vali rasooli sharabiani

In this research, field experiments were carried out in two types of soil (sandy clay loam and clay) to model the traction performance of the tractor considering drawbar power, rolling resistance, and traction efficiency, using deep learning and convolutional neural network while having some parameters such as soil type and conditions, tool parameters, and operation parameters. The tests were conducted within each soil texture in the form of factorial tests based on the randomized complete block design (RCBD) in triplicates. The tests were done in various moisture levels (8-17% for dry soils and 18-40% for moist soils), tractor forward speed (1.2, 1.6, 1.8, and 2.2 km h-1), working depth (30 and 50 cm), the number of pass (2 and 6 times), and tire inflation pressure (20 and 25 psi). The cone index, dynamic load, draft force, and moisture content were measured in each tests. The networks designed to model the drawbar power, rolling resistance, and traction efficiency were of convolutional neural network type. Various algorithms such as Sgdm, Adam, and Rmsprop were utilized to train the network. The results showed that the neural network developed by Sgdm algorithm outperformed the others. Therefore, this algorithm was utilized for the modeling process. Statistical criteria such as R2 and MSE were also employed to evaluate the performance of the network. For the drawbar power, the 8-499-499-1 architecture showed the best performance with R2=0.9953 and MSE=0.0016. Concerning the rolling resistance, the best performance was observed in 8-301-305-1 architecture with R2=0.9903 and MSE=0.0039. The best performance for the traction efficiency was obtained by 8-371-371-1 architecture with R2=0.9888 and MSE=0.003. The results showed that these networks can be used to model parameters by removing convolution layers and reducing dimensions.

Nectarine is a plant that is cultivated as an important commercial product in some countries and is known as an important source of sugar and vitamins in the human diet. Due to the increase in expectations for food products with high quality and safety standards, accurate, fast and targeted determination of the characteristics of food products is necessary. In the nectarine product, quality evaluation after the harvesting stage is necessary to provide a reliable and uniform product to the market. The purpose of this study is to identify and classify nectarines by extracting characteristics from the response patterns of the spectrometer and using chemometrics methods. A near-infrared spectrometer can detect the spectrum of reflected light by estimating its concentration or determining some of its inherent properties. The quality assessment of agricultural products includes two main methods, quality grading systems based on the external characteristics of agricultural products and quality grading systems based on internal quality assessment, which has gained outstanding points in recent years. In the meantime, several methods have been invented for the qualitative grading of agricultural products based on the assessment of their internal properties in a non-destructive manner, and only some of them have been able to meet the above conditions and have been justified in terms of technical and industrial aspects. To be meanwhile, spectrometry can be highly efficient in determining the quality of cultivars. Spectroscopy is a type of system that has a different structure and approach from other methods (image processing, neural network, etc.) and can perform classification and determination of digit quality. With increasing expectations for food products with high quality and safety standards, the need for accurate, fast and targeted determination of the characteristics of food products is now essential. Because manual methods do not have automatic control, they are very tiring, difficult and expensive, and they are easily affected by environmental factors. Today, spectroscopic systems are non-destructive and cost-effective and are ideally used for routine inspections and quality assurance in the food industry and related products. This technology allows inspection works to be carried out using wavelength data analysis techniques and is a non-destructive method for measuring quality parameters. In this research, using spectrometry and chemometrics methods, the variety of nectarine fruit was identified.

For this study, 5 different nectarine cultivars were prepared from the gardens of Moghan city (Ardebil province) and were tested and data collected. A spectroradiometer model PS-100 (Apogee Instruments, INC., Logan, UT, USA) was used to acquire the spectrum of the samples. This spectroradiometer is very small, light, and portable, has a single-wavelength sputtering type with a resolution of 1 nm and a linear silicon CCD array detector with 2048 pixels that covers the spectral range of 250-1150 nm (Vis/NIR) well. Also, there is the ability to connect the optical fibre to the PS-100 spectroradiometer and transfer the data to the computer with the purpose of displaying and storing the acquired spectra in the Spectra Wiz software through the USB port. With the aim of creating optimal light in contrast mode measurements, an OPTC (Halogen Light Source) model halogen-tungsten light source, which can be connected to an optical fibre, was used. This light source has three output powers of 10, 20, and 30 watts, which were used in this research. Also, a two-branch optical fibre probe model (Apogee Instruments, INC., Logan, Utah, USA), which includes 7 parallel optical fibres with a diameter of 400 micrometres, was used in counter-mode measurements. After providing the necessary equipment, the optimal spectroscopic arrangement was designed and implemented in order to facilitate the experiments and minimize the effect of environmental factors during the spectroscopic process. The data obtained from spectral imaging may be affected by the scattering of light by the detector with sample change, sample size change, surface roughness in the sample, the noise created due to the increase in temperature of the device and many other factors, and unwanted information affect the accuracy of calibration models. Therefore, to achieve stable, accurate and reliable calibration models, data pre-processing is needed (Rossel, 2008). In this research, Savitzky-Golay smoothing, first and second derivatives, baseline, standard normal distribution, and incremental scatter correction were applied to the data. The use of non-destructive methods based on spectroscopy in the full range of wavelengths requires spending time and very high costs, which makes the practical application of this method almost impossible; therefore, one should look for a way to find the optimal wavelengths and limit the wavelengths to the minimum possible value. Chemometrics uses multivariate statistics to extract useful information from complex analytical data. The chemometrics used in this study started with principal component analysis (PCA) to explore the output response of the sensors and reduce the dimensionality of the data. In the next step, linear diagnostic analysis (LDA) was also used to classify 5 varieties of Shail. (PCA) is one of the most common statistical data reduction methods. This method is an unsupervised technique used to explore and reduce the dimensionality of a dataset. The analysis itself involves the determination of variable components, which are linear combinations of many investigated characteristics. In this research, in order to construct the LDA model, the data were randomly divided into two parts: 70% of the samples were used for training and cross-validation, and the rest of the data were used for independent validation.

Based on the results of the PCA analysis presented in Figure 2, the first principal component (PC-1) describes 72% and the second principal component (PC-2) 13% of the variance of the tested samples. As a result, the first two principal components together express 85% of the data. Considering that it is possible that the degree of correlation between the properties of different samples during the tests, due to various reasons such as technical problems of the equipment, data collection, incorrect sampling, etc., in some samples, inappropriate or socalled outliers The LDA method is a supervised method that is used to find the most distinct eigenvectors and maximizes the between-class and intra-class variance ratios and is capable of classifying two or more groups of samples. The LDA method was used to identify the nectarine cultivars based on the output response of the spectrometer. Unlike the PCA method, the LDA method can extract the resulting information to optimize the resolution between classes. Therefore, this method was used to detect 5 nectarine cultivars based on the output response of the spectrometer. The results of the identification of figures equal to 100% were obtained.

Potato is considered one of the most important food sources in the world (4th rank) and studying its various aspects is very important to ensure that the produced product has the necessary qualifications and can satisfy the customer. In the food industry, this product is transformed into various products such as baked potatoes, fried potatoes, potato chips, potato starch, dry fried potatoes, etc.The complexity of food odours makes it difficult to analyze them with conventional analytical techniques such as gas chromatography. However, expert sensory analysis is costly and requires trained people who can only work for a relatively short period. Problems such as the human subjectivity of the response to smell and the variation between people should also be considered. Hence, there is a need for a tool such as an electronic nose with high sensitivity and correlation with human sensory panel data for specific applications in food control. Due to its easy construction, cheapness and the need for little time for analysis, the electronic nose is becoming an automatic non-destructive method to describe the smell of food.An olfactory machine can recognize the fragrance composition by estimating its concentration or determining some of its intrinsic properties, which the human nose is hardly able to do. In general, the human olfactory system is a five-step process including smelling, receiving the scent, evaluating, detecting and erasing the effect of the scent. The olfactory phenomenon begins with inhaling the intended smell and ends with breathing fresh air to remove the effect of the scent. The human olfactory system, with all its unique capabilities, also has disadvantages that limit its use in quality control processes, including subjectivity, low reproducibility (for example, results depending on time, people's health, analysis before the presence of odour and fatigue is variable), time-consuming, high labour cost, adaptation of people (less sensitivity when exposed to odour for a long time). In addition, it cannot be used to evaluate dangerous odours.Meanwhile, the electronic nose can detect the volatile compounds of potatoes. The electronic nose has been used in extensive research to identify and classify food and agricultural products.
The purpose of this research was to evaluate the ability of the electronic nose using one of the chemometrics methods to detect 5 different potato cultivars.

First, 5 varieties of potato were prepared from the agricultural research centre of Ardabil city. These 5 varieties included Colombo, Milwa, Agria, Esprit and Sante.After preparing the cultivars, first, the samples were placed in a closed container (sample compartment) for 1 day to saturate the space of the container with the aroma and smell of potatoes, and then the sample compartments were used for data collection with the electronic nose.In this research, the electronic nose made in the Biosystems Engineering Department of Mohaghegh Ardabili University was used. In this device, 9 metal oxide semiconductor (MOS) sensors with low power consumption are used, which are listed in Table 1.The sample chamber was connected to the electronic nose device and data collection was done. This data collection was done in such a way that first, clean air was passed through the sensor chamber for 150 seconds to clean the sensors from the presence of odours and other gases. Then, the smell of the sample was sucked from the sample chamber by the pump for 150 seconds and directed to the sensors, and finally, clean air was injected into the sensor chamber for 150 seconds to prepare the device for repetition and subsequent tests. 15 repetitions were considered for each sample.Through the mentioned steps, the output voltage of the sensors was changed due to exposure to gases emitted from the sample (potato smell) and their olfactory responses were collected and recorded by data collection cards, the sensor signals were recorded and stored at 1-second intervals. . A fractional method was used to correct the baseline, in which noise or possible deviations were removed and the responses of the sensors were normalized and dimensionless.
By chemometrics method in this research, it started with principal component analysis (PCA) to discover the output response of the sensors and reduce the dimension of the data.Principal component analysis (PCA) is one of the simplest multivariatemethods and is known as an unsupervised technique for clustering data according to groups. It is usually used to reduce the dimensionality of the data and the best results are obtained when the data are positively or negatively correlated. Another advantage of PCA is that this technique reduces the volume of multidimensional data while removing redundant data without losing important information.

The scores chart (Figure 1) showed that the variance of the total data is equal to PC-1 (94%) and PC-2 (3%), respectively, and the first two principal components account for 97% of the variance of the total normalized data. When the total variance is higher than 90%, it means that the first two PCs are sufficient to explain the total variance of the data set. So it can be concluded that the electronic nose has a good response to the smell of potatoes and its cultivars can be distinguished, which shows the high accuracy of the electronic nose in identifying the smell of different products.With the correlation loadings plot, the relationships between all variables can be shown. The loading diagram (Figure 2) shows the relative role of sensors for each main component. The inner oval represents 50% and the outer oval represents 100% of the total variance of the data. The higher the loading coefficient of a sensor is, the greater the role of that sensor in identification and classification. Therefore, the sensors that are located on the outer circle have a greater role in data classification. According to the figure, it is clear that all the sensors have an important role in identifying the rice variety, including the role of sensors number 1 and 9, which are respectively the same sensors as MQ9 (to detect carbon dioxide and combustible gases) and MQ3 (to detect alcohol, methane, natural gases), it was less than the rest of the sensors, and by removing these two sensors, the cost of making an olfactory device (to distinguish genuine and fake rice) can be reduced and costs can be saved. In this research, an electronic nose with 9 metal oxide sensors was used to identify and distinguish potato cultivars. The Chemometrics PCA method was used for qualitative and quantitative analysis of complex data from the electronic sensor array. PCA was used to reduce the data and with two main components PC1 and PC2, it described 97% of the variance of the data set and provided an initial classification. The electronic nose has the ability to be used as a fast and non-destructive method to identify potato varieties. Using this method will be very useful for consumers, especially restaurants and processing units, in order to choose high-quality cultivars.

Potato with the scientific name (Solanum tuberosum. L.) is a plant that is cultivated as an important crop in all countries and is known as a source of carbohydrates, proteins, and vitamins in the human diet. This is a native product of South America and its origin is from Peru. In the food industry, this product is transformed into various products such as baked potatoes, fried potatoes, potato chips, potato starch, dry fried potatoes, etc.Due to the increase in expectations for food products with high quality and safety standards, accurate, fast and targeted determination of the characteristics of food products is necessary. In the apple-potato product, quality assessment after the harvest stage is necessary to provide a reliable and uniform product to the market, because potatoes, like many other products, have uneven quality and processing during the harvest stage. - Be At the same time, the safety and desirability of food play an important role in the food industry and are directly related to people's health. In addition, a huge part of potatoes used in the processing industry is stored, so considering the importance of this food item and the demand of the people throughout the year, it is possible to meet the needs of the applicants only through long-term storage with optimal conditions was responsible. Potatoes for the processing industry must have some requirements such as low sugar content, high dry matter and specific weight, high antioxidants, light skin colour and no sprouting.The complexity of the reflectance spectrum of food makes it difficult to analyze them with conventional analytical techniques such as gas chromatography. However, sensory analysis by experts is a costly process and requires trained people who can only work for a relatively short period. A near-infrared spectrometer can detect the spectrum of reflected light by estimating its concentration or determining some of its inherent properties.The quality assessment of agricultural products includes two main methods, quality grading systems based on the external characteristics of agricultural products and quality grading systems based on internal quality assessment, which has gained outstanding points in recent years. In the meantime, several methods have been invented so far for the qualitative grading of agricultural products based on the assessment of their internal properties in a non-destructive way, and only some of them havebeen able to meet the above conditions and have been justified in terms of technical and industrial aspects.Meanwhile, spectrometry can be highly efficient in determining the quality of cultivars. Spectroscopy is a type of system that has a different structure and approach from other methods (image processing, neural network, etc.) and can perform classification and determination of digit quality.With increasing expectations for food products with high quality and safety standards, the need for accurate, fast and targeted determination of the characteristics of food products is now necessary. Because manual methods do not have automatic control, they are very tiring, difficult and expensive, and they are easily affected by environmental factors. Today, spectroscopic systems are non-destructive and cost-effective and are ideally used for routine inspections and quality assurance in the food industry and related products. This technology allows inspection works to be carried out using wavelength data analysis techniques and is a non-destructive method for measuring quality parameters. In this research, using spectrometry and chemometrics methods, changes in acidity and SSC of potato were investigated over time.

In each treatment period (in total 5 periods were considered and the intervals of periods were determined as one week), unripe walnut samples in addition to ripe samples (in the last period) were taken from one of the orchards around Ardabil (located in Shahrivar village) was prepared, tested and data collected.A spectroradiometer model PS-100 (Apogee Instruments, INC., Logan, UT, USA) was used to acquire the spectrum of the samples. This spectroradiometer is very small, light, portable, has a single-wavelength sputtering type with a resolution of 1 nm and a linear silicon CCD array detector with 2048 pixels that covers the spectral range of 250-1150 nm (Vis/NIR) well. Also, there is the ability to connect the optical fibre to the PS-100 spectroradiometer and transfer the data to the computer to display and store the acquired spectra in the Spectra Wiz software through the USB port. To create optimal light in contrast mode measurements, an OPTC (Halogen Light Source) model halogen-tungsten light source, which can be connected to an optical fibre, was used. This light source has three output powers of 10, 20, and 30 watts, which were used in this research. Also, a two-branch optical fibre probe model (Apogee Instruments, INC., Logan, Utah, USA), which includes 7 parallel optical fibres with a diameter of 400 micrometres, was used in counter-mode measurements. After providing the necessary equipment, the optimal spectroscopic arrangement was designed and implemented to facilitate the experiments and minimize the effect of environmental factors during the spectroscopic process.To measure SSC, liquid refractometer model BPTR100 (Middle East System Control Company, brand name Prisma Tech, made in Iran) available at Mohaghegh Ardabili University is used. For this, first, some water is taken from the samples and after pouring it into the microtube, we allow it to reach the ambient temperature, and then it is placed on the refractometer and the amount of sugar is read in terms of Brix.For this purpose, a laboratory pH meter, which is also called a pH meter, was used. The pH meter is actually a potentiometer consisting of an ion-selective glass electrode that selectively responds to the activity of hydrogen ions in the solution and measures the potential difference between the external solution (sample) and the internal solution (reference electrode solution). The pH-sensitive part is made of a special thin glass membrane that is at the bottom of the electrode.

In this research, in order to estimate the amount of acidity and SSC of potato-potato and the amount of wavelength absorption in 5 different periods of storage (two-week periods), reflectance spectroscopy was performed in the wavelength range of 400 to 1100 nm. After removing the noises by PCA analysis, to improve the spectrum, different pre-processings were applied and their effects were investigated. The appropriate model was determined using the partial least squares (PLS) method. Important wavelengths were selected based on the regression coefficient of the best model. Based on PLS analysis, the best results were obtained with Savitzky-Golay smoothing preprocessing. As a result, it seems that the non-destructive method of ultraspectral imaging was able to detect the amount of SSC in potatoes, but no acceptable result was obtained in the case of acidity.

Maize (Zea mays. L) is one of the most important crops acrossthe world that ranks third in terms of acreage behind wheat and rice. As this crop can adapt to different climatic conditions, it is of great importance and has a large area under cultivation.Therefore, maize is one of the major products of temperate, warm-temperate, subtropical, and humid regions. After wheat, rice, and barley, this plant is the main crop in Iran with the largest cultivated area.There are different types of maizeseeds, so their classification is essential to ensure quality. A key component of sustainable agriculture is quality assurance. On the one hand, techniques such as drying, cooling, and edible coating must be used to maintain the quality of agricultural products. On the other hand, effective and efficient methods should be developed to evaluate and classify their quality, which is used in seed and seedling processing centers, silos, and mechanized warehouses.The detection of various varieties of crop seeds using instrumental methods has been the subject of extensive research. As a non-destructive and rapid inspection method for the recognition and classification of cereal seed varieties, the visual machine is available. Machine vision-based automated methods can have a positive impact on food processing. In other words, this tool is the process of preparing and analyzing images of a real scene using a computer to obtain information or control a process. The features of images can be extracted using this machine to recognize and identify the quality of different types of products. To identify the types of plants, their growth patterns, and the effects of the environment on them to obtain more and superior products, machine vision occupies a special place and is one of the most important research areas. Inspection and quality control of factory output products is an important application of machine vision.Advances in image processing technology have opened up a wide range of machine vision applications in agriculture. The development of powerful microcomputers and specialized software has led to the use of image processing for the inspection of fruits and agricultural products, especially for quality control and sorting. Many agricultural products sorting systems used to separate fruits or crops based on color, shape, size, the extent of damage, crushing, bursting, spotting, etc., now rely on visual machines and image processing functions.Images of products moving on the conveyor system are taken by a CCD camera, transmitted to a computer for processing, and in these systems, the necessary data are extracted from them. Depending on the information obtained, commands are then issued to activate or deactivate a mechanical part so that the product can be removed from or allowed to cross the main path. Sorting is a common practice in many industries. Compared to mechanical systems, machine vision technology offers the highest accuracy and quality at the lowest cost and with the lowest error rate, so it can be considered the most effective solution to this problem. The agricultural industry is one of the areas where sorting and grading systems based on machine vision are urgently needed.The core elements of machine vision are image processing and analysis used together with new methods and classifiers such as neural networks, backup vector machines, fuzzy logic, etc. to perform classifications and required measurements. This study aimed to identify seeds of three maize varieties using macroscopic imaging techniques, evaluate the morphological and chromatic features in maize grains, and discriminate varieties using a stepwise method and remove some variables using LDA and ANN.

Three seed varieties of single cross 703,single cross 704, and single cross 705 were provided by the Agricultural and Natural Resources Research Centre of Ardabil Province in Pars Abad Moghan. The seeds were then taken to the Biophysical Properties Laboratory of the Department of Biosystems and Mechanical Engineering, MohagheghArdabili University.Three samples (20 g) of each variety were stored in a laboratory oven at 105 °C for 24 h to determine the initial moisture content of maize grains. According to the dry weight of grains, the initial moisture content of them was calculated by 10.50%. To distinguish 3 maize varieties, 180 samples were analyzed as single seeds (30 replicates in the anterior direction and 30 replicates in the posterior direction) for each variety with 60 replicates. In addition, 9 more samples were used in bulk with 3 replicates for each variety.Thus, we imaged a total of 189 samples. In addition, a digital scale with an accuracy of 0.001 g was used to measure the weight of the grains. Computer vision systems consist of five main components: lighting chamber, camera, analogue-digital card (for digitization), computer, and computer software. Images were taken using a Canon IXY DIGITAL 510 IS digital camera. A dome-shaped chamber was used to reduce noise and control ambient light. The system was illuminated with four fluorescent lamps and two rows of LED lamps, one white and one yellow. While the camera was pointed perpendicular to the imaging surface, it provided images with a resolution of 12.1 megapixels.In this case, the images were processed using MATLAB software. First, 10 maize seeds were randomly sampled from the first variety (single cross 703) and weighed using a digital balance. Then, parameters such as the large and small diameters and thickness of each grain were measured using a caliper of 0.02 mm. Then, these grains were placed at appropriate distances from each other on a plate of red cardboard in the opposite direction to be imaged. Finally, 30 maizeseeds were imaged in both directions and 60 images were taken as single seed. In total, we obtained 180 images of all three varieties as single seeds. To prepare the mass, first, some seeds of the first variety were placed in a cylindrical container (1.5 cm high, 4.2 cm in basal diameter, and 70.62174 cm2 in volume) so that the container was filled. After weighing, the mass of grains with a certain volume was poured onto the red plate in a circular pattern. In the end, the camera was placed on the bulk sample and the image was taken, just like the single grain image.The same procedure was repeated twice more on two more bulk samples of the first variety. Similarly, three bulk samples of two more varieties were imaged. In this way, a total of nine images were obtained. After each imaging, we measured and recorded the dimensions and weight of 10 randomly selected seeds from the imaged bulk. In the end, 189 images were obtained, including 180 single-grain and 9 bulk images.In the single sample feature extraction step using the bwlabel function, all samples were labeled and the grain morphological features were extracted. Then, the set of Regionprop functions was used to determine eight parameters, including area, perimeter, major principal axis, minor principal axis, integrity, irregularity, convex area, and equivalent diameter. An artificial neural network (NAA) and a statistical linear discriminant analysis (LDA) method were used to identify maize varieties based on their morphological and color characteristics. The data were normalized before analysis. LDA is a statistical method for classifying objects based on independent variables. The analysis was carried out using SPSS software. The diagnostic analysis includes stepwise analysis, principal component analysis, and elimination of recursive features. In this study, the stepwise method was used. In the usual method, all variables are included in the analysis. However, in the stepwise method, some variables were removed and only the variables with the greatest influence on the model were included. To classify the maize varieties, a network consisting of three layers: input, output, and hidden layers was used.

We performed image processing to classify three maize varieties based on the results obtained. A linear diagnostic analysis method was used in this study. A total of 16 predictor variables were used with an accuracy of 70.6%. Some variables were eliminated by a stepwise method. In addition, eight other predictor variables were analyzed with the same accuracy of 70.6%. Thus, although the number of predictor variables was reduced, the detection accuracy remained constant. Moreover, the highest accuracy of diagnosis (80%) was associated with the first variety (single cross 703). Additionally, the accuracy of the methods of ANN with 16 and 8 predictor variables was 75.6% and 72.2%, respectively. These values were higher than that of LDA.Predictive variables included areas, perimeter, major principal diameters, minor principal diameters, irregularities, concave areas, equivalent diameters, color indices (red, green, and blue) resulting from maize grain sample processing, weight, and grain size. The following factors were the most important predictors of varietal discrimination: thickness, major principal axis, minor principal axis, irregularity, equivalent diameter, integrity, red color index, and green color index. According to the results, the length and width of individual grains had no significant effect on variety classification.Our finding demonstrated thatmachine vision technology can be used in seed and seedling processing centers, silos, mechanized warehouses, and other places where maize seed crops need to be identified and separated in a non-destructive manner.

Potato is an important vegetable that grows all over the world and is considered an important product in developing and developed countries for the human diet as a source of carbohydrates, proteins, and vitamins. This product is native to South America and its origin is from Peru, after wheat, rice and corn, it is the fourth product in the food basket of human societies. According to the statistics of the Food and Agriculture Organization of the United Nations, the area under cultivation of this crop in Iran in 2017 was 161 thousand hectares and the crop harvested from this area is about 5.1 million tons. Traditional methods of determining potato varieties were based more on morphological features, but with the production of new products, there was a need for methods that were faster and more recognizable. In the meantime, the high-performance artificial neural network can be used to classify cultivars. Artificial neural networks can classify and detect cultivars, are flexible, and are used in most agricultural products. Therefore, the olfactory machine can have high efficiency in classifying and distinguishing cultivar, originality and storage time. The olfactory machine is a system that has a different structure and approach from other methods (image processing, neural network, etc.), is flexible and is used in most agricultural products due to the presence of odour in them.With the rapid and rapid advancement of computer technology and sensor technology, the application of the bionic electronic nose, including a semiconductor gas-sensitive sensor and a pattern recognition system as a means of detection, offers a new method for rapid classification and digit recognition. Give. The electronic nose has also introduced a new method for classifying and detecting rough rice in a non-destructive and fast way.Due to a large number of potato varieties and sometimes the lack of familiarity of processing units with its cultivars and also time-consuming and inaccurate in identifying different potato cultivars by experts and farmers, and the importance of identifying potato cultivars and other agricultural products in At every stage of the food industry process, it is necessary to find ways to do this accurately and quickly enough. The aim of this study was to evaluate the ability and accuracy of the electronic nose with the help of an artificial neural network to detect and differentiate several potato cultivars.

First, potatoes in 3 different cultivars (Colombo, Milvana and Sante) were prepared from Ardabil Agricultural Research Center and kept at a temperature of 10-4 ° C. One day after the data were collected, data collection began with an olfactory machine. 3-4 potatoes from each cultivar were placed in the sample container for 1 day to saturate the sample container with the smell. Then the sample chamber was connected to the electronic nasal device and data collection was performed. The data were collected by the olfactory machine in such a way that first clean air was passed through the sensor chamber for 100 seconds to clean the sensors from other odours. The odour (gases emitted from the sample) was then pumped out of the sample chamber by the pump for 100 seconds and directed to the sensors. Finally, clean air was injected into the sensor chamber for 100 seconds to prepare it for further data collection. According to these steps, the output voltage of the sensors was changed due to exposure to various gases (potato odour) and their olfactory response was collected by data collection cards, sensor signals were recorded and stored in the USB gate of the computer at 1-second intervals. A fractional method was used to correct the baseline in which noise or possible deviations were eliminated and the sensor responses were normalized and dimensionless. In the next step, linear diagnostic analysis (LDA) and artificial neural networks (ANN) were used to classify the 3 potato cultivars. LDA is a supervised method used to find the most distinctive special vectors, maximizing the ratio of the variance between class and within the class, and being able to classify two or more groups of samples. ANN and pattern recognition were used to find similarities and differences in the classification of potato cultivars. For this, 1 neuron was considered for the input layer, the hidden layer with the optimal number of neurons will be considered and five output neurons with Depending on the number of output classes the target will be considered. In-network training, logarithmic sigmoid transfer function and Lunberg-Marquardt learning method were used. Also, the amount of error was calculated using the mean square error. For learning (70%), testing (15%) and validation (15%) all data were randomly selected. Training data was provided to the network during the training and the network was adjusted according to their error. Validation was used to measure network generalization and completion of training. Data testing had no effect on training and therefore provided an independent measurement of network performance during and after training.

LDA and ANN methods were used to detect potato cultivars based on sensor output response. The LDA method can extract multi-sensor information to optimize resolution between classes. Therefore, this method was used to detect 3 potato cultivars based on the output response of the sensors. Detection results of cultivars equal to 100% were obtained (Figure 1). Also, in the ANN method, 8 sensors were considered in the input layer of 8 neurons according to the output data. Also, 3 layers of neurons were considered for the output layer according to the type of cultivars. Therefore, the 3-6-8 topology had the highest accuracy for detecting potato cultivars, so the RMSE value was 0.008 and the R2 value was 99.8. There was also a very high correlation between predicted and measured data (Figure 2). In this study, a portable olfactory machine system with 8 metal oxide sensors was used to investigate the detection of potato cultivars. Chemometrics methods including LDA and ANN were used for qualitative and quantitative analysis of complex data using an electronic sensor array. LDA and ANN were able to accurately identify and classify different potato cultivars with 100% accuracy. The electronic nose has the potential to be used as a fast and non-destructive method to detect different potato cultivars. Using this method in identifying potato cultivars will be very useful for researchers to select and produce pure cultivars and for farmers to produce a uniform and certified crop.

Structural heterogeneity of fruits and vegetables makes it difficult to understand the associated physicochemical changes that occur during drying. Due to its heterogeneous structure, food is one of the most complex types of metamorphic materials. The porosity and hygroscopic nature of fruits and vegetables increase their shrinkage during the drying process, which is a physical process commonly observed during drying. Shrinkage has a significant effect on the mechanical and textural properties of fruits and vegetables. Most importantly, shrinkage is an important factor that has a great impact on drought rate and drought kinetics. Because of these factors, food researchers emphasize that shrinkage should not be ignored when predicting volume and heat transfer during drying. The shrinkage model is better suited to the experimental data during drying than the non-shrinkage model. Food shrinkage depends on several factors such as material properties, mechanical properties, and process status.Knowledge of porosity during drying can also help to accurately predict the transfer phenomenon and quality characteristics. Some researchers have used mathematical equations to predict the porosity of food as a function of moisture content, which can be classified into two categories: (1) theoretical models based on understanding of fundamental physics and the mechanisms involved in pore formation have been established, and (2) experimental models have been developed using parameters in experimental data. Many previous studies on experimental or laboratory shrinkage have predicted porosity as linear, quadratic, and exponential equations. On the other hand, theoretical modeling can provide a better understanding of the shrinkage that occurs simultaneously with heat and volume transfer during drying. However, limited efforts have been made in the theoretical modeling of the contraction of fruits and vegetables, due to the complexity of creating physics-based models. The first porosity model was introduced in the 1950s. Kilpatrick and colleagues proposed a simple model considering the volumetric contraction of fruits and vegetables during drying. Many models in previous research have considered shrinkage to be ideal, during which the reduction of the geometric volume of the product is exactly equal to that of water lost. But in fact, this linear relationship between the decrease in physical volume and the volume of water lost during the drying period is not observed. Cell loss and shrinkage of food tissues occur during the drying process of food. There is a fine distinction between shrinkage and loss, in that shrinkage refers to a reduction in food sample size, but loss indicates irreversible breakdown of cellular and tissue structure. Structural changes at the cellular level occur due to the transfer phenomenon during the dry period. As mentioned earlier, porosity and shrinkage during drying affect the transfer process as well as other quality characteristics. Accurate prediction of porosity and shrinkage helps design an advanced drying system to ensure quality products.Careful examination of theoretical and experimental models of porosity indicates the need for a simple model that can compensate for some of the limitations of theoretical models and use them for drying products and processes. Therefore, the aim of this study is to create a real contraction model with minimal use of experimental coefficients. A simple model considering heat gradient and humidity, glass transfer temperature, and drying time can be a potential way to predict structural changes during drying. Therefore, in this study, a new approach to contraction velocity is introduced. Therefore, paying attention to these parameters in shrinkage rate, process parameters, and material properties are considered in predicting metamorphism during drying. The physical meaning of shrinkage velocity is as follows: The velocity of the outer surface of the specimen during drying.

Apples were selected as the study sample in this study. Apples have high initial porosity and change of porosity during the drying period is very important. Therefore, it is expected that the experimental measurement of porosity changes and confirmation of the proposed model for this sample will be better. Kohn rose apples were prepared from a local supermarket and stored in a refrigerator at 2 ° C. The apples were selected from a box to have the same degree of ripeness. The treatment step was calculated using a liquid refractometer (BPTR-100 V3.0). The average ripeness of apples was 14.20 ± 0.20. The initial moisture content of fresh apples was calculated to be 77 ± 0.50% wb. 10 samples were used to measure the moisture content. Apples were removed from the refrigerator and washed at room temperature for one and a half hours. The skin of the samples was taken and cut into round pieces 10 mm thick and 40 mm in diameter. A hot air dryer with a fan was used. The drying temperature varied from 50 to 65 ° C and airspeed was set at 1 m / s. Particle density was measured using a gas (helium) pycnometer. The density of the mass was measured from the volume of the sample and the weight of the sample so that the sample was first coated with organic solvents to cover the open pores because there were numerous open pores that were large enough to be glass beads. They could have entered them. The density of the same sample was calculated before and after coating. The density of the glass beads was calculated from the weight of the required glass beads. The simulation was performed using COMSOL Multiphysics 5.6.0.280 Win / Linux. This software facilitates all stages of the modeling process including geometric structure definition, lattice, physical dimensioning, solving, and displaying results. COMSOL Multiphysics can manage variable properties that are a function of independent variables. A two-dimensional symmetry mode is also provided to facilitate the simulation process. There will be a small amount of 3D effects that can be ignored, and 3D consideration can complicate matters.

The physical quality of dried food depends on the degree of metamorphosis during drying. Shrinkage also has a significant effect on mechanical and textural properties as well as drying speed and kinetics. Accurate prediction of shrinkage can lead to better food quality and optimal drying process design. Food shrinkage depends on several factors including material properties, microstructure, mechanical properties, and process conditions. Experimental models can be created quickly that have a high impact. However, they do not show physical changes in the process. Physics-based models, on the other hand, are used as predictive models not only in food drying but also in other food industries. However, the theoretical model for predicting porosity is a complex one, due to the need for a number of properties that change under drying conditions. In this study, in order to counter the limitations of experimental and theoretical models, a simple shrinkage model based on the shrinkage rate was developed, which considers the main factors affecting porosity. The results show that the proposed model accurately predicts shrinkage and porosity, and this shows that the simulated shrinkage of the apple is related to the experimental results. For example, the porosity of the apple sample simulation is 0.6, which is consistent with laboratory data. The influence of desiccant air temperature and air velocity was also investigated. Studies show that process parameters (including air velocity and temperature) have a significant effect on the final porosity of the dried food. The porosity model proposed in this study requires the least experimental parameters. Future research could use this model to examine other foods because the structure of different foods is different, and this affects the porosity detection mechanism. Different types of process conditions can be used in future research to develop a general model for pore formation.

Sweet potato grows as a strong plant all over the world and is a product compatible with drought, temperature, and low fertile soils. Potatoes are high in starch, vitamins, minerals, and non-mineral salts such as calcium, phosphorus, iron and low in calories. This product is widely consumed fresh, boiled, etc. due to its functions for various reasons, such as improving immunity and preventing cancer, and its consumption is due to the abundance of nutrients such as carbohydrates, dietary fiber, minerals and other health-promoting compounds such as beta-carotene, vitamin C, phenolic acids, etc. are on the rise.Conventional evaluation methods for the internal quality of potatoes are mostly destructive and inefficient. In the practical production of potatoes, the quality evaluation system must have good accuracy, high speed, and low cost. Such goals can be achieved using modern techniques such as spectroscopy and electronic nose, as they do not require sample preparation, are non-destructive, efficient, fast, accurate, pollution-free, and inexpensive.Organic acids (OAs) are organic acidic compounds containing carboxyl groups that are widely present in organisms. Organic acids in fruits mainly include citric acid, malic acid, tartaric acid, and succinic acid. The traditional method for detecting OA concentrations is ion chromatography in the laboratory. Ion chromatographic testing requires standard solutions as a reference, also requires the use of chemical reagents, and organic acids must be measured separately. This is a tedious operation that wastes a lot of time. Therefore, a rapid detection technology is needed and preferred as an alternative.Near-infrared spectroscopy is a type of rapid detection technology that extracts spectral information from a sample through the difference between radiated light and reflected light. NIR technology has the advantages of fast performance, no use of chemical reagents and is also able to detect multiple components simultaneously. Spectral signals can be further amplified by the combined use of the Fourier transform technique. Fourier transform near-infrared spectroscopy has been widely used in the fields of food science, agricultural informatics, environmental monitoring, biomedicine, and pharmacy.Based on the simplicity of PLS regression, nonlinear methods are investigated to improve the PLS algorithm by embedding nonlinear core functions. This method plots the data before PLS scoring in a high-dimensional feature space, and the data converted in the new space characterize the samples. In this study, a neural network as a core function is designed to optimize PLS in the quantitative NIR analysis of OA concentrations in potato samples. A three-layer lattice with an adjustable number of neural nodes is designed to extract spectral feature variables to optimize the PLS core model.

Potato samples were harvested and 248 of healthy size and almost the same size were selected. The samples were transferred to the laboratory 24 hours after picking and stored at room temperature for 2 days. In the next 5 days, about 50 glands per day were selected and their OA concentration and FT-NIR spectrum were identified. Each potato sample was divided into two parts, half of which were used to detect the OA concentration and the other half to measure the NIR spectrum. The FT-NIR spectrum was measured using a PS-100 spectroradiometer (Apogee Instruments, INC., Logan, UT, USA) made in the USA. Temperature and humidity were kept constant at 25 ° C and 47% during the spectrum study.PLS kernel is an improved PLS method to deal with the nonlinear problem of spectral data. Raw data is mapped by a special nonlinear core function in high-resolution image space, so the original PLS linear algorithm can be used to discover the relationship between feature data and sample analysis. In short, this method can be done in two consecutive steps of mapping and regression.In modern studies, a neural network is a good tool for operating dynamic data, as it is flexibly taught by automatically fitting its link weights to the data-based model. A three-layer neural network was constructed in this study as a new nucleus for PLS output in the quantitative NIR analysis of potato OA concentrations.All 248 potato samples were divided into three parts for calibration, validation, and testing. The calibration section is used to create models and teach the model structure as well as the main algorithmic parameters. The validation section is used to check the model and optimize the parameter values. And the test section to evaluate the model.All 248 potato samples were divided into three parts for calibration, validation, and testing. The calibration section is used to create models and teach the model structure as well as the main algorithmic parameters. The validation section is used to check the model and optimize the parameter values. And the test section to evaluate the model.

Core PLS regression was applied to create FT-NIR calibration models to quantify OA concentrations in potato samples. The proposed network architecture was used as a new kernel conversion function to select attribute variables. The network was created connected with an input layer, a hidden layer, and an output layer.All 3114 wave number variables were transferred to the input layer. The same number of input nodes were generated to accept the data, and then perceptron units were applied, converting the data into a hidden layer. In the case of using a data-driven learning mechanism, the number of hidden nodes varies from 10 to 200 with step 10. Each Nh value was tested to screen for the best latent structure. Perceptron calculations converted the hidden data into an output layer, and a total of 20 output neurons were generated in the output layer to reduce the dimensions. These output variables were mostly used for PLS regression.In general, neural perceptron units were adjusted with their link weights, which automatically matched the data. 20 output variables were delivered to the softmax MLR predictor. Predictive errors were used for 50 rounds of error-feedback repetition optimization on link weights. Figure 3 shows that the RMSEV gradually shrinks with more repetitions and gradually decreases for each Nh number. This phenomenon means that the initial feedback and error replication mechanism can optimize machine learning for the network kernel. Duplicate optimized network link weights were used to serve the network architecture as a core evaluation function to optimize PLS regression. . The most optimal network structure was constructed with 130 hidden nodes and 20 output nodes.Then, the optimal network structure constructed with 130 hidden nodes and 20 output nodes is used as the core function for PLS regression. Hidden PLS variables were selected by network search mode. We tested PLS regression models with f = 1, 2… 20 based on the optimal network core. The results of model training for validation samples are shown in Figure 4. The optimal number of latent variables was determined as f = 8. The results of the network core model prediction and common cores are listed in Table 2.According to the principle of sample division introduced, PLS core models were quantified for FT-NIR analysis of potato OA concentration based on calibration samples and optimized by validation samples. The PLS model of the selected optimal network core should then be evaluated by 64 experimental samples that were unique to the model training process. Spectral data of the experimental samples were entered into the core of the optimal network with 130 hidden nodes and 20 output nodes.Table 2 shows that for PLS kernel regression, the proposed network kernel performs better than conventional kernels, regardless of the model training process or in the model evaluation process. Therefore, using neural network architecture to optimize the PLS regression kernel is a practical idea. FT-NIR calibration models have clearly improved compatibility by the adjustable network core.

Annual herbaceous rice, standing, rooted, shallow, strong, and white, belongs to the Oryza family, belonging to the Oryzeae family. Rice is the staple food of about 2.5 billion people, which is about 20 percent of the energy needed, and provides protein for 15 percent of the world's population. In general, tropical and subtropical countries Burma, Thailand, Vietnam, Laos, Indonesia, Philippines, Pakistan, India, USA, Japan, Italy, Egypt, China, Brazil, Cuba, Mexico, and Australia are the main rice producers in the world. Among them, Sadri, Tarom, and Hashemi cultivars are among the best and most high-quality rice cultivars native to Iran, and the most productive cultivars of this country can be Caspian, Speedroad, Sahel, Kadous, Shafaq, Darfak, Gohar and Neda pointed out. Accurate determination and evaluation of odor in rice require identification of substances affecting odor in parallel with the development of methods for determining their amount. More than 3 decades have passed since the beginning of studies related to recognizing the creative and effective factors in rice aroma. Much research has been done in the field of using more efficient and faster methods in identifying rice volatiles and identifying the main causes. Of the more than 100 known compounds in rice, a few are effective in creating its aroma and aroma. In the meantime, the electronic nose can detect volatile compounds in rice. The electronic nose has been used in extensive research to identify and classify food and agricultural products. Pandan leaf aroma of rice is a special feature and is used to differentiate the quality of rice. Quality determines whether it has a certain percentage of cleanliness and purity or not. Aromatic rice is usually preferred by consumers due to its good quality, which includes delicacy, shape, colour, aroma, taste, and consumers use aromatic rice for celebrations and occasions due to high demand and use good quality. The quality of aromatic rice is influenced by various factors such as cultivation location, climatic conditions, genetic activities and post-harvest. Important issues in the rice industry include quality control, incorrect labelling, grading and fraud in different types of rice. For this reason, the rice industry uses standard grades based on market criteria to identify grain. Due to these factors, quality control and fraud are the main issues that are wrong labelling and grading are the main problems. The use of human expert panels is the most common technique used to evaluate the quality of aromatic rice. They distinguish rice based on its aroma. With the rapid and rapid advancement of computer technology and sensor technology, the application of the bionic electronic nose, including a semiconductor gas-sensitive sensor and a pattern recognition system as a means of detection, offers a new method for rapid classification and digit recognition. Give. The electronic nose has also introduced a new method for classifying and detecting rough rice in a non-destructive and fast way. The aim of this study was to evaluate the ability and accuracy of the electronic noses using one of the chemometrics methods to distinguish pure rice cultivars from 3 gross cultivars.

First, 4 rice cultivars were prepared from the Iranian Rice Research Center located in Rasht. These 4 cultivars included 1 high-quality rice cultivar named Hashemi and 3 substandard rice cultivars named Neda, Khazar, and Sahel. Therefore, in the experiments, one genuine rice cultivar (Hashemi) and three non-genuine or counterfeit cultivars (mixture of Caspian, Neda, and Sahel cultivars with Hashemi cultivars) were prepared, so that the counterfeit cultivars each contained 80% of Hashemi cultivars and 20% of substandard cultivars. After preparing and mixing the cultivars, first, the samples were placed in a closed container (sample container) for 1 day to saturate the container with the aroma of rice, then the sample containers were used for data collection with an electronic nose. Were located. In this research, an electronic nose made in the Department of Biosystem Engineering of Mohaghegh Ardabili University was used. In this device, 9 low-power metal oxide (MOS) semiconductor sensors are used, which are given in Table 1 of the sensor specifications. The sample chamber was connected to the electronic nasal device and data collection was performed. The data collection was done by first passing clean air through the sensor chamber for 150 seconds to clear the sensors of odours and other gases. The sample odor was then sucked out of the sample chamber by the pump for 150 seconds and directed to the sensors, and finally, fresh air was injected into the sensor chamber for 150 seconds to prepare the device for repetition and subsequent tests. 22 replicates were considered for each sample. The study started with the chemometrics method with principal component analysis (PCA) to detect the output response of the sensors and reduce the data dimension. Principal component analysis (PCA) is one of the simplest multivariate methods and is known as an unsupervised technique for clustering data by groups. It is usually used to reduce the size of the data and the best results are obtained when the data are positively or negatively correlated. Another advantage of PCA is that this technique reduces the size of multidimensional data while eliminating additional data without losing important information.

The scores diagram (Figure 1) showed the total variance of the data equal to PC-1 (99%) and PC-2 (0%), respectively, and the first two principal components constitute 99% of the total variance of the normalized data. When the total variance is greater than 90%, it means that the first two PCs are sufficient to explain the total variance of the data set. According to the shape of Hashemi's main cultivar (a) on the left side of the chart and 3 fake cultivars (b, c, and d) are visible, which are well separated by the PCA method. Therefore, it can be concluded that e-Nose has a good response to rice odor and it is possible to distinguish between original and counterfeit rice cultivars, which shows the high accuracy of electronic nose in detecting the smell of different products. The correlation loadings plot diagram can show the relationships between all variables. The loading diagram (Figure 2) shows the relative role of the sensors for each principal component. The inner ellipse represents 50% and the outer ellipse represents 100% of the total variance of the data. The higher the loading coefficient of a sensor, the greater the role of that sensor in identifying and classifying. Therefore, sensors mounted on the outer circle have a greater role in data classification. According to the figure, it is clear that all sensors have played an important role in identifying rice cultivars, including the role of sensors No. 1 and 9, which are the same sensors MQ9 (to detect carbon dioxide, combustible gases) and MQ3 (to detect). Alcohol, methane, natural gases) were slightly less than the other sensors, which can be reduced by removing these two sensors to reduce the cost of making the olfactory device (to detect genuine and counterfeit rice) and save costs. In this study, an electronic nose with 9 metal oxide sensors was used to identify and distinguish between original and counterfeit rice cultivars. PCA chemometrics method for qualitative and quantitative analysis of complex data, an electronic sensor array was used. PCA was used to reduce the data and with 99 main components PC1 and PC2, it described 99% of the variance of the data set and provided a preliminary classification. The electronic nose has the ability to be used and exploited as a fast and non-destructive method to detect genuine and counterfeit rice cultivars. Using this method in identifying rice cultivars will be very useful for consumers, especially in restaurants and halls, in order to select pure and high-quality cultivars.

Existence of small and scattered lands and non-geometric properties, lack of road network between farms and lack of aggregation of agricultural parts of each farmer, etc. cause the cost of production to increase at different stages of production. The ultimate goal in agricultural production is to reduce costs and increase production efficiency. By using advanced tools related to each stage of production and the so-called mechanization of production, the farmer's concern in providing production factors and inputs to perform tasks and critical times on time is reduced. In order to justify the implementation of the plan to equip and renovate lands in paddy fields, the amount of energy consumed in rice production of paddy fields in the stages before planting, planting, holding and harvesting rice in ten paddy fields where the plan of equipping and renovating lands was implemented and ten paddies without a plan In Bandar Anzali district, four duties were examined, which showed a decrease of 6448.27 megajoules per hectare of total energy consumption in rice production. The results show a reduction in reliance on labor (which is a limiting factor in agricultural production) and the import of agricultural implements with high farm capacity also leads to increased speed and convenience of agricultural work for farmers. Integration and suitable dimensions of land, creating irrigation and drainage canals, ease of entry and use of agricultural implements and mechanization of different stages from tillage to after harvest, reducing water and seed consumption, which are the most important inputs used, are the main reasons for this amount. Reduce energy consumption.

Approximately three-quarters of harvested tomatoes are freshly used. Good quality is an important factor in distributing of fresh tomato. Chlorophyll is the green chemicals to provide required food of plants and ensure plant growth and productivity. The main function of chlorophyll is to absorb blue and red lights and perform photosynthesis. In recent years, the tendency to use of prediction methods such as soft computing and artificial intelligence for growth of plans has increased. The main aim of this study was to investigate the relationship between height and chlorophyll content in the leaves of tomato plants using modeling and predicting techniques and compare the accuracy of these methods. In this study, some cultivated plants of tomato were randomly selected for height and SPAD measurements. The results showed the relationship between Chlorophyll content and height of plants was very low (R2 = 0.276). However using the modelling of ANN and ANFIS improved the prediction power up to (R2=0.982 and 0.913), respectively.

In this research was investigated the impact of various pre-treatments on effective moisture diffusivity coefficient (Deff</sub></em>), specific energy consumption (SEC</em>), total color difference and shrinkage of blackberry (Rubus spp) in convective – infrared dryer. Experiments were done at three temperature leveles 50, 60, 70 °C with four different pretreatments, including of blanching thermal pretreatment at 70, 80, 90 °C, pulsed pretreatment with microwaves power 90, 180, 360 W, chemical with Ascorbic acid solution at concentration of 1% and mechanical pretreatment with ultrasound in times of 15, 30, 45 min. Results shown that the highest and lowest of Deff</sub></em> value (1.43×10-8</sup> m²/s) and amount (55.52 kWh/kg) was obtained using microwave pretreatment under 360 W power and 70 o</sup>C drying temperature, respectively. Also, the minimum and maximum extent of Deff</sub></em> (4.24×10-9</sup> m²/s) and (239.89 kWh/kg) were in controlled sample and drying temperature of 50 °C, respectively. The highest amount of color difference (30.28) and shrinkage (55.62%) were seen in controlled sample. The lowest value of color difference (7.01) and shrinkage (19.23%) was obtained in microwave pretreatment with power of 360 W and drying temperature of 70 o</sup>C. So, the value of SEC (76.86%), color difference (76.85%) and shrinkage (65.43%) of samples reduced significantly (P<0.05) using various pretreatments, respectively.

Today, the use of pretreatment operations has been considered to reduce the time and energy with the aim of reducing the cost of agricultural products drying, such as medicinal plants. In this research, the modeling and optimization of energy parameters in the Rosmarinus officinalis drying with microwave pretreatment was evaluated using response surface methodology in three treatments (90 W for 5 min, 180 W for 2.5 min and 360 W for 1.5 min) and control (without pretreatment) in a convection dryer at temperatures of 40, 50 and 60 oC and the constant air flow velocity of 0.4 m/s. The results showed that with increasing power (from 90 to 360 W) and temperature (from 40 to 60 °C), the amount of drying coefficient increased quadratically. The value of energy efficiency, drying efficiency, thermal efficiency and convective efficiency increased by increasing of power and temperature from 90 to 180 W and 40 to 50 oC, respectively, but these amounts decreased by increasing of power and temperature from 180 to 360 W and 50 to 60oC, respectively. The increasing of the power (from 90 to 360 W) and temperature (from 40 to 50 °C) increased the amount of specific heat, specific power and specific energy consumption, while with increasing of temperatures from 50 to 60 °C, these values were reduced during quadratic equation. Based on modeling using RSM, optimum conditions for obtaining of the best energy parameters were determined to be microwave power of 360 W and drying temperature of 60 oC with desirability 99.5%.

Agricultural production systems have a wide and varied range of energy consumption and environmental publications. Therefore, the study of environmental dimensions of any agricultural product is necessary. In this study, an integrated approach was used to assess and evaluate the greenhouse cucumber production process in East-Azarbaijan using life cycle assessment. The boundary of the system included all activities related to the production, transport and consumption of various inputs for the production of greenhouse cucumbers. Regarding the conditions of the study area, which has cold and dry air, information on spring cucumber cultivation was investigated. After collecting basic information, we calculated the life cycle log information (including three parts of consumption inputs, direct and indirect agricultural publications), and after obtaining indirect publications (Publication to Water, Publications to the Air and Publications to the Soil), the amount Pollutants were calculated in greenhouse cucumber production. According to the results of the study, the highest amount of contamination with 2914.81 kg of dichlorobenzene 861 was related to free water poisoning. The depletion of fossil resources with 1500 MJ was the second generation of pollutants, and the potential for global warming with a share of 183 Kg of carbon dioxide was the third largest pollutant.

The effects of hot air temperature (40, 50 and 60 oC) and Radiation Intensity (RI) (0.21, 0.31 and 0.41 w/cm2) on the response variables (drying time, Head Parboiled Rice Yield (HPRY), color value and hardness)) of parboiled rice were investigated. The drying was performed using hybrid hot air–infrared drying. The optimization of drying variables and the relationship between response variables and the influence factors were analyzed using response surface methodology (RSM).  Based on RSM results, the best mathematical model for prediction of HPRY, hardness and color value and drying time of samples was linear(R2= 0.96), quadratic(R2= 0.99), linear(R2= 0.93) and linear(R2= 0.99) equation, respectively. The HPRY (62.13- 68.13%) and hardness (130.27- 247.3 N) increased with increasing drying temperature and RI, while the color value (19.77- 18.03) and drying time (59.72- 34.41 min) decreased. The optimized parameters of drying were obtained 55 oC drying temperature and 0.41 w/ cm2 RI.

The soaking stage is the most important steps of the parboiling process. Hot soaking requires precise control, because starch granules are gelatinized during soaking. Degree of starch gelatinization (DSG) of rice was measured using differential scanning calorimetry (DSC). This method has high costs and can not be utilized to obtain online data. Thus, in this study a mathematical relationship correlating the DSG of rice to the paddy physical (paddy moisture) -electrical (electrical conductivity (EC) and capacitance sensor output voltage of paddy water) during the soaking portion of the parboiling process was formulated. For measuring of electrical properties of paddy water was designed and manufactured an experimental system of ohmic heater and capacitance sensor for measuring electrical conductivity and voltage, respectively. For doing experiment, paddy (Shiroudi variety) was parboiled by soaking at 60, 65 and 70 oC. At each temperature, samples of paddy and paddy water were selected at five different soaking times. The experimnet results showed that paddy moisture content (21.18-35.1%w.b.), electrical conductivity (0.63-1.6 mS.cm-1) and output voltage of capacitance sensor (216 – 595 mV) of paddy water and rice DSG (5.5 to 31.7%) increased significantly (p<0.05) and exponentially (R2>0.98), exponentially (R2>0.93), quadraticly (R2>0.95) and exponentially (R2>0.96) during soaking, respectively. Linear relationships were fitted between DSG of parboiled rice and physical-electrical properties of paddy water. The results revealed that EC of paddy water be able to predict the DSG of rice during soaking with the lowest regression error.

Iranian paddy (Shiroudi cultivar) has poor milling yield. So, parboiling process has been used for improving its milling quality. In this study, Shiroudi paddy was soaked and steamed at 65oC for 180 min and at 96oC for 2 - 10 min, respectively. After steaming, samples were dried in the shade (27±1 oC temperature and 20±5% relative humidity) to achieve a final moisture content of 11±1% (w.b.). Final, the effect of various steaming times on degree of starch gelatinization (DSG) and some quality properties of rice (head rice yield, color value and rupture force) were investigated. Results show that the DSG, color value and rupture force increased significantly (P

Drying is one of the most important stages in the rice milling process. In present study the effect of two drying techniques (intermittent and continuous drying) on the some quality properties of parboiled paddy (Fajr cultivar) were investigated. The intermittent drying was performed in two stages with a tempering time between drying steps using a combination dryer of hot air – infrared (HIR) at the first stage (from 35 to 23%) and microwave dryer at the second stage (from 23 to 13%). While drying technique of continuous was done using HIR dryer in one stage for moisture content reduction of paddy from 35 to 13% (w.b.). The experiments were carried out at drying temperature of 40, 50 and 60 oC, infrared radiation (IR) intensity of 0.32 and 0.49 w/cm2, microwave power of 100, 200 and 300 W and tempering duration of eight times previous drying (the first stage). After drying, quality properties (head rice yield (HRY), color and toughness force (TF)) of parboiled rice and drying time were measured. Results revealed that the effect of drying techniques, air temperature, IR intensity and microwave power on the quality properties of parboiled rice were significant (poC (200-0.32) and 40oC (100-0.32), respectively. But, the maximum drying time, color value and TF were obtained using continues drying method with 61.04 min, 19.63 and 227.3 N, respectively. In conclusion, the intermittent dryer technique is appropriate and recommendable method for parboiled paddy of Fajr variety.