Journal of Researches in Mechanics of Agricultural Machinery

Abstract Nowadays, application of machine vision techniques had been extensively used in agriculture and particularly in food industries. This system can be used in quality separation, especially for valuable products such as walnut. The high nutritional value of walnuts has caused the crop to be widely processed in many processed foods. The texture is one of the most important features of agricultural crops which has been widely applied in food industry for quality evaluation. The texture of images reflects changes in pixel intensity values, which may include information from the geometric structure of objects. In this study, the possibility of walnut separating in three categories, based on quality, including: light-intact , dark-intact and damaged kernel using image processing and color systems such as RGB, HSV and L*a*b* on Kaghazi varieties was investigated. The machine vision system includes a lighting box, a camera (model SC-W?? SONY with resolution of ? mega pixels), a computer and MATLAB software. So that, all samples are captured in RGB color system, then using transfer functions the other color systems components were calculated. Also, separation of intact samples from non-intact was evaluated using statistical analyzing on color space of RGB, L*a*b* and HSV. The results showed that in the RGB color space using of components of R (redness intensity) and G (green color intensity), and in the HSV color space based on component H and V, separation of healthy samples was possible. The success of this method was ??%. Also, in the L*a*b* color space, components of L* and b* be able to clear healthy samples from the other two categories. The success of this method was ??%. However, separating dark-intact samples from damaged samples were not possible because of overlapping of colors area. In case of surface tissue indices, contrast and energy were able to separate intact samples from non-intact samples.

Abstract Investigating the properties of a rice single kernel is one of the methods used to determine the quality of grain bulk. In addition, one of the major problems in predicting the quality properties of rice is the high cost of conducting laboratory experiments in food industry. Therefore, the objective of this study was to predict some quality properties of white rice via the use of bending test in three varieties of white rice (Khazar, Dorfak and Kadus) at two levels of moisture content ? and ??% w. b. In this study, the response surface methodology (RSM) (historical data design) was used to model and estimate quality properties (Amylose content (AC), Protein content (PC), Gelatinization Temperature (GT), Gel Consistency (GC), Minimum viscosity (Min. V), Peak Viscosity (PV), Final Viscosity (FV), Breakdown viscosity (BD.V), Setback Viscosity (SB.V), Peak Time (PT) and Pasting temperature (Pasting. T)) of white rice through analyzing independent variables (strength value of rice single kernel in deformation of ?.?? mm, maximum rupture force, maximum deformation, rupture energy, tangent modulus at deformation of ?.?? mm, maximum tangent modulus, secant modulus at deformation of ?.?? mm and maximum secant modulus). The results of ANOVA, showed that all models are significant at probably levels of (P < ?.??) and (P < ?.??). According to the results, the most appropriate model for prediction of the response variables was the linear-Interaction (?FL) model whit the coefficient of determination (R? ? ?.??). In addition, Optimum conditions of independent variables were determined based on the maximum goal or the minimization of response variables. The desirability degree of the optimization was (R? ? ?.??). The results indicated that the response surface methodology can be quite useful in optimizing the models developed for predicting the quality properties of white rice from the strength properties of a single kernel.

Abstract The quail egg is a without cholesterol food and riches of different vitamins. This food expunges the radioactive particle and the carcinogen of human’s body. For mechanizing its preparation process, the quality parameters should be modeled with computer techniques. Therefore in this research, the image processing technique was used to determine the physical properties of quail egg, then egg volume was calculated by slicing each egg and rotating the slices around its main axis. Furthermore to determine the egg weight, a regression multi-variable model and a neural network model were developed. The results of suggested methods and models in this research were compared with real values and previous methods (Hoyt model). Finally, the influence of nutrition, ventilation and female quail age on quality parameters of eggs was determined. By comparing the real volume of eggs with the Hoyt model, accuracy was ??% and with the slices-rotation method, accuracy was ???% which well shows the importance of slices-rotation method. By comparing the real weight of eggs with the Hoyt model, accuracy was ??%, with the regression multi-variable model, accuracy was ??%, and with the neural network model, accuracy was ??%. The neural network model not only presented the egg weight with high accuracy but also considered the effect of laying time which is very important for the egg weight estimation. After comparing the results, it was shown that the nutrition, ventilation and female quail age influence egg quality. The eggs of younger females were limited to spherical geometry that may be due to the mother's womb not being expanded. Furthermore, the female quails in topper cages which had better nutrition and ventilation showed more weighting and sizing. These results show that coupled the image processing and artificial neural network techniques can help very well to mechanize the preparation process of quail egg.

Abstract In this research, the adaptive neuro-fuzzy inference system was used for predicting the parameters related to traction of tractor included of drawbar power (DP), drive wheels slippage (S), traction efficiency (TE) and overall energy efficiency (OEE) in tractor- implement combination under the effect of independent variables included of tine type (subsoiler and paraplow), depth (30, 40 and 50 cm) and forward speed (1.8, 2.3, 2.9 and 3.5 km/h) during subsoiling operation. The field data were used to create the regression and ANFIS models for predicting the studied parameters and the results of them were compared together. The field results showed that all independent variables were effective on the studied parameters except TE. The increment of forward speed and depth resulted in increase of S, DP, OEE and decrease of TE. Moreover, with considering the studied parameters, the paraplow tine was more commodious than subsoiler tine. The results of ANFIS part showed that about S, DP, TE and OEE, the membership functions of Trimf, dsigmf, Primf and Gaussmf with the mean square error of 0.0159, 0.0231, 0.0212 and 0.0224 also correlation coefficient of 0.9996, 0.9999, 0.9985 and 0.9997 caused the best models to predict, respectively. ANFIS models had higher accuracy than regression models and it could be calculated the model outlet for a special inlet using ANFIS outlet surfaces.

Abstract The use of digital image processing (DIP) has made it possible to exactly locate fruits to survey the performance. Moreover, advancements in the use of sensors and the robot technology have provided an opportunity to develop mechatronic crop harvesting systems as agricultural tools to facilitate gardening and harvesting mechanization. Walnuts are among crops that require proper and science-based management when harvesting. Traditional walnut harvesting has a high cost and high risk for Harvesting workers. In addition to the high cost and relatively low performance of walnut shakers, tall and large-trunk trees and irregular and rolling gardens make it difficult to widely employ mechanized harvesting technologies and machinery. One of the precision agricultural tools is crop yield mapping for local management and higher crop production. Product performance increases with local management based on understanding the causes of changes in different environments. Hence, to evaluate performance and build a harvesting machine, a DIP algorithm is proposed to detect and locate walnuts on trees in a natural light condition. The proposed algorithm functions based on image texture characteristics, light and shade density, and edge detection. The use of texture characteristics in combination with the light and shade density as an innovative method has worked well in conditions where fruits and leaves have a common color spectrum. In this method, fruit detection was independent of its shape to minimize the effect of fruits appearance obstruction on the algorithm's performance. The results indicated that the proposed method was able to detect walnuts in images taken from trees with a detection rate of ??.?%. The fruits that were next to each other were also distinguished at ??% resolution. Walnut detection-inhibiting factors were image darkness-induced errors with ?.?%, low resolution with ?.?%, improper scale with ?.?%, distinguish the tandem walnuts with ?.?%, and walnut appearance obstruction with ?.?%