sh rafiee

Increasing the production efficiency is an important goal in precision farming. The use of precision farming requires a lot of labor work. Also, due to the risk of agricultural operations, it is not recommended to do it directly by humans. Therefore, it is necessary for agricultural operations to be carried out automatically. For this reason, the application of robotics in agricultural environments, especially in the greenhouse, is increasing. The first step in automatic farming is autonomous navigation. For autonomous navigation, a robot must be the ability to understand its environment and recognize its position. In other words, a robot must be able to create a map of an unknown environment, locate itself on this map and finally plane for the path. This problem is solvable by Simultaneous Localization and Mapping (SLAM). The SLAM problem is a recursive estimation process. In the other words, when a robot moves in an unknown environment, mapping and localization errors increase incrementally. To reduce these two errors, a recursive estimation process is used to solve the SLAM problem.

In this research, two webcams, made by Microsoft Corporation with the resolution of 960×544, are connected to the computer via USB2 in order to produce a stereo parallel camera. For this study, we used a greenhouse that was located the Arak, Iran. Before taking stereo images, a camera path was designed in the greenhouse. This path may be either straight or curved. The designed path was implemented in the greenhouse. The entire path traversed by a stereo camera was 32.7 m and 150 stereo images were taken. Graph-SLAM algorithm was used for Simultaneous Localization and Mapping in the greenhouse. Using the ROS framework, the SLAM algorithm was designed with nodes and network for connecting the nodes.

For evaluation, the stereo camera locations, every step was measured manually and compared with the stereo camera locations that were estimated in the graph-SLAM algorithm. The position error was calculated through the Euclidean distance (DE) between the estimated points and the actual points. The results of this study showed that, the proposed algorithm has an average of error 0.0679412, standard deviation of 0.0456431 and root mean square error (RMSE) of 0.0075569 for camera localization.In this research, only a stereo camera was used to prepare a map of the environment, but other researches have used multiple sensor combinations. Another advantage of this research related to others was created a 3D map (point cloud) of the environment and loop closer detection. In the 3D map, in addition to determining the exact location of the plant, the height of the plant can also be estimated. Plant height estimate is important in some agricultural operations such as spot spray, harvesting and pruning.

Due to the risk of agricultural activities, the use of robotics is essential. Autonomous navigation is one of the branches of the robotics. For autonomous navigation, a map of environment and localization in this map is need. The purpose of our research was to provide simultaneous localization and mapping (SLAM) in agricultural environments. ROS is a strong framework for solving the SLAM problem. So that, this problem can be solved by combining different nodes in ROS. The method depended only on the information from the stereo camera because stereo camera provided exact distance information. We believe that this study will contribute to the field of autonomous robot applications in agriculture. In future studies, it is possible to use an actual robot in the greenhouse with various sensors for SLAM and path planning.

Knowledge about the quantity of municipal solid waste (MSW) generation plays a key role in formulating policies of waste management. So far, different methods have been applied to estimate the quantity of waste generation. In this study, eight specific forms of mathematical functions were evaluated to predict waste generation by the regression analysis method based on population.

The significance test of each model and the existence necessity of predictor parameters were performed using the F- and t-statistic, respectively. The statistical indicators of determination coefficient ( ), adjusted determination coefficient ( ), root mean square error (RMSE), mean bias error (MBE) and mean percentage error (MPE) were used for model’s goodness of fit. The predicted determination coefficient ( ) was calculated to assess the predictive ability of models by method of Leave-one-out cross validation.

The results showed that polynomial models of second order and more are not significant (at 0.01 level) despite good accuracy and are not suitable for long-term prediction. Linear, power and exponential models are best with equal to 0.942, 0.932 and 0.936 and  equal to 0.904, 0.893 and 0.898 respectively. However, the uncertainty was greater in the exponential model.

The status of waste generation was investigated in four scenarios based on growth rate of population (increasing, fixing and decreasing births) at Tehran metropolis in 2021-2051. In all scenarios, annual generation and per capita of waste are increased to 2051. The daily waste generation will increase to 12317 ton in 2051.

IntroductionAgricultural productions has been identified as a major contributor to atmospheric greenhouse gases (GHG) on a global scale with about 14% of global net CO2 emissions coming from agriculture. Identification and assessment of environmental impact in the production system will be leading to achieve the goals of sustainable development, which would be achieved by life cycle assessment. To find the relationship between inputs and outputs of a production process, artificial intelligence (AI) has drawn more attention rather than mathematical models to find the relationships between input and output variables by training, and produce results without any prior assumptions. The aims of this study were to life cycle assessment (LCA) of Alfalfa production flow and prediction of GWP (global warming potential) per ha produced alfalfa (kg CO2 eq.(ha alfalfa)-1) with respect to inputs using ANFIS.
Materials and MethodsThe sample size was calculated by using the Cochran method, to be equals 75, then the data were collected from 75 alfalfa farms in Bukan Township in Western Azerbaijan province using face to face questionnaire method. Functional unit and system boundary were determined one hectare of alfalfa and the farm gate, respectively. Inventory data in this study was three parts, included: consumed inputs in the alfalfa production, farm direct emissions from crop production and indirect emissions related to inputs processing stage. Direct Emissions from alfalfa cultivation include emissions to air, water and soil from the field. Data for the production of used inputs and calculation of direct emission were taken from the EcoInvent®3.0 database available in simapro8.2.3.0 software and World Food LCA Database (WFLD). Primary data along with calculated direct emissions were imported into and analyzed with the SimaPro8.2.3.0 software. The impact-evaluation method used was the CML-IA baseline V3.02 / World 2000. Damage assessment is a relatively new step in impact assessment. The purpose of damage assessment is to combine a number of impact category indicators into a damage category (also called area of protection). To assess the damage in this study, IMPACT 2002 V2.12 / IMPACT 2002 method was used. ANFIS is a multilayer feed-forward network which is applying to map an input space to an output space using a combination of neural network learning algorithms and fuzzy reasoning. In order to enable a system to deal with cognitive uncertainties in a manner more like humans, neural networks have been engaged with fuzzy logic, creating a new terminology called ‘‘neuro-fuzzy method. An ANFIS is used to map input characteristics to input membership functions (MFs), input MF to a set of if-then rules, rules to a set of output characteristics, output characteristics to output MFs, and the output MFs to a single valued output or a decision associated with the output. The main restriction of the ANFIS model is related to the number of input variables. If ANFIS inputs exceed five, the computational time and rule numbers will increase, so ANFIS will not be able to model output with respect to inputs. In this study, the number of inputs were ten, including machinery, diesel fuel, nitrogen, phosphate, electricity, water for irrigation, labor, pesticides, Manure and seed and GWP was as the model output signal. To solve this problem and employ all input variables, we proposed clustering input parameters to four groups. Correspondingly, the proposed model was developed using seven ANFIS sub-networks. To obtain the best results several modifications were made in the structure of ANFIS networks, and some parameters were calculated to compare the results of different models. Making a comparison between different topologies the employment of some indicators was a pivotal to get a good vision of various the structures, such as the correlation coefficient (R), Mean Square Error (MSE) and Root Mean Square Error (RMSE). In addition, for checking comparison between experimental and modeled data, the t-test was performed. The null hypothesis was equality of data average. To develop ANFIS models, MATLAB software (R2015a) was used.
Results and DiscussionImpact categories including Global warming potential (GWP), eutrophication potential (EP), human toxicity potential (HTP), terrestrial ecotoxicity potential (TEP), oxidant formation potential (OFP), acidification potential (AP), Abiotic depletion (AD) and Abiotic depletion (fossil fuels) were calculated as 13373 kg CO2 eq, 19.78 kg PO4-2 eq, 2054 kg 1,4-DCB eq, 38.7 kg 1,4-DCB eq, 3.84 kg Ethylene eq, 90.64 kg SO2 eq, 0.015 kg Sb eq and 205169 MJ, respectively. The results of damage assessment of alfalfa production revealed that electricity in three categories, human health damage, climate change and ecosystem quality had maximum role, but in the resources damage category was the largest share of damage related direct emissions. The value of the climate change was calculated as 13373 kg CO2 eq. The best structure was including five ANFIS network in the first layer, two network in the second layer and a network in output layer. Values of R, MSE and RMSE for the final ANFIS in k-fold model were 0.983, 0.107 and 0.327 and in C-means model were 0.999, 0.007 and 0.082, respectively. The p-value in t-test was 0.9987 that indicates non-significant difference between the mean of modeling and experimental data. Coefficient of determination (R2) between actual and predicted GWP based on the best k-fold and C-means models were 0.994 and 0.99, respectively. The coefficient of determination for these index demonstrated the suitability of the developed network for prediction of GWP of alfalfa production in the studied area.
ConclusionsBased on the results of this study, to reduce the emissions, electricity consumption should be reduced. Adapting of electro pumps power with the well depth and the amount of required water taken for field will be a possible solution to reduce the use of electricity in order to trigger of electro pumps and thus reducing of emissions related to it. In some situations, the type of mineral fertilizer is the main determinant of emissions at the whole farm level and changing the type of fertilizer could significantly reduce the environmental impact. Comparison of GWP modeling results using two methods of k-fold and C-means revealed that C-means method has higher accuracy in prediction of GWP. Also the high quantities for the determination coefficient related to both modeling methods demonstrates high correlation between actual and predicted data.

Stereo vision means the capability of extracting the depth based on analysis of two images taken from different angles of one scene. The result of stereo vision is a collection of three-dimensional points which describes the details of scene proportional to the resolution of the obtained images.
Vehicle automatic steering and crop growth monitoring are two important operations in agricultural precision. The essential aspects of an automated steering are position and orientation of the agricultural equipment in relation to crop row, detection of obstacles and design of path planning between the crop rows. The developed map can provide this information in the real time. Machine vision has the capabilities to perform these tasks in order to execute some operations such as cultivation, spraying and harvesting.
In greenhouse environment, it is possible to develop a map and perform an automatic control by detecting and localizing the cultivation platforms as the main moving obstacle. The current work was performed to meet a method based on the stereo vision for detecting and localizing platforms, and then, providing a two-dimensional map for cultivation platforms in the greenhouse environment.
In this research, two webcams, made by Microsoft Corporation with the resolution of 960×544, are connected to the computer via USB2 in order to produce a stereo parallel camera.
Due to the structure of cultivation platforms, the number of points in the point cloud will be decreased by extracting the only upper and lower edges of the platform. The proposed method in this work aims at extracting the edges based on depth discontinuous features in the region of platform edge.
By getting the disparity image of the platform edges from the rectified stereo images and translating its data to 3D-space, the point cloud model of the environments is constructed. Then by projecting the points to XZ plane and putting local maps together based on the visual odometry, global map of the environment is constructed.
To evaluate the accuracy of the obtained algorithm in estimation of the position of the corners, Euclidian distances of coordinates of the corners achieved by Leica Total Station and coordinates and resulted from local maps, were computed.
Results showed that the lower edges have been detected with better accuracy than the upper ones. Upper edges were not desirably extracted because of being close to the pots. In contrast, due to the distance between lower edge and the ground surface, lower edges were extracted with a higher quality. Since the upper and lower edges of the platform are in the same direction, the lower edges of the platform have been only used for producing an integrated map of the greenhouse environment. The total length of the edge of the cultivation platforms was 106.6 meter, that 94.79% of which, was detected by the proposed algorithm. Some regions of the edge of the platforms were not detected, since they were not located in the view angle of the stereo camera.
By the proposed algorithm, 83.33% of cultivation platforms’ corners, were detected with the average error of 0.07309 meter and mean squared error of 0.0076. Non- detected corners are due the fact that they were not located in the camera view angle. The maximum and minimum errors in the localization, according to the Euclidian distance, were 0.169 and 0.0001 meters, respectively.
Stereo vision is the perception of the depth of 3D with the disparity of the two images. In navigation, stereo vision is used for localizing the obstacles of movement. Cultivation platforms are the main obstacle of movement in greenhouses. Therefore, it is possible to design an integrated map of greenhouse environment and perform automatic control by localization of the cultivation platforms. In this research, the depth discontinuity feature in the locations of the edges, was used for the localization of the cultivation platforms’ edges. Using this feature, the size of the points required for establishing the point cloud model and also the associated processing time decreased, resulting improvement in the accuracy of determining coordination of the platforms’ corners.

The occurrence of landslides in Sanandaj cause significant financial losses and lot of stress on people living in the sliding regions.The purpose of this research is to separate and identify the sensitivity of landslide area and performance compared with two logistic regression model and fuzzy membership function with Gaussian and triangular.According to the review of internal and external studies and Field survey of Sanandaj, eight factors on the occurrence of landslides of the study area were considered effective. SPSS 19 software for data processing by logistic regression model and MATLAB software for using in fuzzy logic model and Arc GIS 9.2 software for landslide susceptibility map of the study area were used. Based on the coefficients obtained from the logistic regression model, equation Probabilistic and landslide susceptibility zonation map of the city of Sanandaj in Arc GIS 9.2 software and the sensitivity of the 4 classes low, medium, high sensitivity, and were very susceptible to preparation. Logistic regression model was evaluated using ROC curves and the area under the curve of 0.958 showed a high accuracy of the statistical models. Lithology and elevation of sea because of the statistical confidence level of 95% correlation with the landslide were introduced as the most influential factors in the logistic regression model. In the fuzzy logic model, normalized tables classes for each factor provide and using the final formula as two Triangular and Gaussian function were entered into the software. Results show that in the fuzzy model, the most landslides has occurred in areas with activities and road building, north aspect, slope between 30-40 percent, elevation of sea 1700 to 1500 m, 400 meters away from the fault, a distance of 100 -0 m from the road, constructive KSh2 (yellow with black shale’s and limestone Mykraity (shale Sanandaj), the soil was too shallow to shallow, rocky and gravel on schist with some sandy loam. Final results of comparing the validity of landslide susceptibility maps showed that in the case study the fuzzy logic with tow membership functions; triangular by 13.21 percentage of accuracy and Gaussian by 13.14 percentage of accuracy is more precise of the mapping than the logistic regression model by 1.9 percentage of accuracy.

In this study, the effects of drying air temperature and airflow rate on the drying duration of semi-dried and completely dried green tea were determined. Also, their effects on the ingredients of the extaction from green tea leaves were determined. Drying temperatures and airflow rates were: 50, 60, 70, 80 and 90 °C and 0.5, 1 and 1.5 m/s. The recorded data were: the drying duration and the amount of poly phenols available in the dried leaves extractions. A factorial statistical experiment was conducted by randomized complete block design. The results showed that with increasing temperature and drying airflow rate, the required time to reach half and completely dried samples are decreased.The variation in the airflow rate at high temperatures did not have any significant effect on drying duration. However, at the low temperatures the variation in airflow rate had significant influence on the drying duration. The amount of poly phenols extracted from green tea leaves dried at high temperatures was more than that for those dried at low temperatures. However, the airflow velocity did not show any significant effect on the indredients of the extraction. Due to destruction of the ingredients of green tea at both high and low temperatures of dryer, it is recommended to use the average temperatures in drying process

One of the important processes to preserve agricultural crops after harvest is drying. Since drying time and condition has considerable impacts on the process economic efficiency and energy consumption, therefore, kinetic modeling of drying can be a suitable way for optimum process control. In this study, banana thin layer drying was performed at four air temperatures (50, 60, 70 and 80°C), three air velocities (0.5, 1 and 1.5 m/s) and three banana thicknesses (2, 4 and 6 mm). Results showed that the Page and modified Page models have the best fit with the experimental data. These models had higher R2 and lower RMSE and χ 2. Artificial neural network was also used to optimize thin layer drying of banana. The results demonstrated that the experimental data can be fitted well with the network (correlation coefficient: 0.9984).

The objective of this study was to determine and compare several physical and hydrodynamic attributes between two commercial pomegranate varieties (Sweet Malase and White Leather) cultivated in Iran. Results showed that the geometric mean diameter (74. 61-82. 45mm)، volume (176-503mm3)، true density (970. 25-1028. 3kgm-3) and packaging coefficient (0. 48-0. 55) values had statistically significant differences at 1% probability level. Moreover، projected and surface areas of White Leather variety were 15 and 18% higher (P<0. 01) than Sweet Malase variety، respectively. Terminal velocity، dropping time and drag force of White Leather variety were -0. 17ms-1، 3. 42s and 17N، respectively، while the corresponding values for Sweet Malase variety were 0. 18ms-1، 3. 38s and 1. 94N، respectively. It was also found that، the difference of buoyancy force value between Sweet Malase (2. 41N) and White Leather (3. 25N) was significant (P<0. 01). Determination of these properties is necessary to the design of transportation، grading and processing equipments of the pomegranate varieties.

Olive oil extraction methods and technology can affect the quality and quantity of the extracted oil. In this study, qualitative and quantitative parameters were investigated for oil extracted using a hammer crusher at 720 and 1450 rpm at 30°, 40°, 60° C mixing temperatures. The data were analyzed using a complete randomized block factorial design using SPSS software with three replications. The results showed that increasing the temperature and rotary speed had more effect on acidity than did time. The only factoraffecting peroxide content was mixing unit temperature. For quantitative parameters, increasing time and mixing temperature resulted in a reduction of the pomace moisture content (increasing quantity). Also, increasing time, mixing temperature and rotational velocity of the crusher resulted in decreased fat content (increased quantity). Hence, to obtain high quality oil, it is recommended that oil extraction be carried out at 30°C mixing temperature and 720 rpm crusher rotary speed for 20 min mixing time.

Preeclampsia is an idiopathic multisystem disorder specific to human pregnancy and the puerperium and hematological abnormalities may develop in preeclamptic women. This study was designed to determine coagulation parameters in patients with severe preeclampsia in Shiraz, southern Iran. From 2002 to 2005, coagulation indices including platelet count, prothrombin time (PT), activated partial thromboplastin time (aPPT), plasma fibrinogen, and fibrin degradation products (FDP) were measured within 24 hours of admission for fifty women with severe preeclampsia and fifty normal pregnant women. The patients with coagulopathies were excluded. Abnormal coagulation indices were compared between the two groups. The mean value of platelet counts were significantly lower while the mean values of aPPT and FDP were higher in the preeclamptic patients. However, the mean values of plasma fibrinogen and PT did not show any statistical difference between these two groups. Fifty percent of the patients with severe preeclampsia showed thrombocytopenia, 10% prolonged PT, 30% prolonged PTT, 28% hypofibrinogenemia, and 32% elevated FDP. Prolonged aPTT was seen in 6% of patients with platelet counts of more than 150´103/mm3 at the admission time. However, these patients showed evidence of coagulopathies and needed to receive blood or blood products later in their hospital course. In case an abnormal platelet count or aPTT is detected in a patient with severe preeclampsia, a coagulopathic disorder should be clinically suspected.

Rice whitening is an important stage in rice milling process and improvement of the whitener machines has a remarkable effect on rice loss. To decrease the amounts of broken rice and losses, the rotor of the blade whitener was equipped with screw conveyor. To investigate the effects of rotor speed (in four levels: 600, 700, 800 and 900 rpm) and output rate (in three levels: 400, 500 and 600 kg/h) on quality of milled rice, a factorial design with randomized complete block experiments with three replications were conducted. In this study, Sorkheh variety (a medium rice variety), which is one of the prevailing varieties in Esfahan, was used. The results showed that the lowest breakage percentage (20.92%) occurred at rotor speed of 700 rpm with an output rate of 600 kg/h and the best degree of milling (6.33%) took place at rotor speed of 600 rpm with an output rate of 400 kg/h. The best rice quality (low broken rice and high degree of milling) was observed at rotor speed of 600 rpm with 500 kg/h output rate.

Improvement and optimization of the processing machines, including blade-type whitener, is one of the factors that could decrease in rough rice milling process. In this study, a rotor equipped with screw conveyor was used. The effect of rotor speed (in four levels: 600, 700, 800 and 900 rpm) and output rate (in three levels: 400, 500 and 600 kg/h) on losses of the Sazandegi rice variety were examined by using a factorial experiment based on randomized complete block design (RCBD) with three replications. In each test, the breakage percentage and degree of milling were measured. The results showed that the main and interaction effects of rotor speed and output rate on the breakage percentage were significant at the 1% level and the main effect of output rate and interaction of rotor speed and output rate on degree of milling were significant at the 1% level. Based on results, the highest of breakage percentage (38.25%) occurred at the rotor speed of 800 rpm with an output rate of 600 kg/h and the lowest of breakage percentage (20.79%) occurred at rotor speed of 700 rpm with an output rate of 400 kg/h (on the average of four rotor speeds). In addition, the highest degree of milling (6.45%) occurred at the rotor speed of 900 rpm with an output rate of 400 kg/h and the lowest degree of milling (2.15%) took place at rotor speed of 600 rpm with an output rate of 600 kg/h. However, in practice the rotor speed of 700 rpm with 400 kg/h output rate was recommended to be an optimum setting.

Rapid drying can increase brittleness of and induce internal cracks in the grain which predispose the product to breakage during subsequent activities. To fully understand the drying process requires an accurate description of the drying mechanism. Kernel equilibruim moisture content (EMC) is a property strongly related to agricultural products drying phenomena. Its accurate prediction can lead to optimisation of drying processes, especially in highly automated computer aided drying systems. In this study, a finite element formulation and solution of a set of coupled conductive heat and diffusive moisture transfer equations, to improve grain drying simulation of axisymmetric bodies are presented. Axisymmetric linear triangular elements with two degrees of freedom per node are used to discretize the rice grain in model for different equilibrium moisture content (from 7.5 to 0.12 d.b.%). For the purpose of this study, one medium grain, ‘Sepidrod CV.’, was used. During the thin layer drying, the drying air temperature of 69 °C and initial moisture content of 17.23 d.b.%, were adopted. A high relation has been observed when the output of model with 11.5 d.b.% EMC was compared to experimental data obtained by others. The least and most root mean square error analysis (RMSE) calculated for models at different EMC with experimental data were 0.0091 and 0.1025. The least and most mean relative deviation modulus were 1.394 and 5.129, respectively. Considering the mean errors of the models in relation to the obtained experimental data, the equilibrium moisture content for 11.5 d.b gave the best result.