Z. Khosrobeygi; Sh. Rafiee; S. S. Mohtasebi; A. Nasiri
Abstract
Introduction 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 ...
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Introduction 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. Materials and Methods 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. Results and Discussion 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. Conclusion 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.
O. Ghaderpour; Sh. Rafiee; M. Sharifi
Abstract
Introduction Agricultural 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 ...
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Introduction Agricultural 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 Methods The 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 Discussion Impact 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. Conclusion Based 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.
A. Nasiri; H. Mobli; S. Hosseinpour; Sh. Rafiee
Abstract
Introduction 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 ...
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Introduction 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. Materials and Methods 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 and Discussion 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. Conclusion 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.
M. Sharifi; A. Akram; Sh. Rafiee; M. Sabzehparvar
Abstract
Alborz province with an area of about 5121.7 km2 has about 0.31% of the total area of the country. The total arable area of the province is about 48954 hectares. Water, land and capital are the most important factors for agricultural production. By understanding the subjective beliefs, decision-making ...
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Alborz province with an area of about 5121.7 km2 has about 0.31% of the total area of the country. The total arable area of the province is about 48954 hectares. Water, land and capital are the most important factors for agricultural production. By understanding the subjective beliefs, decision-making criteria and economic incentives of local farmers, the priority of crops can be achieved with the maximum profitability of farmers and the least damage to the resources (water and land). The combination of Fuzzy Delphi techniques and methods of integrating analytical hierarchy process (AHP) can be an appropriate approach for achieving this goal. By employing the above combination of Fuzzy and AHP techniques, the priorities of the strategic agricultural crops in Alborz province achieved as wheat, barley, corn silage, alfalfa, cotton and canola, with final priority weighting factors of 0.496, 0.403, 0.354, 0.320, 0.183, and 0.090, respectively. By comparing the decision criteria it has been determined that the farmers prefer the amount of cultivation area, net income, production costs and livestock needs with the relative importance factors of 0.487, 0.410, 0.346 and 0.188, respectively. Among all prioritization criteria, the cultivated area had the highest priority. Water shortage, labor costs, lack of financial support, and governmental purchase allowance for wheat, were the main reasons for shifting the cultivated area towards wheat cultivation with total area of 14350 hectares.
M. Namdari; Sh. Rafiee; A. Jafari
Abstract
Farm management needs creative methods to success. FMEA (Failure Modes and Effects Analysis) is a new method to analyze potential reliability problems in the development cycle of the project, making it easier to take actions to overcome such issues, thus enhancing the reliability through design or process. ...
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Farm management needs creative methods to success. FMEA (Failure Modes and Effects Analysis) is a new method to analyze potential reliability problems in the development cycle of the project, making it easier to take actions to overcome such issues, thus enhancing the reliability through design or process. Anticipating these failure modes, being the central step in the analysis, needs to be carried on extensively, in order to prepare a list of maximum potential failure modes. Risk is measured in terms of Risk Priority Number (RPN) that is a product of occurrence, severity, and detection difficulty. This study attempted to improve clod mean weight diameter and soil inversion as indicators of tillage quality by FMEA methodology. The results showed that low soil moisture, slow speed of ploughing and great depth of ploughing is the most important factors that increase clod MWD with 900, 630 and 560 RPN, respectively. Also for soil inversion the slow speed of ploughing, not using coulter, low soil moisture and great depth of ploughing are important factors with 720, 648, 490 and 420 RPN. Using a split - split factorial experiment with 16 treatments and three replications also acknowledged the results of this method. After reforming the conditions and re-testing the experiment, results showed that clod MWD was reduced 20% and soil inversion increased 2% approximately. This study proposes the use of this technique in agricultural management.
