A. Ajili Lahiji; A. Mohammadi Torkashvand; A. Mehnatkesh; M.N. Navidi
Abstract
Introduction In order to land classification for agricultural and natural resources, the most important criterion and factor is the production or yield of the lands. The best way to evaluate a method is to interact between the yield and potential of the product with the land specification involved in ...
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Introduction In order to land classification for agricultural and natural resources, the most important criterion and factor is the production or yield of the lands. The best way to evaluate a method is to interact between the yield and potential of the product with the land specification involved in the production. One of the new methods in the land evaluation for different uses is the modeling or simulation of the intended use. Artificial neural networks are one of the new tools that are used today to simulate yield and determine the effective factors in the production of agricultural crops. New land evaluation methods include different modeling techniques, which these new methods, including simulations, are used in land valuation methods to test the ability of models for determining the relationship mentioned. An artificial neural network is one of the techniques that can do nonlinear analysis. It is important to recognize the most important input parameters to the predictive models of olive yield, which are also considered to be effective variables in production. Through the process of sensitivity analysis, valuable information about the sensitivity of the model to its input variables can be provided to the designer and model architect. In north of Iran, in the southern areas of Gilan Province, the most important olive gardens of the country are located in the southern part of the country. Different soil and water, topographical and climatic factors affect the yield of olive orchards.
Materials and Methods Climatic data, soil and water characteristics, topographic characteristics and leaf nutrition elements as input variables and olive yield were used as output models. Twenty-eight factors were selected as the most important factors or variables affecting the yield of olive orchards. These input variables included soil properties: EC, (TNV), organic carbon percentage, available phosphorus, available potassium, clay percentage and silt percentage; irrigation water characteristics including: EC and content of irrigation water; topographic characteristics including: altitude and the slope; the concentration of nutrients in leaves included: nitrogen, phosphorus, potassium, iron and zinc; climatic factors including: sunny hours, evaporation rate, average temperature and olive yield were considered as output of artificial neural network model.
Results and Discussion Using the MATLAB software by artificial neural network, the best structure of this network was obtained for the component of the yield of olive. The trained structure had 28 input nodes in 5 groups and one output node. The number of hidden nodes, 38 nodes and the most appropriate number of repetitive learning based on the test and error, 20 were determined for olive product yield. After determining the best structure of the neural network with a R-test of 80%, using Hill's sensitivity analysis, the model's response to each of the input variables was studied and the most important factors influencing the yield of olive oil were obtained. Based on the results of sensitivity analysis, the most important parameters affecting the yield of olive are content of leaf nitrogen, soil phosphorus, winter evaporation, summer evaporation, average autumn temperature, summer sunshine, leaf potassium, salinity, salinity, and slope, respectively. In general, this study showed that the amount of nitrogen and phosphorous is the most important factor for the production of olives, and then variables that are related to the amount of water in the soil, (summer evaporation, average autumn temperature, summer sunshine), were identified as important. The concentration of potassium, iron and zinc in the leaf as a nutrient element and water and soil salinity from stressors for plants and slope are important topographical factors that affected the amount of soil depth and soil water content as the most important factors in olive crop production.
Conclusion In brief, the role of micro and macro nutrients and the factors affecting the maintenance of water in the soil and providing moisture for the plant. In addition to nutrients, gardeners should consider soil and water salinity, which is a stressor for plants, as well as slope, which is important topography factor that effective on soil depth and water available and nutrients for plants as the most important factors in olive production.
hossein sabourifard; Azim qaesmnezhad; Khodayar hemmati; Aboutaleb hezarjaribi; Mahmoudreza bahrami; Fahimeh nosrati
Abstract
Introduction: One of the most important requirements in planning production and processing of medicinal plants in order to obtain high yield and high-quality is the initial assessment of the physical and chemical properties of soil, which reduces the production cost by avoiding the use of unnecessary ...
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Introduction: One of the most important requirements in planning production and processing of medicinal plants in order to obtain high yield and high-quality is the initial assessment of the physical and chemical properties of soil, which reduces the production cost by avoiding the use of unnecessary soil analysis. Summer savory (Satureja hortensis L.) is one the most widely used medicinal plants that quality index of plant is related to the quantity and the constituent of its essential oil content. Understanding the relations between the quantity and quality of medicinal plants with the very physical and chemical properties of soil is very complex and the estimation of parameters changes of medicinal plants affect by soil quality characteristics is more difficult. Today, with the arrival of multivariable regression models and artificial lattice models in the research, many complex relationships found in nature is understandable. Hence the need for estimation the biomass yield of savory using fast, cheap and with acceptable accuracy is feeling.
Materials and Methods: The present study was performed at the Agricultural Research Station Neyshabur as pot experiment based on a completely randomized design with three replications. Around 53 soil samples were collected from different parts of Neyshabur city, and soil texture, organic matter, pH, salinity, phosphorus, potassium, nitrogen and carbon content were selected as the easily available parameters. Before planting the parameters were measured in laboratory. Approximately 90 days after planting seeds in pots containing soil samples, the sampling of plants was done based on the treatments. For drying, samples were placed for 24 hours in an oven at 40 °C. Finally, the relationship between the biomass yield and easily available soil parameters was determined using artificial neural network by Matlab7.9 software.
Results and Discussion: The results showed that soil variability, is a key element in the management of valuable information on soil properties within a field and valuable information on soil properties within a field nature puts at our disposal. In yield modeling with 10 parameters for 53 soil samples, the best makeup hidden layer with Levenberg-Marquardt algorithm training as a hidden layer, 58 neurons, logsig threshold function for hidden layer and Tansig for the output layer were selected. High values of R2 and low levels of RMSE mentioned the proximity of the forecast data with measurement data and high accuracy of the model in summer savory biomass yield estimation. To obtain the most sensitive parameters, the sensitivity analysis was calculated using no-sensitive coefficient. So that, if the coefficient of a sensitive parameter is more than 1.0, the mentioned parameter, is one of the critical parameters of model. Accordingly, the parameters of organic carbon, nitrogen, phosphorus, organic matter, potassium, pH, salinity, clay, silt and sand respectively were selected as the most sensitive parameters. The addition of input parameters increases the value of R2 and reduces the RMSE during training, validation and test stages. This represents an increasing in the accuracy of model in estimation of biomass yield via increasing the input parameters. Models 1(soil texture) and 2(carbon) are not enough strong for biomass yield estimation. With increasing the experiment from 1 to 2, the potency of the neural network model 3(soil texture + carbon) significantly increased. Thus with an overview, the model No. 3 suggested as an improved model because with the minimum number of imputes produced equal output comparing the models with more inputs.
Conclusions: Based on the obtained results, it seems that with the improvement of artificial neural network models and determining appropriate parameters, results to understanding the soil factors involved in the formation of savory plant biomass and better planning. Till leads to a cheaper and better product. Also, results showed that the artificial neural network has high accuracy in estimating the biomass plant Summer Savory. So that, the 80% of yield variability of the study area, presents by using the data of 10 readily available properties of the soil. Yield biomass of savory, largely depends on the soil texture, organic matter, carbon and the minerals of the soil. Since, this study is the first work to estimate the biomass of medicinal plants using artificial neural network, therefore recommended to use this method to estimate the yield and essential oil of other medicinal plants.
