Design and Construction
S. I. Saedi; R. Alimardani; H. Mousazadeh; R. Salehi
Abstract
Introduction Global increase in the food demand and challenges regarding the water, energy and fertile soil has made it clear that current strategies are no longer efficient for maintaining food safety. Therefore, attention to novel, science-based, seasonal and climate-independent farming methods which ...
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Introduction Global increase in the food demand and challenges regarding the water, energy and fertile soil has made it clear that current strategies are no longer efficient for maintaining food safety. Therefore, attention to novel, science-based, seasonal and climate-independent farming methods which could result in the higher crop quality and quantity is an inescapable decision. Among all agricultural practices and technologies, intensive culture and hydroponic methods in controlled environments play an important role. Materials and Methods To address these challenges, an indoor solar-powered auto-irrigate rotary cropping system (SARCS) was designed and implemented. Arrangement of plants in the surface area of an open-ended drum makes it possible to use space rather than area to maximize the acreage. An embedded fuzzy control system managed the irrigation process based on the plant water requirement predictions, and photovoltaic panels (PVs) was responsible for system electrical energy provision. The drum rotates around its horizontal axis where LED lamps are positioned to provide light to plants. This structure causes the plants gain the light illumination efficiently while getting access to water accumulated in the secondary tank positioned beneath the drum. Fertigation fuzzy control was based on plant evapotranspiration (ET) estimations with temperature, humidity, and light as its inputs. The instantaneous estimated ETs which were measures for root substrate moisture were summed until reaching its critical value which is equivalent to plant readily available water (RAW). This tends to trigger a pump submerged in a primary tank to fill the secondary one up to a predefined height ruled by a level sensor. The solar energy system consisted of PVs, MPPT, inverter, and battery bank. The SARCS evaluation procedure included two valid lettuce cultivation in grow bags filled with the same proportions of perlite and coco peat as a root substrate. The first cultivation used water level sensors to rule the irrigation process (non-fuzzy) while the second one (fuzzy) were governed by fertigation cycle fuzzy control. Results and Discussion The results showed that employing these two modes increased lettuce planting density to about 12 times in the field culture and 4 times in the greenhouse. The energy consumption evaluation revealed that in fuzzy and non-fuzzy approaches the same amounts of energy were needed. But in fuzzy mode the amount of energy consumed per kilogram of marketable lettuce was 74.33% less than in non-fuzzy mode. Fuzzy and non-fuzzy modes utilized 58.81% and 48.41% of the total energy requirements from PVs, respectively. It was calculated that the solar system is able to supply 51.16 % of SARCS total annual energy requirements in Karaj Province. The results of water consumption evaluations revealed that the fuzzy approach could cut the needed water to 24%, and improved the marketable product to 74.47%. For producing one kilogram dry and fresh biomass, fuzzy mode used 50.41% and 55.53% less water than non-fuzzy, respectively. Furthermore, one kilogram marketable product in fuzzy approach needed 56.46% less water than in non-fuzzy. The averaged water needed for growing one lettuce plant in non-fuzzy and fuzzy modes were 15 times less than in field lettuce. The comparison of growth parameters of harvested lettuce in the two studied approaches revealed that fuzzy mode would have significantly higher results in all parameters. Conclusion The results suggested that the development of intensive culture strategies would play an important role in the sustainable agricultural production and food safety. Also, the solar energy utilization in farming practices could save fossil resources and decrease air pollutions. Finally, purposeful irrigation approaches which are based on plant water requirement predictions can significantly reduce the total water consumption and improve products quality. This strategy, therefore can be introduced to other farming practices such as field and greenhouse methods.
S. I. Saedi; R. Alimardani; H. Mousazadeh
Abstract
Introduction Global solar radiation is the sum of direct, diffuse, and reflected solar radiation. Weather forecasts, agricultural practices, and solar equipment development are three major fields that need proper information about solar radiation. Furthermore, sun in regarded as a huge source of renewable ...
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Introduction Global solar radiation is the sum of direct, diffuse, and reflected solar radiation. Weather forecasts, agricultural practices, and solar equipment development are three major fields that need proper information about solar radiation. Furthermore, sun in regarded as a huge source of renewable and clean energy which can be used in numerous applications to get rid of environmental impacts of non-renewable fossil fuels. Therefore, easy and fast estimation of daily global solar radiation would play an effective role is these affairs. Materials and Methods This study aimed at predicting the daily global solar radiation by means of artificial neural network (ANN) method, based on easy-to-gain weather data i.e. daily mean, minimum and maximum temperatures. Having a variety of climates with long-term valid weather data, Washington State, located at the northwestern part of USA was chosen for this purpose. It has a total number of 19 weather stations to cover all the State climates. First, a station with the largest number of valid historical weather data (Lind) was chosen to develop, validate, and test different ANN models. Three training algorithms i.e. Levenberg – Marquardt (LM), Scaled Conjugate Gradient (SCG), and Bayesian regularization (BR) were tested in one and two hidden layer networks each with up to 20 neurons to derive six best architectures. R, RMSE, MAPE, and scatter plots were considered to evaluate each network in all steps. In order to investigate the generalizability of the best six models, they were tested in other Washington State weather stations. The most accurate and general models was evaluated in an Iran sample weather station which was chosen to be Mashhad. Results and Discussion The variation of MSE for the three training functions in one hidden layer models for Lind station indicated that SCG converged weights and biases in shorter time than LM, and LM did that faster than BR. It means that SCG provided the fastest performance. However, the story for accuracies was different i.e. the BR, LM, and SCG algorithms provided the most accurate performances, respectively, both among one or two hidden layers. According to the evaluation criteria, six most accurate derived models out of 1260 tested ones for Lind station was 3-14-1 and 3-11-19-1 with LM, 3-20-1 and 3-20-19-1 with BR, and 3-9-1 and 3-20-17-1 with SCG training algorithm, and 3-20-19-1 topology with BR showed the best performance out of all architectures. Results of the evaluation of the six accurate models in the remaining 18 stations of Washington State proved that regardless of the climate, in each weather station, BR with its inherent automatic regularization, provided the most accurate models (0.87 67.41 %), and then SCG (0.90>R>0.83, 3.91>RMSEMAPE > 77.28 %). Therefore, the Bayesian neural networks, which showed the best performance among all Washington State weather stations, were evaluated for Mashhad station, as an Iran sample climate. The results proved the ability of the said networks for this climate (R=0.82, RMSE=3.92 MJm-2, MAPE=79.92%). Conclusion The results indicated that the Bayesian neural networks are capable of predicting global solar radiation with minimum inputs in different climates. This was concluded both in Washington State weather stations, which has a variety of climates, and also in Mashhad as an Iran sample weather station. These models would eliminate the need for complex climate-dependent mathematical relations or other models which are mostly dependent on many inputs. So, this algorithm would be a good means first in weather forecast practices, also in the design and development of solar assisted equipment, as well as in managerial practices in agriculture when monitoring crop solar-dependent processes like photosynthesis and evapotranspiration.
A. Rohani; S. I. Saedi; H. Gerailue; M. H. Aghkhani
Abstract
Introduction: Fast and accurate determination of geometrical properties of agricultural products has many applications in agricultural operations like planting, cultivating, harvesting and post-harvesting. Calculations related to storing, shipping and storage-coating materials as well as peeling time ...
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Introduction: Fast and accurate determination of geometrical properties of agricultural products has many applications in agricultural operations like planting, cultivating, harvesting and post-harvesting. Calculations related to storing, shipping and storage-coating materials as well as peeling time and surface-microbial concentrations are some applications of estimating product volume and surface area. Sphericity is also a parameter by which the shape differences between fruits, vegetables, grains and seeds can be quantified. This parameter is important in grading systems and inspecting rolling capability of agricultural products. Bayram presented a new dimensional method and equation to calculate the sphericity of certain shapesand some granular food materials (Bayram, 2005). Kumar and Mathew proposed atheoretically soundmethod for estimating the surface area of ellipsoidal food materials (Kumar and Mathew, 2003). Clayton et al. used non-linear regression models for calculation of apple surface area using the fruit mass or volume (Clayton et al., 1995). Humeida and Hobani predicted surface area and volume of pomegranates based on the weight and geometrical diametermean (Humeida and Hobani, 1993). Wang and Nguang designeda low cost sensor system to automatically compute the volume and surface area of axi-symmetricagricultural products such as eggs, lemons, limes and tamarillos (Wang and Nguang, 2007). The main objective of this study was to investigate the potential of Artificial Neural Network (ANN) technique as an alternative method to predict the volume, surface area and sphericity of pomegranates.
Materials and methods: The water displacement method (WDM) was used for measuring the actual volume of pomegranates. Also, the sphericity and surface area are computed by using analytical methods. In this study, the neural MLP models were designed based upon the three nominal diameters of pomegranatesas variable inputs, while the output model consisted of each of the three parameters including the volume, sphericity and surface area. Priorto any ANN training process, the data normalized over the range of [0, 1]. Fig. 1 shows a MLP with one hidden layer. In this study, back-propagation with declininglearning-rate factor (BDLRF) training algorithm was employed. The mean absolute percentage error (MAPE) and the coefficient of determinationof the linear regression line between the predicted values fromthe MLP model and the actual output were used to evaluate the performance of the model.
Results and Discussion: The number of neurons in the hidden layerand also theoptimal values for the learning parameters η and αwere selected bytrial and error method. The bestresult was achieved with five neurons in the hidden layer. The results showed thatthe optimum modelof performance was obtained at constant momentum termequal to 0.8 and learning rate equal to 0.9. In this study, 300 epochs were selected as the starting points of the BDLRF. Some statistical characteristics of the actual values of volume were estimated by WDM, surface area was computed by equation (3) and sphericity of pomegranates was computed by equation (1) and the predicted values of them using the neural network method were shown in Table 1. The obtained results verified that the differences between theactual values and the estimated ones can be ignored. But, the predicted values of the volume using the MLP model in comparison with equation (2) are much closer to the actual values. Statistical comparisons of desired and predicted data and the corresponding p values are given in Table 2. The results showed that P-value was greater than 0.08 in all cases. Therefore, there was no significant difference between the statistical parameters. However, the P-value for equation 2 is much less than that of the MLP model. The results shown in Figures 2, 3 and 4 show that the coefficients of determination between actual and predicted data were greater than 0.9. Considering all the results in our study, the MLP model is more accurate than the WDM and analytical methods.
Conclusions: In this paper, we first measure the actual volume of the pomegranate using WDM and equation (2). Also, assuming an elliptical fruit, the sphericity and surface area are computed analytically based on the three nominal diameters of a pomegranate. Finally, the results of achievements of the MLP designed revealed that the MLP model could be successfully applied to the prediction of thesphericity and surface area. Therefore, the MLP model can be a viable alternative to the analytical methods. However, this is possible only if there is a precise way to compute the three nominal diameters of pomegranates. In addition, according to the MAPE, the accuracy of the MLP model in prediction of volume of pomegranates was twicethe analytical method.
M. H. Aghkhani; M. H. Abbaspour-Fard; M. R. Bayati; H. Mortezapour; S. I. Saedi; A. Moghimi
Abstract
Drying is a high energy consuming process. Solar drying is one of the most popular methods for dehydration of agricultural products. In the present study, the performance of a forced convection solar dryer equipped with recycling air system and desiccant chamber was investigated. The solar dryer is comprised ...
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Drying is a high energy consuming process. Solar drying is one of the most popular methods for dehydration of agricultural products. In the present study, the performance of a forced convection solar dryer equipped with recycling air system and desiccant chamber was investigated. The solar dryer is comprised of solar collector, drying chamber, silica jell desiccant chamber, air ducts, fan and measuring and controlling system. Drying rate and energy consumption in three levels of air temperature (40, 45 and 50 oC) and two modes of drying (with recycling air and no-recycling with open duct system) were measured and compared. The results showed that increasing the drying air temperature decreased the drying time and increased the energy consumption in the mode of non-recycling air system. The dryer efficiency and drying rate were better in the mode of recycling air system than open duct system. The highest dryer efficiency was obtained from drying air temperature of 50 oC and the mode of recycling air system. In general, the efficiency of solar collector and the highest efficiency of the dryer were 0.34 and 0.41, respectively.
S. I. Saedi; M. H. Aghkhani; A. Farzad
Abstract
Disk plows are one of the most important tillage tools. Two way (reversible) disk plows can perform continues plowing. So they can save time and costs and hence improve overall efficiency. In this study, a “two-way” disk plow was designed based on a λ-formed straight-line, four-bar ...
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Disk plows are one of the most important tillage tools. Two way (reversible) disk plows can perform continues plowing. So they can save time and costs and hence improve overall efficiency. In this study, a “two-way” disk plow was designed based on a λ-formed straight-line, four-bar linkage (Daniel mechanism). This design contains disk and rear wheel reversing mechanism, stabilization mechanism of the plow, a disk angle adjustment tool and transport condition for safe operation of the mechanism. Disk reversing mechanism was designed based on a geometrical analysis considering working condition of the disk plow. The suitable displacement of the plow’s frame was achieved by dimensional analysis of Daniel mechanism and a derived mathematical equation. The rear wheel mechanism was made by means of adding a slotted link to the previous four-bar linkage. The synthesized five-bar linkage was then analyzed for its kinematical and force conditions. For each analysis, related diagrams were plotted and discussed. This innovation has the advantages of low production cost and maintenance as well as easy operation, because of its design simplicity with minimum mechanical auxiliaries. The modeling and analysis was done by the aid of CATIA software.
