the results. This method involves feeding the biofilter column with biogas that contains H2S while the biofilm is present on the surface of the biofilter bed particles. The bacteria in the biofilm change the gaseous H2S into the harmless substance sulfur and store it in their cells. The assumptions that form the foundation of the mathematical models are: the H2S concentration is uniform throughout the gas flow, the gas flow is constant, and the column's temperature is constant at a specific height.

In the beginning, biochar was used as a substrate in the biofilter to test its effectiveness, and the results obtained for removing H2S from the biogas were acceptable. H2S concentration in biogas was significantly reduced using biochar beds. It dropped from 300 ppm and 200 ppm to 50 ppm where the greatest H2S concentration reduction was achieved. The level of Methane in the biogas was not significantly impacted by the biofilter. This is regarded as a significant outcome when taking into account the goal which is producing biogas with a high concentration of methane. The H2S elimination effectiveness was 94% with the biochar bed and biogas input with 185 ppm H2S concentration. The removal efficiency reached 76% with the compost bed and input concentration of 70 ppm. Using mathematical models, the simulation was carried out by modifying the model's parameters until the predicted results closely matched the experimental data. It may be concluded that the suggested mathematical model is sufficient for the quantitative description of H2S removal from biogas utilizing biofilm in light of how closely the calculation results matched the experimental data. The only model parameter that was changed to make the model results almost identical to the experimental data was the value of the maximum specific growth rate (μmax) which has the greatest influence on the model results. The value of μmax for the biochar bed was calculated as 0.0000650 s-1 and for the compost bed at 70 ppm and 35 ppm concentrations as 0.0000071 s-1 and 0.0000035 s-1, respectively.

The primary objective of this study is to examine the removal of H2S from biogas using readily available and natural substrates. According to the findings, at a height of 60 cm, H2S concentration in biochar and compost beds decreased from 185 ppm to 11 ppm (removal efficiency: 94%) and from 70 ppm to 17 ppm (removal efficiency: 76%), respectively. The mathematical models that were created can quantify the H2S elimination process, and the μmax values in biochar and compost were calculated as 0.0000650 s-1 and 0.0000052 s-1, respectively.