Ali Farahmandi; Mojtaba Naderi-Boldaji; Maral Ajamian; Mahdi Ghasemi-Varnamkhasti; Saman Abdanan
Abstract
Sugarcane is an important crop in sugar production in the country widely produced in Khuzestan province. After harvesting the sugarcane in the field and when delivered to the sugar factory, the cane is shredded. In this study, the dielectric spectroscopy technique in the frequency range of 0-100 MHz ...
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Sugarcane is an important crop in sugar production in the country widely produced in Khuzestan province. After harvesting the sugarcane in the field and when delivered to the sugar factory, the cane is shredded. In this study, the dielectric spectroscopy technique in the frequency range of 0-100 MHz was evaluated for measurement of sucrose concentration in shredded cane using a cylindrical parallel-plate sensor with compaction of the shredded cane. Fifty five samples of shredded cane were taken from the sugar production line of Salman Farsi factory during the harvesting season of 2021-2022 and their reference properties including sugar concentration with three indicators of Brix using refractometry method, Pol using simple polarimetry method and the sucrose measured using double polarimetry were measured. The samples were subjected to dielectric spectroscopy using the capacitance sensor and the dielectric spectra were analyzed for sugar concentration prediction. Multivariate regression analyses of partial least-sugare regresseion (PLSR), principal component regression (PCR) and support vector regression were applied for development of prediction models of sugar concentration variables. Validation of the prediction model of PLSR showed a stronger prediction of sucrose (R2= 0.79, RMSE= 0.89, RPD= 2.22) measured using the double-polarimetry as compared to Pol (R2= 0.76, RMSE= 0.8, RPD= 2.07) and Brix (R2= 0.77, RMSE= 0.89, RPD= 2). This result was explained with higher number of OH groups of sucrose molecule as compared to the other sugars existing in the sugarcane juice and the dominant effect of sucrose on the dielectric characteristics of the juice.
Azam Asadi; Mojtaba Naderi; Amin Lotfalian; Mahdi Ghasemi-Varnamkhasti; Saman Abdanan
Abstract
Determination of sugar concentration of sugar beet in sugar factories is of great importance and is a basis for valuation of the sugar beet as well as assessment of the sugar production process in the factory. In this study with the aim of development of a non-destructive method for measurement of the ...
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Determination of sugar concentration of sugar beet in sugar factories is of great importance and is a basis for valuation of the sugar beet as well as assessment of the sugar production process in the factory. In this study with the aim of development of a non-destructive method for measurement of the sugar concentration of sugar beet in terms of Brix, a proximal dielectric sensor was developed and evaluated. This sensor is an open-end coaxial probe consisting of two metallic concentric ring electrodes which is connected to function generator and spectrum analyzer through coaxial cables. One hundred random samples of sugar beet were selected and measured by the dielectric sensor. Dielectric spectra i.e. amplitude as a function of frequency were obtained in the frequency range of 0-150 MHz. The reference Brix was measured with extracting juice from the points of dielectric measurement using a refractometer. The results showed that in the ranges of 30-50 and 120-140 MHz, the dielectric spectra varied noticeably in relation with variations in sugar beet Brix so that the amplitude decreased with increasing the Brix. The partial least square regression (PLSR) method could model the Brix as a function of the dielectric spectra variables with R2= 0.81 and RMSE of 0.72 Brix. The results of the study indicated that the dielectric sensor and measurement method was a simple and reliable method for non-destructive measuring of sugar beet Brix.
Hadi Orak; S. Abdanan-Mehdizade
Abstract
One of the objectives of precision agriculture is to minimize the volume of herbicide application by using weed management systems. To achieve this goal, a system based on image processing techniques was developed to detect weeds. In the proposed method, HSV color space was used to discriminate ...
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One of the objectives of precision agriculture is to minimize the volume of herbicide application by using weed management systems. To achieve this goal, a system based on image processing techniques was developed to detect weeds. In the proposed method, HSV color space was used to discriminate between vegetation and background, and between crops and weeds. In this research, the H component of HSV color space along with suitable erosion and dilation were used to automatically segment background (soil) and foreground (weed). According to what was obtained, the algorithm could identify weed from sugar beet plants with the accuracy of 94%. The intelligent sprayer system, in the field trials, in comparison with conventional sprayers (Buferagri sprayer), reduced 67.86% of volume of herbicide due to application of computer vision. The use of this method, as an intelligent sprayer system in sugar beet fields, is recommended.
H. Orak; S. Abdanan-Mehdizadeh
Abstract
A large amount of herbicide is being used for controlling weeds in agricultural, lawns, sport fields on yearly basis. This causes environmental pollution and economic concerns. To reduce the use of herbicides, hand labor may be the best way of removing weeds. It is, however, costly and time consuming. ...
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A large amount of herbicide is being used for controlling weeds in agricultural, lawns, sport fields on yearly basis. This causes environmental pollution and economic concerns. To reduce the use of herbicides, hand labor may be the best way of removing weeds. It is, however, costly and time consuming. In this paper, two methods of distinguishing weeds from lawns using computer vision techniques are proposed. Due to the fact that the weeds have different colors and identification of them from grass is not possible; therefore, an algorithm was developed based on the assumption that the grass area should contain more edges while the weed area is smoother than the grass area. For identification of weed/grass two methods were used, namely: Bayesian Classifier (BO) and morphology (MO. Results indicated that correct weed identification rates for MO and BO methods were 89.58% and 80.42% respectively. Furthermore, from results obtained it can be concluded that herbicide usage was reduced more than 70%, which from economical point of view as well as reduction of environmental pollution is of great importance.
Saman Abdanan
Abstract
The purpose of this research was to introduce a new laboratory test procedure which could be used under field condition. In this context, the performance of a pneumatic planter was investigated under laboratory conditions for maize, castor, fababean, sorghum, sugarbeet, watermelon and cucumber seeds. ...
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The purpose of this research was to introduce a new laboratory test procedure which could be used under field condition. In this context, the performance of a pneumatic planter was investigated under laboratory conditions for maize, castor, fababean, sorghum, sugarbeet, watermelon and cucumber seeds. The effect of operational speed and vacuum pressure were evaluated by examining the quality of feed index, precision in spacing (coefficient of variation), miss index and multiple indexes. The most suitable operating parameter values for maize, castor, sorghum and sugar beet seeds were obtained at the first level of operating speed and 4.0 kpa pressure; for watermelon seed: second level of speed and 4.5 kpa pressure; for cucumber seed: first level of speed and 4.5 kpa pressure. Furthermore, in order to determine the relationship between most important operating parameters affecting the performance of the Pneumatic metering device and seed physical properties, regression models were developed. According to the results, the vacuum pressure of Pneumatic planter could suitability and acceptably be described by two final models with values of root mean square error 6.7×10-2 and 5.7×10-2 and reduced chi-square 8.2×10-2 and 5.6×10-2 for the first and second model, respectively.
