سید مهدی نصیری

Drying shrimp is one of the storage methods that, while increasing the shelf life, leads to the production of a versatile product with various uses, from consumption as snacks to use as one of the main components of foods. Drying is preferred over other preservation methods because it offers numerous advantages, including extended shelf life, enhanced microbial stability, convenient consumption, reduced transportation costs, increased value, and product diversity.To accurately model these processes and thus obtain information on factors such as shelf life and energy consumption, it is necessary to determine the product’s initial and final temperatures, its geometry and dimensions, and its thermo-physical characteristics. Simulation of different drying processes requires accurate estimation of the effective moisture diffusion coefficient, which is highly dependent on temperature and humidity. Its dependence can be shown by an equation with an Arrhenius structure as an empirical function of humidity and temperature, or by considering the activation energy.It is necessary to have sufficient knowledge about heat and mass transfer characteristics, such as diffusion or penetration coefficient and the heat transfer coefficient to estimate the final temperature and drying time. This study investigated the drying process of peeled farmed shrimp (Litopenaeus vannamei) using a convective hot air dryer. Various parameters such as shrinkage and the effective moisture diffusion coefficient were examined.

A drying device was built to conduct experimental studies on drying shrimp samples. The experiments were conducted on sliced shrimp meat samples at temperatures of 40, 50, and 60 degrees Celsius, with a constant air velocity of 1.5 m/s. The experimental drying models were based on diffusion theory. In these models, it is assumed that the resistance to moisture diffusion occurs from the outer layer of the food. In most cases, Fick's second law was used to describe the phenomenon of moisture penetration.The study used the standard method of immersion in toluene to measure volume changes in the samples. During the drying process, the volume of the samples was measured at 45-minute intervals, and their volume changes were calculated. To measure the moisture content of the samples, each test started by recording the initial weight of the samples using a digital scale with an accuracy of ±0.001 g. During the drying process, the samples were weighed each time their volume was measured.Shrinkage during the drying process is commonly modeled by finding a relationship between shrinkage and moisture, using linear and non-linear models. In most cases, effective permeability is defined as a function of humidity and temperature. For this purpose, curve-fitting methods were employed to analyze the data collected from experimental tests. The appropriate function was extracted by incorporating the Arrhenius equation, which is applicable to most food items.

Based on the results of statistical indices, the linear model was the best model for depicting the relationship between shrinkage changes versus moisture ratio changes among the various experimental models evaluated for shrinkage and drying kinetics. Similarly, the Weibull distribution demonstrated superior performance in expressing variations in moisture ratio over time. A moisture dependent experimental model was used to express the variations in the apparent density of shrimp, resulting in a computed range of 1017-1117 kg m-3. Furthermore, an Arrhenius equation was derived to express the effect of moisture content and temperature on the effective diffusion coefficient of shrimp. According to the results, the effective diffusion coefficient of shrimp exhibited variations ranging from 0.08 ×10-9 m2 s-1 to 7.39×10-9 m2 s-1. When deriving the effective diffusion coefficient, the impact of the number of terms in Fick's second law on the variation of the moisture ratio was studied. The findings revealed that increasing the number of terms beyond 100 did not significantly affect the model’s outputs.

The linear model had the highest coefficient of determination (R2) among the evaluated shrinkage models, as well as the lowest root mean square error and sum of square error (SSE). This makes it the most optimal model for interpreting shrinkage at the tested temperature levels. The Weibull distribution experimental model proved to be the most suitable for expressing changes in the moisture ratio of shrimp meat slices over time within the evaluated temperature range. The Arrhenius model accurately predicts changes in the effective diffusion coefficient of shrimp slices with respect to temperature and moisture content within the tested temperature range.

The improper use of chemical fertilizers in crop production can result in unsafe food sources for consumers. This research focuses on estimating the accumulation of nitrate in tomatoes by analyzing images of tomato tissues. The experiments were conducted using a completely randomized design with four nitrogen levels: 400, 800, 1200, and 1600 kg.ha-1. Fifty samples were randomly selected from each treatment to create images for feature processing and develop a prediction model. The samples were sliced to a consistent thickness, and their images were prepared. The nitrate contents of the same samples were then measured in the laboratory. Color features, including R, G, and B color components, as well as non-color features such as white pixel area (WPA), total slice surface area (TSA), and the ratio of white pixel area to total slice surface area (WPA/TSA), were extracted from the images. The results showed that the nitrate content of the samples increased significantly (P<0.05) in response to the applied nitrogen fertilizer, with measurements of 1.6%, 2.7%, 2.8%, and 3.3%, respectively. Moreover, a strong correlation was found between the color components, WPA, TSA, WPA/TSA, and nitrate accumulation in the samples. Multiple regression and multilayer perceptron neural network (MLP) models were employed to predict the nitrate content. The best subset method was used to build an appropriate regression model. Various topologies and transform functions were applied to identify the best MLP model. The results indicated that an MLP model with a 3-15-1 topology and the lowest mean relative percentage error (MRPE) was the most accurate neural network model. The final regression and neural network models were validated using 60 intact samples. The neural network model achieved a MRPE of approximately 3.5%, demonstrating its precise estimation of nitrate contents compared to the regression model with an MRPE of around 5.2%.

The knowledge-based companies are formed by a group of educated entrepreneurs to use and commercialize innovative ideas. Technical and advanced knowledge would open the way for the entry and competition of national productions into the global markets and have a significant effect on domestic self-sufficiency and foreign trade, in this regard the knowledge-based companies in the science and technology parks are being supported by specialized incubators for growth, development and maturity. The main purpose of this study was to identify and analyze the challenges of the companies located in the Science and Technology Park from the point of view of the park managers and companies. In this qualitative research, the case study method was used.  Purposeful sampling and snowball techniques were used to select the key informants of in-depth interviews. The data was gathered through semi-structured interviews with a special protocol. Required data was gathered through interviews with 17 managers of Science and Technology Park and the CEOs of knowledge-based companies located in Fars Science and Technology Park. The data analysis was accomplished by MAXQDA2020 software. According to the findings, the companies are facing two types of challenges. The first category is the challenges related to the processes and internal executive affairs of the park during its acceptance and establishment in the park. The second one is the external challenges, which include administrative bureaucracy, exemptions, and the instability of the business environment. These challenges cause a lot of problems for newly founded and young companies because they do not have access to enough resources to be resilient in difficult situations. Based on the results, the acceptance process of companies needs to be changed. The admission time should be shortened so that the entrepreneurs do not give up on admission. It is required to pay special attention to companies and their type of activities in terms of providing better and more related services and facilities. The dedicated space should also be in accordance with the basic needs of these companies. It is more effective that the method of paying the loans should be in accordance with a purposeful schedule and appropriate to the companies` activities. Also, more practical training is provided to companies through people who have great experience in the entrepreneurship area. On the other hand, it needs to have a stronger network system for companies so they can collaborate and learn from each other, attract more customers, and succeed in the market

A country's economic success is often linked to its capacity to produce entrepreneurs capable of changing and advancing the economic fabric. The phenomenon of hybrid entrepreneurship is considered a strategic lever for economic development that is becoming increasingly prevalent in organizations. The existence of hybrid entrepreneurs reflects changes in market conditions that show that individuals choose to use their experience to develop businesses outside the organization. Based on this, this research was conducted to identify and analyze the factors affecting the acceptance of hybrid entrepreneurship among members of government organizations in Fars Province. This research is qualitative and is a multiple case study. The data was collected through semi-structured interviews with experts and key informants in the field of hybrid entrepreneurship in the university and various organizations in the province who are recognized as experts and were selected using a purposeful snowball sampling method. The data collection process continued until theoretical saturation was reached, achieved with the fifteenth interview. MAXQDA2020 software was used for qualitative content analysis and coding of the interview data. According to the research results, personal characteristics, economic characteristics, family roles, organizational characteristics, networking, and the link between new scientific and industrial technologies were among the factors affecting the acceptance of hybrid entrepreneurship from the perspective of experts.The components of the link between new scientific and industrial technologies, time management, economic development, work experience, and job performance improvement were, in order, the most important from the respondents' point of view. According to the research results, having two roles or becoming a hybrid entrepreneur has a better financial situation for employees. This research provides practical suggestions for policymakers and planners.

Fish fillet is a corruption-sensitive product that requires several quality and hygiene reviews before consumption. Many methods of fish quality and freshness determined based on destructive and time-consuming tests require manpower and sample sizes. This study evaluated the ability of a non-destructive fusion method of ultrasound, electrical resistance measurement, and colorimetry to monitor the freshness of rainbow trout fillets at refrigerator temperature. For this purpose, the samples of fish fillets were prepared and stored in a refrigerator for 12 days, and physical tests such as ultrasound wave attenuation, electrical resistance, and colorimetry to determine the color component of the fillets in both color space of RGB and Lab, and sensory evaluation (color, taste, odor, and texture) were performed. Also, a chemical test was conducted to determine the fillets ash, protein, and fat during storage. Extracted data from physical tests were subjected to two classification models of neural network and decision tree for determining the best classification method according to the fillet freshness. Various single hidden layer neural networks with zero to ten neurons and decision trees with five algorithms of LMT, REP, J48, RANDOM FOREST, and RANDOM TREE were evaluated. In each model, physical test data were considered as input and storage days as output. Based on the results, the highest classification accuracy (100%) and the lowest mean error value (0.02) were obtained for the neural network with 6 neurons in the hidden layer, and correspondingly 100 percent and nearly zero for the decision tree with J48 algorithm The confusion table of both models also confirmed the classifiers' accuracy. The integrated non-destructive system presented in this study does not require sample preparation. It is accurate, relatively inexpensive, and can be used during the food and fishery process to determine their freshness.

In this research, the ability of the integrated non-destructive system including ultrasound, electrical resistance measurements and colorimetric assays to predict the textural properties (hardness, brittleness, viscosity, elasticity, guminess and chewiness) of trout fillets were evaluated. For this purpose, fillets were stored for 12 days at refrigerator temperature and then examined at interval times. At the same time physical, mechanical, chemical and sensory tests were performed on the fillets. The performance of neural network and support vector machine methods for predicting and modeling tissue properties were compared. In each model, physical properties were considered as inputs and textural properties as outputs and modeling was performed. The results showed that in the prediction of hardness, fragility and elasticity of the support vector machine and in the viscous, resin and chewability indices, the neural network method had more capability to model the texture properties, so that the root mean square error (RMSE) Hardness, fragility, viscosity, elasticity, gum and chewability indices were equal to 0.144, 0.025, 0.015, 0.015, 0.044 and 0.171 respectively and their correlation coefficients were equal 0.993, 0.985, 0.992, 0.961, 0.995 and 0.995. Therefore, the proposed non-destructive system in combination with artificial intelligence methods showed promises as a non-destructive and efficient tool for monitoring and quality control during trout fillet storage.

Oilseeds after cereals ranked at second place as human’s food sources. Among them, sunflower is one of the main oilseed for cultivation. Because of increasing demand rate for edible oil import, it is necessary to avoid any grain loss during harvesting and processing of oilseeds. In some sunflower fields, mechanical harvesting is hardly done by combines due to difficulty for movement as well as the small-cultivated area. Then, manual harvesting and processing of sunflower heads and grains are appropriate and most used field operations. Detachments of grains from heads is a tedious work and labor-intensive operation. Also, form medical and ergonomic point of views it makes physical problem for workers. All commercial equipment thrash field dried sunflower heads, entirely. Therefore, some wastes are produced at preharvest (in field), threshing and separation processes. Some research have been conducted to adjust combine to decrease grain losses. Cylinder rotational speed and type, distance between cylinder and concave, heads moisture content and threshing unit feed rate were such adjustment. However, none of the detached grains from head without head threshing. On the other hand, losses to about ‎‏46% during harvesting the ‎sunflowers with the combine, a partially mechanized approach for grain detachment was chosen in this study. ‎

To do this an existing sunflower seed detacher unit (Jahani, 2014) was equipped with cleaning unit. In addition, the unit efficiency was improved by optimizing the device settings. This device consisted of a feeding conveyor belt, two series detacher cylinder with 8 cm rubber fingers covered all through their peripherals in segregated patterns, cleaning unit, two electric motor and deriving mechanism (belt and pulley). Cleaning unit comprised an axial flow fan and an inclined guide canal for grain movement. Fan was selected in such a way that separate the gross waste materials mixed with grain, detached from heads by laboratory test using a terminal velocity test rig. Slope and material of canal was determined by laboratory trials using a test rig to measure sliding coefficient of grain batches. For evaluating the unit performance, laboratory experiment was carried out at four levels of rotational speed of the detacher cylinders (300, 450, 510 and 600 rpm) and four levels of grain moisture content (7, 15, 20 and 27% db) in three replications. Cylinder rotational speed and feeding belt speed were selected based on Janani’s research results. Feeding belt speeds were adjusted in such a way that the speed ration of linear velocity of cylinders to feeding belt were constant and about 72.2 as reported by Jahani (2014). Moisture content levels were considered based on harvesting, processing and storage conditions of sunflower grains. Raw data were used to compute percentage of seed detachment, percentage of separation and grain fracture percentage.

ANOVA revealed that main factors of cylinders rotational speed and moisture contents and their interaction significantly affected percentage of separation (p < 0.01). Increasing in moisture content reduced the amount of detached grains from head due to flexibility of grains at higher moisture content and damping impact of rubber fingers. In addition, it was reported that lower moisture content produced lower adhesive force between grain outer surface and the pod. On the hand, increasing in rotational speed increased the amount of detached seed because of impact of rubber fingers and stored kinetic energy in shelled grains. The most detaching was obtained at 600 rpm and 5% moisture content. The rotational speed and grain moisture content and their interaction significantly (p < 0.01) affected percentage of fractured grains. Moisture content had more share for grain failure than rotational speed. Higher fracture was observed at lower moisture contents and higher cylinder speeds, so that it was around 6.4% at 600 rpm and 7% moisture. According to the results, cleaning was affected by moisture content alone and higher percentage took place at lower moisture contents because of lighter produced wastes. Overall, considering all above aforementioned indices, cylinder rotational speed of 600 rpm and grain moisture content of 20 % was the appropriate adjustment set for grain detaching. In such adjustment, detachment rate was as high as 94.6 %, grain fracture was limited to 0.27 %, cleaning was 68 % and the output capacity of the machine of 268 kgh-1 was achieved. More adjustment on fan speed is required to increase the rate of cleaning to approach hundred percent of cleanness.

Separation and weighing of broken of rice kernels within a batch is a common way for measuring the amount of brokens. Separation is carried out by sieve or hand that is time-consuming method and required periodical sampling. In the present study, some image texture features of a rice batch for determining the percentage of brokens were applied. In order to this, twenty-one samples of 100 g were prepared with different breakage percentages from mixture of broken and whole rice kernels (containing zero to 100 percent broken). Ten images were then taken from each sample batch. After pre-processing of the images such as noise removing and creating binary images, the texture features of the images were extracted using MATLAB software. Twenty-three texture features were extracted using a grayscale co-occurrence matrix method. The principal components analysis (PCA) method was employed to reduce number of the features. By which, 23 calculated and measured texture features was reduced to three principal components (latent variables). Then, the relationship between percentage of broken rice in the sample and the principle components of 23 extracted texture features from the images by artificial neural network method was pinpointed. Four different topology of neural networks with different neurons in two hidden layers were employed. The results of neural networks outputs revealed that 3-6-8-1 topology could determine the percentage of broken in a batch of rice kernels with 0.958 coefficient of determination (R2) and total estimation error of 1.5 percent. Moreover, it was found that 10 texture features were related to first principal component, four features related to second component, four related to third component, and five fractures were simultaneously related first and second principal components. Eight features with correlation more than 0.9 with the first and third principal component had the most impact on estimation of percentage of broken. This method of measurement is fast, affordable and has proper precision with respect to the common (hand) method for online applications.

Frostbite is one of the most important reasons for crop loss in Iran. Radiation frost due to inversion condition causes temperature drop below critical damage point which leads to extracellular ice formation inside the plant tissues. The purpose of this study was to identify the most important factors affecting radiation freezing. A region near the city of Shiraz with a chance of happening frostbite, according to previous weather reports, was selected. The temperature and wind speed were measured at the field level in the first and second half of the year, in nights expecting frostbite could be occurred. Then, the CFD simulation of heat transfer and thermal distribution was carried out using the FLUENT software, based on the time and the height of the ground surface under unstable conditions during frostbite. The numerical model was studied based on the data which obtained from the field stand. The results showed a good correlation between empirical and analytical data, so that the estimation by the root means square error method, 0.99, was obtained for experimental and simulation temperature values. Also, the results illustrated the wind blowing had a significant effect on preventing a sudden drop in temperature and frosting of products take place.

In this study pressure drop of bin containing corn as granular porous material with two common inlet duct arrangements (H, F) for a laboratory silage was studied by using experimental method. Higher pressure was obtained for the F arrangement compare to the H arrangement. The Ergun equation was fitted on the pressure drop of empirical data and the correction coefficients of modified Ergun equation for both H and F arrangements were determined. The coefficient of determination, mean root square error and mean relative error were used as indices to investigate the goodness of fitness between the empirical data and Ergun equation, and the equation had good fitness on the empirical data. The numerical simulation was carried out by finite element simulation of COMSOL Multiphysics v5.3 software using the modified Ergun equation. Moreover, distribution of velocity and flow lines within the bin was also presented.

The effect of using microwaves instead of steaming in parboiling step on mechnial properties of two paddy variety such as breakage strength and healthy rice yield were studied. Paddy samples were soaked in hot water at 50, 60 and 70°C for two hours and then exposed to microwave radiation for 15, 30, 45, 60, 75 and 90 seconds at the output power of 800 Watts. Sample were divided into two categories, one steamed at atmospheric pressure for 15 minutes and another was not. Finally, all the samples were dried at 35 °C. An increasing trend for breakage strength and healthy rice yield (for both paddy varieties) was observed when microwave exposure duration was increased. This increase was significantly different in treatments with microwave duration greater than 30 s as compared to control. The highest kernel bending strength and healthy rice yield in 90 s microwave treatment and soaking temperature of 70 ° C for Fajr and Lanjan varieties  were 24.7 MPa and 70.7%, and 20.8 MPa and 75.5%, respectively. There was no significant difference between breakage strength and healthy rice yield of steamed samples for 15 minutes and non-steamed microwave treatment for both varieties. According to these results, the impact of microwave on parboiling process was acceptable and can be replaced by steaming.

A practical model including a parameter as a function of drying condition were introduced to predict the effect of far-infrared radiation (FIR) in a combined rough rice drying process. Tests were conducted in three replication for four levels of FIR (0, 0.1, 0.2 and 0.3 Wcm-2) and three levels of inlet air temperature (30, 40 and 500C). The results obtained from the simulation of the proposed model demonstrated that the models were capable satisfactorily express the characteristics of drying rough rice in combined dryer. Also due to the effects of combination of hot air and FIR drying technique on drying rates, cracking enhancement ratio (CER), required breaking energy for grain, specific energy consumption (SEC) and optimal method was presented. Combination of hot air at temperature of 40 0C and FIR at intensity of 1000 W.m-2 is the best way to have lowest SEC and highest grain quality point of view.

In the present study, classification of tomatoes by fuzzy logic and change in classification due to storage conditions were studied. In order to conduct the study, qualitative properties such as color, size and hardness of tomatoes were measured. Tomato samples were sorted by a deigned fuzzy logic system based on fuzzy rules, and compared with the experts. Sensitivity analysis of outputs of membership functions were tested by chi-square statistic. In cool room storage, sensitivity of membership functions started from six-day after storage with change from grade 1 to grade 2. For samples that kept at ambient temperature sensitivity started on third day after storage with the same change in sorted groups as previous tests. The results emphasized that any processing operation or marketing after initial sorting of tomatoes should spend time in a way that does not change the results of the initial classification.

In this study, surimi chips and fish chips from Sarm (Scomberiodes commersonnianus) and potato chips were prepared. Some quality attributes of fish chips and surimi chips were investigated in comparison to potato chips during 2 months of storage at 25C. After production, compositional analysis (protein, fat and ash contents) were performed on the samples. Other properties such as moisture content, textural hardness, rancidity parameters such as peroxide value and free fatty acid (acid value) and also sensory properties were determined for the products during storage at 25C. Results showed the protein content of surimi and fish chips were higher than potato chips and consequently had better nutritional value. Textural hardness, moisture content, peroxide value and acid value significantly changed in the samples throughout storage. Potato chips had better sensory quality attributes compared to fish chips and surimi chips. The products absorbed moisture during storage, thus a decrease in crispiness was observed. The peroxide and acid values of products were increased during storage. Overall, potato chips and surimi chips had much higher quality attributes and less quality deteriorations compared to fish chips during storage.

In this research, the ability of a machine vision system was evaluated to monitor the oxidation period of olive oil. For this purpose, the olive oil samples studied during accelerated storage for 24 days in an oven and then image processing processes was carried out to extract parameters related to color spaces RGB, HSV and L*a*b*. The performance of artificial neural network and decision tree techniques compared to determine the olive oil oxidation rate. In each of the models color parameters were used as inputs and different stages in the olive oil oxidation period were considered as output. According to the results, the highest classification accuracy (94.44%) and the lowest RMSE (0.0696) are related to the decision tree technique. The proposed machine vision system combined with artificial intelligence techniques as non-destructive and efficient tool will offer for monitoring and quality control during oil storage.

Iran is a frontier of pomegranate fruit production in the world (with almost 40 % of the world`s production). However due to traditional processing operations is not ranked as the largest pomegranate exporter. Saveh, Neyriz and Ferdows are the top pomegranate producing cities in Iran. Pomegranate is consumed as a fresh fruit as well as processed product as food additive, paste, syrup, jelly, pectin, jam, beverage, essence, vinegar and concentrate. Aril extraction is the first and essential postharvest operation for pomegranate processing. Arils are mostly extracted manually even in large scales for fresh and processed consumption. This labor intensive operation is rational when aril quality is an important index for consumer. But whenever pomegranate juice is desired, the aril quality has no priority for consumer, and therefore arils can be extracted with less care. Sarig (1985) was the first inventor of a pomegranate aril extractor who employed air jet force to extract the arils. Later, other researchers employed the same method as well as water jet to extract fruit juice and sac. In the present study, fabrication and evaluation of vibratory aril extractor augmented with air system was conducted.
The study was conducted using Rabab cultivar samples which were manually harvested from an orchard in Neyriz town, Fars province. Samples were kept in refrigerator at 5 0C till experimental trials. Initial moisture content of fruit skin, arils and internal fleshes were measured by gravimetric method as 31.7±2.6 %, 61.5±1.8 % and 42.8±1.4 %, respectively and for a whole fruit was measured 45.3±11.5 % (w.b.). For conducting laboratory tests, an aril extraction unit was designed and fabricated. It comprised a steel main frame, a 746 W electric motor, drive mechanism (eccentric and shaft), sample retentive unit, air jet unit, aril tank, and an air compressor-tank assembly. Sample retentive unit was designed in such a manner to hold a halved fruit. This unit was made from four elements, a hemisphere bowel, four pressure (spring) arms to apply force on skin of the sample, and four tension (spring) arms for fixing the sample in the bowel by applying pressure on the edges of the halved sample. Such configuration helped sample to open more and more while extracting the arils to expose trapped aril for easier extraction. Sample retentive assembly was vibrated by the electric motor and drive mechanism. Electric motor was equipped with an electric convertor to create different levels of vibration frequency. Also, the drive mechanism was designed in such a manner to create different levels of vibration amplitudes. According to the previous studies, 2 nozzles with 3.5 mm diameter were selected for air jet unit. Nozzles were spaced at 8 cm apart according to the measured mean diameter of samples. Outlet air jet from nozzles covered the cross sectional area of the halved fruit. Nozzles assembly was rotated 180 degrees clockwise and counterclockwise with an electronically controlled stepper motor. Pressurized air (from air tank) was transferred to nozzles assembly by flexible pipes. Air pressure was controlled at 500 kPa level by air regulator. To conduct experimental trials, samples halved at three different cutting directions (horizontal (equatorial), vertical and oblique) by a sharp cutter and halved samples were used for tests. Halved sample was fixed in bowel and then the unit was excited by the electric motor. The assembly was vibrated for 60 seconds before blowing the air jet for extra 30 seconds. Tests for air jet alone were conducted for 90 seconds and percentage of detached and damaged arils were calculated. Damaged aril during cutting process was subtracted from total damaged arils for each trial. Collected data were analyzed according to factorial experiments based on completely randomized design, and means were compared by Duncan post-hoc test. Data of combined and air jet alone systems were analyzed by two independent sample t tests.
ANOVA results revealed that cutting type, frequency and amplitude, significantly influenced the percentage of aril extraction at 5 % level of significance. The highest amount of extraction was obtained at 30 Hz frequency and 4 mm amplitude for diagonal cutting by 87 %. At this condition, 13.9 % of arils were damaged by air jet pressure. A significant difference in percentage of extracted and damaged arils was observed between vibratory-air and air systems at 5 % level of significance. The highest amount of aril extraction as well as damage was observed for vibratory-air system with the means of 80.1 % and 9.9 %, respectively.
Maximum percentages of extraction and aril damage were achieved by applying the combined system with as compared to air jet system alone, so that combined system increased aril extraction by 7.1 % with 2.2 % extra damages.

Sugar beet (Beta vulgaris L.) as the second most important world’s sugar source after sugarcane is one of the major industrial crops. The presence of weeds in sugar beet fields, especially at early growth stages, results in a substantial decrease in the crop yield. It is very important to efficiently eliminate weeds at early growing stages. The first step of precision weed control is accurate detection of weeds location in the field. This operation can be performed by machine vision techniques.
Hough transform is one of the shape feature extraction methods for object tracking in image processing which is basically used to identify lines or other geometrical shapes in an image. Generalized Hough transform (GHT) is a modified version of the Hough transform used not only for geometrical forms, but also for detecting any arbitrary shape. This method is based on a pattern matching principle that uses a set of vectors of feature points (usually object edge points) to a reference point to construct a pattern. By comparing this pattern with a set pattern, the desired shape is detected. The aim of this study was to identify the sugar beet plant from some common weeds in a field using the GHT.
Images required for this study were taken at the four-leaf stage of sugar beet as the beginning of the critical period of weed control. A shelter was used to avoid direct sunlight and prevent leaf shadows on each other. The obtained images were then introduced to the Image Processing Toolbox of MATLAB programming software for further processing.
Green and Red color components were extracted from primary RGB images. In the first step, binary images were obtained by applying the optimal threshold on the G-R images.
A comprehensive study of several sugar beet images revealed that there is a unique feature in sugar beet leaves which makes them differentiable from the weeds. The feature observed in all sugar beet plants at the four-leaf stage was a stretched S-shaped curve at the junction of the leaf and petiole. This unique shape characteristic was used as the pattern for sugar beet detection using GHT. To implement the Hough transform in the images, a 50-member group of samples was prepared from S-shaped curve to build appropriate patterns. Desired features for the Hough transformation were extracted from the patterns. In the next step, the attempts were made to find the images for the shapes similar to each of the patterns.
Plants were thoroughly separated from soil and residues. The accuracy of segmentation algorithm was achieved by almost 100%.
The accuracy of the generalized Hough algorithm was evaluated in two stages. In the first stage, the algorithm accuracy was assessed in detecting patterns in the images. Results showed that the accuracy of the algorithm was 96.21%. In the second stage, the algorithm was evaluated for some other test images, whereas the algorithm achieved an overall accuracy of 91.65%. In some cases, the presence of a large overlap between objects in the image reduced the detection accuracy. This was because of two main reasons; 1) high interference and ambiguity in the object edges, so that Hough transform was not able to detect the predefined patterns in the objects and, 2) weeds highly overlapped with sugar beet plants and thereby they were wrongly detected as sugar beet. However, since there is no or little interference between plants at the four-leaf stage, this interference can be eliminated by morphological operations. Due to this fact, it can be said that the results of GHT algorithm are acceptable for the detection of sugar beet in the plants close to four-leaf stage.
A special feature in the shape of sugar beet leaves was used as a criterion to distinguish between sugar beet and weeds. The results showed that by quantifying this special feature, which is an S-shaped curve near the petioles connection of beet leaves, sugar beet can be discriminated from weeds with an accuracy of 91.65 %. Recalled that this feature is a shape characteristic, therefore, the generalized Hough algorithm must be applied prior to plant canopy development, which is consistent with the critical period of weed control in sugar beet fields.

Firmness is a very important quality property for kiwifruit. This property is affected by storage temperature and duration. Recently, some destructive and non-destructive methods were developed and used for fruit firmness assessment. However, non-destructive methods have merits of real-time and on-line applications as well as preventing fruit destruction. The objective of the present study was to evaluation of non-destructive acoustic technique for assessing the kiwifruit firmness at two different temperature levels during a storage period. On this basis, firmness of kiwifruit during two weeks of storage by non-destructive method was monitored. For determining the correlation between resonant frequency, stiffness and elasticity coefficients, and fruit firmness, destructive puncture test (Magness-Taylor) was also applied. The maximum penetration force in puncture test depicted a strong correlation with those from acoustic tests, resonant frequency, stiffness coefficient and elasticity modulus with the coefficients of determination of 0.838, 0.857 and 0.865, receptivity. According to the maximum correlation coefficient between modulus of elasticity and firmness, the relation was verified by new set of data, and revealed that it could estimate the fruits firmness with an error of 7.4 percent. Therefore, non-destructive acoustic test is an appropriate substitution method for estimation of kiwifruit firmness during shelf-life.

Given that fruit drying is one of the best economical storing methods, in the present study attempt has been made to apply a commercial drying method, namely passing warm air through fruit in a cabinet drier, to pinpoint the change in textural properties of pear fruit during drying at different inlet air temperature and velocity levels. The outcomes of the study can be used for optimizing the drier operation during drying (intelligent driers) to keep suitable textural properties of final product for those who use sweet dried fruit as sweetener due to diabetic issues.
Pear fruits, Shah Miveh cultivar, were collected form a garden near Isfahan city and carried to the laboratory carefully. Experimental samples with 2×2.5×2.5 cubic centimeter dimension were prepared from upper part of pear fruits. A conventional cabinet drier was used for drying the pear cubes with adjustable inlet drying air temperature and velocity. Air temperature and velocity were adjusted at three levels of 40, 50 and 60 oC, and 0.5, 1 and 1.5 m/s, respectively. Initial moisture content was measured by gravimetric methods and samples weight loss measured during drying by an online system comprising hooked type balance with ±0.001 accuracy. Dried samples were then subjected to texture profile analysis (TPA) with Instron (Santam-STM 20). Two-bite test was performed with 20 mm diameter aluminum probe, at 1 mm/s speed rate for 5 mm deformation. Textural properties of samples such as hardness, cohesiveness, adhesiveness, springiness and chewiness were drawn from force-time curve. Textural properties were measured at five sample moisture contents of 82, 66, 51, 35 and 20 percent (w.b.). Data were analyzed according to the factorial experiments based on completely randomized design by SPSS software (version 16), and the means were compared by Duncan multiple range test at 5 percent of significance.
Mean comparison of drying time significantly affected by air temperature and velocity, and the effect of temperature was more than air velocity. Results revealed that textural properties of samples have changed during drying process and these changes were related to drying conditions. Hardness decreased exponentially and adhesiveness decreased linearly as fruits were being dried, whereas springiness and cohesiveness increased linearly. Chewiness followed a parabolic trend, reached to the peak in the range of 40 to 50 % moisture content levels (wet basis). ANOVA showed that drying air temperature had significant influence on hardness, springiness and chewiness, while its influence on adhesiveness and cohesiveness was not significant. Moreover, it was found that air drying velocity affected all aforementioned properties. Having considered the results of dried pear chewiness, the minimum chewiness (0.46 J) took place at air drying temperature of 40 ˚С and velocity of 1m/s, and therefore it is recommended as the best drying condition. Drying time period at this condition was measured nearly 33.5 hours, which was approximately 24 hours more than the shortest one in the drying condition of 60˚С and 1.5 m/s and 6.5 hours lower than the longest one in drying condition of 40˚С and 0.5 m/s.

Grading science and grading equipment for many kinds of sea products are growing rapidly in developed communities and variety of grading equipment can be found in most of the large fishery units. Computer vision has the potential to be used as a precise method for recognition and assessment of apparent characteristics. In this study، machine vision technology was used to sort fish based on species، size and weight. Tiger-toothed croaker and Silver pomfret fishes were selected for this study. In the first stage، each sample fish was weighted and put in the illumination chamber and images were captured. Matlab environment was used for segmentation and image processing tasks. Linear and non linear regressions were used to estimate fish weight. Seven variables extracted from each image (length، height، area، perimeter، equal diameter، major axis length and minor axis length) in four models of mathematical approach (linear، logarithmic، binomial and exponential) were considered for developing each weight prediction equation. Results indicated that fish weight can be estimated with R2 values of 95. 4% and 94% for Tiger-toothed croaker and Silver pomfret، respectively. Model validation was investigated with new data. Results showed that there is not a significant difference between the actual and estimated weight at 5% significance level for all fish species in this study. It was also concluded that the system can accurately measure the length of the fishes using machine vision technology envisaged in this study. The algorithm was also able to sort two species of fishes including Tiger-toothed croaker and Silver pomfret with an accuracy of 100%.

In the present study variation of air relative humidity at the inlet and outlet of a cabinet dryer as well as drying kinetic of pear fruit in thin layer were studied. The experiments were conducted at three temperature levels of 40, 50 and 60oC and three air velocity levels of 0.5, 1 and 1.5 m/s. It was observed that the difference between input and output air relative humidity increased when drying temperature was increased. This difference followed declining trend at the same level of drying temperature when air velocity was increased. If drying at lower air temperature and higher velocity is desired, for optimum use of energy, a closed loop drying method is appropriate. Otherwise, increasing in air temperature and decreasing in air velocity is recommended. Eight mathematical models were fitted on drying data and the best one was selected according to coefficient of determination (R2) and Chi-square (χ2) statistics for 70 percent of data. The model then validated by statistics of root mean square of error (RMSE), mean bias of error (MBE) and mean relative error (ARE) for 30 percent of remaining data. The approximation of diffusion model with highest R2 (0.998), and lowest χ2 (0.0001), RMSE (0.01), MBE (0.0008) and ARE (5.2%) was found an appropriate model for estimating the kinetics of thin layer drying of pear cubes drying in a cabinet dryer.

Precision Agriculture is continuously trying to address the sources and factors affecting the in-field variability and taking appropriate managerial decisions. One of the popular research focuses in the recent three decades has been on the development of new approaches to making yield variability maps. Advancement in development of sensors and the importance of quality factor in high value crops has motivated scientists to investigate real-time and nondestructive testing methods. This study tried to introduce and evaluate a new approach to concurrent yield mapping and to estimate some nutritional qualitative factors of alfalfa production. In this study، yield quantity was determined by measurement of added hay slice in every hay compression cycle by employing a new star wheel and integrating its output with positioning data from Global Positioning System. To predict some nutritional quality properties، measurement of specific shear energy applied on the cutting blade and compressive energy on plunger head of a hay baler in field conditions were also evaluated. The results of statistical analysis of yield quantity measurement data showed a very good correlation between the suggested approach and yield mass (r=0. 96 and R2=0. 92). The results of using specific shear energy for estimation of crude fiber and cumulative index RFV with regard to field conditions were rated as acceptable. Using specific compression energy was suitable only for estimating the dry matter. None of the suggested methods was able to estimate the hay crude protein. Further investigations at more extensive variations of quality indices and alfalfa varieties are suggested.