mansour rasekh

Potato is an important vegetable that grows all over the world and is considered as an important product in developing and developed countries for human diet as a source of carbohydrates, proteins, and vitamins. This product is native to South America and its origin is from Peru, and 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. Meanwhile, the high-performance artificial neural network can be used to classify cultivars. An artificial neural network can classify and detect cultivars, is flexible and is used in most agricultural products. Azizi conducted a study on 120 potatoes in 10 different cultivars using a visual and image processing machine with a MATLAB R2012 software toolbox to detect texture, shape parameters and potato cultivars. First, potato cultivars were classified usithe ng LDA method, which obtained 66.7% accuracy. This method also erred in distinguishing the two cultivars Agria and Savalan and also classified the two cultivars Fontane and Satina in other classes. They also used artificial neural networks to classify potato cultivars, in which the network was 82.41% accurate with one hidden layer and 100% accurate with two hidden layers. In this study, it was found that different types of potatoes can be identified and identified with a very high level of accuracy using the three color characteristics, textural and morphological features extracted by the visual machine and the use of a non-linear classifier artificial neural network. Categorized.In another study that was conducted using neural networks and image processing on 5 sweet potato cultivars, the researchers showed that this method was successful and could classify sweet potato cultivars with 100% accuracy.By determining and examining the existing relations between the force and the deformation of agricultural products up to the point of surrender, the range of forces harmful to fruit can be determined so that harvesting and transportation machines are designed in such a way that the forces from them do not exceed this range. On the other hand, one of the ways to determine the degree of ripeness of the fruit is to touch and press it with the thumb, which is an experimental way and depends on the skill of the person touching it. The mechanical penetration test of the fruit can be an indicator to check the ripeness of the fruit by quantifying this diagnosis and using this diagnosis to determine the optimal harvest time.Several types of research have been conducted on the physical and mechanical properties of agricultural products in Iran and other countries. In a research conducted by Ali Mohammadi and Rasakh to determine some mechanical properties of lime fruit under quasi-static loading, the results showed that the effect of loading speed, loading direction and size of a lime on the breaking force of lime is significant. As the size of the lemon decreases, the breaking force and deformation decrease, and also with increasing loading speed, the braking force increases. In another research conducted by Mohd Nejad and Khosdada, the effect of size, speed and direction of loading on the mechanical properties of lime was investigated and the results showed that the interaction of loading speed and size on fracture energy and toughness and the main effects of size, loading speed and The loading direction is significant on the modulus of elasticity, but none of the effects on the rupture force is significant.

First, 5 different varieties of potatoes (Agria, Spirit, Sante, Marfona and Jelly) were prepared from Ardabil Agricultural Research Center immediately after harvest. After preparing the varieties, 21 samples of each potato variety were prepared using a cutting cylinder and then data collection was done with santam machine.To measure the rupture energy of potato samples, santam device (available in the Biosystem Engineering Department of Mohaghegh Ardabili University) was used. For this purpose, each potato variety was subjected to a compressive force at three loading speed levels (10, 40 and 70 mm/min) with 7 repetitions. Then, using the amount of rupture force and deformation (surface area under the force-deformation curve), the amount of rupture energy was calculated. The data obtained from the experiment were analyzed statistically with Minitab 18 software.

Conclusion:

 The amount of rupture energy in 5 different varieties of potato was obtained using santam device and equation 1. The values obtained for 5 potato cultivars were analyzed using Minitab18 software and the results are given in Table (1).The results of the analysis of variance for the firmness of 5 different potato cultivars were significant at the 1% level and the coefficient of variation was 9.6. In Figure 2, you can see the average results.According to Figure 2, it is clear that the lowest amount of rupture energy is related to the Agria variety and the highest is related to the Jali variety. Also, it can be found that with a loading speed of 10 mm/min, the highest amount of rupture energy is obtained in all figures.In this research, the firmness level for 5 different potato cultivars was calculated using the santam machine available at Mohaghegh Ardabili University and the area under the force-deformation curve. The amount of calculated rupture energy has the ability to be used as a method for the proper selection of different potato cultivars. The use of this method in potato cultivars will be very useful for factories such as chips factories and processing units, and it is also expected that similar methods related to mechanical properties such as crispness and hardness and with the help of different statistical methods to optimize production and The processing of agricultural products can be used in the food industry, which leads to more customer-friendliness and can also reduce agricultural waste.

Introduction:

 Potato with 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. After wheat, rice and corn, potato is the fourth product in people's food basket, which in Iran sometimes takes the place of rice and takes second place, which shows its importance in meeting people's food needs. According to the reports of the Food and Agriculture Organization of the United Nations, the area under potato cultivation in Iran in 2019 was more than 164 thousand hectares and the harvested product from this area was about 32.5 million tons. 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.Agria, Sante, Arinda, Marfona, Jelli, Born, Satina, Milva, Banba, Fontane, Ramos and Esprit varieties are among the most common potato varieties in Iran. Due to the increasing expectations for food products with high quality and safety standards, it is necessary to accurately, quickly and purposefully determine the characteristics of food products. 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 handling 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 the 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 color and no sprouting. Storage conditions after harvesting can cause changes in the chemical composition and quality of the product.The nutritional and chemical composition of potatoes differs from each other depending on the variety, storage period, growing season, soil type and pre-harvest nutrition. In general, potatoes contain 70-80% water and 16-24% starch and contain small amounts (less than 4%) of protein, fat, anthocyanins, minerals, etc.Storage conditions after harvesting can cause many changes in the chemical composition of potato tubers and as a result, change the quality characteristics of the final product. Sugar and starch are the main components that are affected by metabolism after harvesting in the potato tuber and may ultimately affect their texture, sensory and cooking properties.The quality of potatoes and, consequently, the quality of processed products, significantly depends on the variety and environmental conditions, both during the growing season and during the storage period.Although the quality of raw potatoes is determined primarily by the size, shape, color and attractiveness of the tuber, its quality is mainly determined by examining the quality of the final product. The quality of processed potato products is evaluated in terms of color, flavor and texture, and most of their quality depends on the quality of raw potatoes.By analyzing the relationship between the color of chips, dry solids, sucrose, reducing sugar, ascorbic acid, protein and storage temperature data, Meza showed that dry solids, reducing sugar and sucrose in determining the color of fresh potato chips and reducing sugar, tuber temperature and sucrose content are very important in determining the color of stored tuber chips, and the relative importance of each of these parameters changes with the variety and age of potato tubers.

different varieties of potato were prepared from Arallo Agricultural Research Center (Ardebil Province) immediately after harvest. Then, data collection was done from different samples and cultivars (measurement of carbohydrate content) as explained below.The carbohydrate content of the samples was extracted using the equipment available in the central laboratory of Mohaghegh Ardabili University. This process was carried out by the Schlegel method, in which carbohydrates were extracted using 95% ethanol based on the sulfuric acid method in each sample. The amount of light absorption of each sample was obtained from a nano-spectrophotometer device (Nanodrop) with a volume of 1000 microliters (Figure 1) using a cuvette (made by Termo scientific company from the USA) and the amount of extracted carbohydrates were obtained based on micrograms per millilitre from the standard curve.Glucose was used to prepare the standard curve. Serial dilution of glucose was prepared and color development at 490 nm was controlled for different concentrations of glucose and one millilitre of distilled water was used as a blank. This standard curve was used to calculate the concentration of total carbohydrates in the samples. The standard curve had a coefficient of determination of 0.9955.For each sample, data collection was done in three repetitions and the amount of absorption wavelength and then the amount of carbohydrate was calculated.

In order to obtain the number of carbohydrates, the number of the absorption wavelength was placed in the relationship obtained from the standard curve, and the number of carbohydrates was obtained in micrograms per millilitre. The results of the analysis of the variance of the effect of cultivar on potato carbohydrate content can be seen in Table 1. According to the analysis of the variance table, the effect of variety on potato carbohydrate content was significant at the 1% probability level.As you can see, Sante and Esprit cultivars have more carbohydrates than other cultivars. Also, the carbohydrate content of the Marfona variety was the lowest.According to the data and results of the research, it was observed that the amount of carbohydrates in different potato cultivars is different, and Sante and Esprit cultivars had more carbohydrates at the time of harvest. Also, according to the resulting graphs, it was observed that the amount of carbohydrates of the Marfona cultivar is lower than other cultivars. It is recommended to choose a more suitable variety according to the type of consumption and the importance of quality characteristics for consumption and processing, according to the storage conditions and time.

The use of plant-derived compounds is common in medicine and preventive health care, while the scope of use of some substances is steadily increasing. The mint family, with more than 200 genera and 3000 species, is very important economically and medicinally. The mint genus contains 25 to 30 species that grow in different temperate regions of Asia, Europe, Australia and South Africa. There is a great diversity in terms of chemical composition among the species of the mint genus. Peppermint essential oil (Mentha spicata L.) is rich in carvone, which produces the special aroma of mint. The yield of essential oil of Sentha spicata is lower than that of Mentha piperita. Carvone is the main component of Mentha spicata and Mentha longlifolia, while Carvone is absent in Mentha piperita, Mentha aquatic, Mentha arvensis and Mentha pulegium. Peppermint essential oil and extract are used in the pharmaceutical, cosmetic and food industries all over the world. Mentha spicata essential oil and leaves have therapeutic uses and its general properties are analgesic, tonic, stomach tonic, antitussive, anticonvulsant, astringent, analgesic and sedative. Peppermint oil has been used since ancient times for medicinal purposes, mostly to treat headaches, colds and neuralgia. It can also relieve skin irritations and digestive problems and has antispasmodic effects. Although, there is mixed information about the chemical composition of Mentha spicata essential oil, many studies have confirmed carone and limonene as its main components. Carvone is responsible for the smell of peppermint essential oil. The high price of carvone in the market has pushed breeders to improve mint varieties with high carvone. Different chemotypes are characterized by specific odors and biological activities, which indicate different applications in the aromatic and pharmaceutical industries. For example, Europeans enjoy the scent of Carvone. The use of medicinal plants in the food and pharmaceutical industries depends on the amount of biologically active substances and their chemical composition. Changes in the concentration of volatile compounds of mint during drying also depend on several factors, including drying conditions (temperature, air speed), humidity, variety and age of the plant, climate, soil and harvesting method. The drying process and storage conditions of the dried plant can have an adverse effect on the medicinal properties of the essential oil. Drying is one of the efficient methods to preserve agricultural products and maintain food quality. Drying, as an important food preservation technique, is used in the food industry. Drying is required to reduce the water activity of the product to suppress the growth of microorganisms and inhibit chemical reactions to increase the shelf life of the product at room temperature. In addition, drying lightens shipping weight and reduces storage space. Conventional drying methods include hot air drying (HAD), vacuum drying (VD), vacuum freeze drying (VFD), and microwave-hot air alternating drying (MW-HAD). HAD is the most common method that dries food in an oven with a constant flow of hot air. As an optimal approach for drying raw vegetable food, this method has easy operation and low cost, but it requires a long drying time and has low energy consumption.

After the drying process, the essential oil was extracted from the dried product, and for this purpose, a Clonger machine was used using the water distillation method. Distillation with water is a method of extracting essential oils. This method is cheap because it mostly uses water as a solvent. Qualitative GC-MS analysis of the extracted essential oils was performed using an HP 6890 gas chromatograph coupled to an HP 5973 mass-selective detector (Agilent Technologies, Foster City, CA, USA) operating at 70 eV mode. The electronic nose consists of three parts: (1) a sample transport system (2) a detection system consisting of a set of gas sensors with partial characteristics and (3) an odor data processing system. The e-nose instrument can detect the presence of VOCs in various molecular structures with high accuracy and reliability regardless of more or less odor. Samples were analyzed using a portable e-nose, which consists of a multiple gas sensor array, a signal acquisition unit, and pattern recognition software. Essential oil samples (1 mL) were placed in a 10 mL sealed glass vial and equilibrated at 40 °C for 30 min under stirring. Clean ambient air was used as the carrier gas to transport the volatiles in the headspace of the sealed glass vials to the temperature and humidity controlled sensing chamber. The conductivity change in the sensor array is expressed by the normalized response of the sensor. Each measurement cycle lasted 100 seconds, which allows the sensor to reach a steady state, and the data collection interval using a computer was 1 second. Between measurement cycles, the sensor was purged for 200 s with purge gas filtered through activated charcoal to return the sensor signal to baseline. 15 measurements were made for each sample of peppermint essential oil. Data obtained from GC-MS analysis were first processed by in-house MSD Chemstation and structural identification was performed through NIST 2014 library research along withretention index (RI) validation. The dataset consists of pre-processed signals from 9 MOS gas sensors obtained in the e-nose during 120 measurements corresponding to 8 independent samples evaluated with 15 repetitions. The performance of e-nose for evaluating peppermint essential oil samples was evaluated using three supervised statistical methods, namely QDA, MDA and ANN.

Drying is the most suitable method used to preserve the natural products of plants. Choosing a special drying method is one of the important costs in the production and commercialization of medicinal plants. This study determined the effect of different drying methods on the quantity and quality of peppermint essential oil. The results showed that the highest yield of essential oil was in the HAD1A drying method and the lowest yield was related to the sun drying method. Also, the obtained compounds of the essential oil were determined by the GC-MS method, and in the HAD drying method, 18 compounds were determined, and the content of some of them decreased significantly with the increase of the drying temperature. In the dried samples, the main components were Carvone (64.30-7.45%), Limonene (24.21- 6.59%) and Carveol (18.34-1.92%). Also, the aroma characteristics of mint essential oil were evaluated with the help of an E-nose. Three classification algorithms QDA, MDA and ANN were used, and the highest percentage of classification related to QDA and MDA methods was 100%, and the accuracy of the ANN method was also 0.967%. The findings of this study provide a theoretical basis for the development of hot air thin layer drying process for medicinal plants and improving their sensory quality and related products. The future perspective is to continuously improve the in situ drying technique for medicinal plants and develop a suitable monitor system to control the sensory quality of the final products based on the findings of the current study.

Potato with the scientific name (Solanum tuberosum. L.) is a plant that is cultivated as an important product 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. After wheat, rice and corn, potato is the fourth product in people's food basket, which in Iran sometimes takes the place of rice and takes second place, which shows its importance in meeting people's food needs. According to the reports of the Food and Agriculture Organization of the United Nations, the area under potato cultivation in Iran in 2019 was more than 164 thousand hectares and the harvested product from this area was about 32.5 million tons. 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.Since the expectations for food products with appropriate quality and safety standards have increased, it seems necessary to determine the characteristics of food products. In the meantime, in the potato product, quality evaluation 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 handling in It is the harvesting stage. 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. Potatoes for the processing industry must have some requirements such as low sugar content, high dry matter and specific weight, high antioxidants, light skin color and no sprouting. Stored potatoes may suffer from sweetening, rotting, water loss and sprout growth during storage. Storage conditions after harvesting can cause changes in the chemical composition and quality of the product. Therefore, the management of potato tubers in all stages of production and storage is very important.The quality of this product and its processed products depends on the variety and environmental conditions (both during the growing season and during the storage period). By analyzing the relationship between chips color, dry solids, sucrose, reducing sugar, ascorbic acid, protein and storage temperature data, Meza showed that dry solids, reducing sugar and sucrose in determining the color of fresh potato chips and reducing sugar, tuber temperature and sucrose content are very important in determining the color of stored tuber chips and the relative importance of each of these parameters changes with the type of tuber variety and storage.The amount of potato sugar significantly depends on the variety and storage temperature and it happens quickly in cold weather. In potato tubers during the storage period, starch is gradually hydrolyzed and turned into sugar (glucose). In unripe tubers and potatoes that are stored for a long time at low temperatures, there are more amounts of glucose, this feature is considered an anti-quality feature for the potato product in the industry, why? The increase of regenerating sugars causes the produced chips to turn brown and bitter. Storing potatoes for more than 7 months can cause ageing or old sweetness, and storage at a temperature of fewer than 10 degrees Celsius can cause sweetness caused by cold. Although potato storage at low temperatures can have beneficial results such as reducing respiration rate, reducing physiological ageing, inhibiting germination, reducing evaporative water loss and reducing microbial pathogens. But sugars accumulate when the balance between starch degradation and breakdown is not established and there is carbohydrate respiration. Therefore, potatoes that are kept at a lower temperature have a lot of sugar. Researchers reported that when potatoes are stored at zero degrees Celsius, there will be a complete stop in th accumulation of sugar.

First, 5 different varieties of fresh potatoes (Spirit, Agria, Sante, Jelli and Marfona) were prepared from Ardabil Agricultural Research Center (Arallo District). It should be noted that these potatoes were prepared immediately after harvesting so that there are no changes in the amount of sugar due to the time interval after harvesting.The amount of sugar in each sample was measured in three replicates using a liquid refractometer model BPTR100 (Middle East Control System Company, brand name Prisma Tech, made in Iran) available at Mohaghegh Ardabili University (Figure 1). For this, first, some water was taken from the samples and after pouring it into a microtube, it was placed inside a refrigerated centrifuge (top speed) of the LISA France model, and after rotating at a speed of 1800 rpm for 2 minutes, the impurities at the bottom settled and separated the pure liquid (pure potato juice). After reaching the ambient temperature, the said liquid was placed on the refractometer and its sugar content was read in terms of Brix.

The results of the analysis of the variance of cultivar effect on potato sugar content are shown in Table 1. According to the analysis of the variance table, the effect of variety on potato sugar level was significant at 1% probability level. You can see the changes in the amount of sugar of different potato cultivars in Figure 2. The difference in the amount of sugar in different cultivars is due to the difference in their starch hydrolysis (the main compound of potato-potato tubers) which occurs as a result of the respiration of the product, and it is in this way that the higher the amount of starch in If one variety is less, that variety has less sugar, and it is important to note that the chemical composition depends on the potato variety, soil, climate, and agricultural factors. In general, it can be said that potatoes with more sugar are suitable for the chips industry, and potatoes with medium sugar are suitable for frying. According to Figure 2, the highest amount of sugar is related to the Esprit variety and the lowest amount is related to the Jelly variety. The reason for the difference in the amount of sugar between different cultivars is mainly related to the type of soil, fertilizer and poison used. According to the data and the results of the research, it was observed that the amount of sugar in different varieties of potato is different, in the meantime, the jelly variety generally has a lower amount of sugar at the time of harvesting, and the variety of Esprit has the highest amount of sugar at the time of harvesting. Was. It is recommended to choose a more suitable variety according to the conditions according to the type of consumption and the importance of quality characteristics for consumption and processing, of course, physical characteristics are also involved in this relationship, which should be taken into consideration.

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 as 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, and 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. Meanwhile, the high-performance artificial neural network can be used to classify cultivars. An artificial neural network can classify and detect cultivars, is flexible and is used in most agricultural products. Azizi conducted a study on 120 potatoes in 10 different cultivars using a visual and image processing machine with a MATLAB R2012 software toolbox to detect texture, shape parameters and potato cultivars. First, potato cultivars were classified use the ng LDA method, which obtained 66.7% accuracy. This method also erred in distinguishing the two cultivars Agria and Savalan and also classified the two cultivars Fontane and Satina in other classes. They also used artificial neural networks to classify potato cultivars, in which the network was 82.41% accurate with one hidden layer and 100% accurate with two hidden layers. In this study, it was found that different types of potatoes can be identified and identified with a very high level of accuracy using the three color characteristics, textural and morphological features extracted by the visual machine and the use of a non-linear classifier artificial neural network. Categorized.In another study that was conducted using neural networks and image processing on 5 sweet potato cultivars, the researchers showed that this method was successful and could classify sweet potato cultivars with 100% accuracy.By determining and examining the existing relations between the force and the deformation of agricultural products up to the point of surrender, the range of forces harmful to fruit can be determined so that harvesting and transportation machines are designed in such a way that the forces from them do not exceed this range. On the other hand, one of the ways to determine the degree of ripeness of the fruit is to touch and press it with the thumb, which is an experimental way and depends on the skill of the person touching it. The mechanical penetration test of the fruit can be an indicator to check the ripeness of the fruit by quantifying this diagnosis and using this diagnosis to determine the optimal harvest time.Several types of research have been conducted on the physical and mechanical properties of agricultural products in Iran and other countries. In a research conducted by Ali Mohammadi and Rasakh to determine some mechanical properties of lime fruit under quasi-static loading, the results showed that the effect of loading speed, loading direction and size of a lime on the breaking force of lime is significant. As the size of the lemon decreases, the breaking force and deformation decrease, and also with increasing loading speed, the braking force increases. In another research conducted by Mohd Nejad and Khosdada, the effect of size, speed and direction of loading on the mechanical properties of lime was investigated and the results showed that the interaction of loading speed and size on fracture energy and toughness and the main effects of size, loading speed and The loading direction is significant on the modulus of elasticity, but none of the effects on the rupture force is significant.

First, potatoes in 5 different varieties (Agria, Esprit, Sante, Marfona and Jelli) were prepared at Ardabil Agricultural Research Center and stored at 4-10 degrees Celsius. One day after preparing the varieties, 21 samples of each potato variety were prepared using a cutting cylinder and then data collection was done. The data collection included mechanical properties.To determine the toughness of the samples, the santam machine available in the mechanical properties laboratory of the biosystem engineering department of Mohaghegh Ardabili University was used. Each potato variety was subjected to a compressive force at three loading speed levels of 10, 40 and 70 mm/min and with 7 repetitions. Then, using the amount of braking force, deformation and sample volume, the toughness was calculated according to equation (1).
These experiments were carried out in 5 storage periods (at 2-week intervals).

The toughness of different cultivars showed different behavior during the storage period so no changes were observed in the Marfona cultivar for toughness during the storage period, and in the Sante cultivar, the toughness level was almost the same at the beginning and end of the period and only in the middle of the storage period the value There was a slight increase. But in the case of Agria, Sprit and Jali cultivars, it should be said that the changes in toughness do not follow a specific trend and are unpredictable. Also, according to Figure 3, it is quite clear that in all figures, the lower the loading speed, the greater the toughness obtained, and the reason for this is that at a lower loading speed, the breaking force occurs in high values. Falls, and as a result, according to relation 1, the toughness value also increases.According to Figure 3, during the storage period, the two varieties of Jelli and Marfona (especially the Marfona variety) did not change much in terms of toughness and considering this issue, it is recommended to use these two varieties for some purposes, including frying.In this research, firmness was calculated for 5 different varieties of potatoes in 5 storage periods using the santam machine available at Mohaghegh Ardabili University and with the help of equation 1. The results showed that Jali and Marfona cultivars maintained their firmness during the storage period, and hence they are recommended for uses such as chips.

A large number of biogas plants fed by municipal solid waste (MSW) are installed around the world. However, extensive research is being conducted with the aim of maximizing the performance of the anaerobic digestion process, in order to further compare it with fossil fuels. One of the most important problems of this process and the main obstacle to this transformation is the complex structure of lignocelluloses and the difficulty of its biological transformation. For this purpose, the effect of different concentrations of nanoparticles (NPs) of zero valent iron (ZVI) (50, 70, 90 and 110 ppm ZVI NPs) on maximizing biogas and methane production was evaluated. In this study, the ratio of co-digestion mixing MSW and sewage sludge (SS) was determined from our previous studies as the optimal ratio (MSW: SS: 60:40). The results showed that the highest methane yield was obtained by adding 90 ppm ZVI NPs based on statistical analysis (at least a significant difference using Duncan test) compared to the control treatment (p<0.05). Iron nanoparticles (Fe NPs) also led to an 85% increase in cellulose, a 64% decrease in lignin and a 33% decrease in hemicellulose, indicating an increase in biodegradability due to NPs. The highest methane production was obtained at 90 ppm ZVI NPs concentration which increased by 45% compared to the control treatment and the highest reduction of TS and VS in this digester was 31 and 17% compared to the control digester, respectively.

Pepper (Capsicum annuum L.) is one of the most consumed vegetables in the world, containing a large amount of vitamins C and A, as well as minerals. Therefore, the consumption of about 60 to 80 g of pepper per day can provide 100 and 25% of the recommended daily amount of vitamin C and A, respectively. In addition, this horticultural product contains considerable levels of other health-promoting substances with antioxidant activity, including carotenoids, flavonoids, and other polyphenols.The quality of fresh pepper depends primarily on consumer acceptance, which is determined primarily by color, pungency, and aroma. Aroma plays an essential role in determining the sensory characteristics of these products. Volatile organic compounds (VOCs) are generally associated with the taste and aroma of foods and are important factors in assessing consumer acceptance or rejection. Consequently, food quality, originality, purity, and origin can be evaluated by determining VOC.Because it is important to distinguish hot peppers from sweet ones, we used an electronic nose to determine food quality in this study. Research has shown that the electronic nose is able to discriminate between products.

The variety used in this study was Padrón, a very popular species in Spain. The peppers can be harvested when they reach a length of 2.5 to 4 cm. One fruit out of 20 has a spicy flavor, while the rest has a mild taste. The green fruits showed no signs of ripening or discoloration and remained completely green.The peppers weighed an average of 12 ± 2 g when fresh. The weights for the sweet and spicy varieties were determined by weighing 30 fruits each. The fruits to be examined were evaluated by electronic nose.In this research, an electronic nose made in the Department of Biosystem Engineering of Mohaghegh Ardabili University was used. This device uses 9 low-power metal oxide (MOS) semiconductor sensors.The sample chamber was connected to the electronic nose and data collection was performed. The data collection was done by first passing clean air through the sensor chamber for 100 seconds to clear the sensors of odors and other gases. The sample odor was then sucked out of the sample chamber by the pump for 100 seconds and directed to the sensors, and finally fresh air was injected into the sensor chamber for 100 seconds to prepare the device for repetition and subsequent tests. 30 replicates were considered for each sample.The study began with the chemometrics method with principal component analysis (PCA) to detect the output response of the sensors and reduce the data dimension. In the next step, Quadratic detection analysis and Mahalanobis detection analysis (QDA and MDA) were used to classify 2 group of pepper. 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 with each other.Quadratic detection analysis and Mahalanobis detection analysis (QDA and MDA) are the most common monitored technique for separating samples into predetermined categories. This technique selects independent data variables to differentiate the sample that is to follow the normal distribution. The QDA and MDA are based on linear classification functions in which intergroup variance is maximized and intragroup variance is minimized.

Principal component analysis diagram shows the total variance of the data equal to PC-1 (90%) and PC-2 (6%), respectively, and the first two principal components constitute 96% of the total variance of the normalized data. When the total variance is above 90%, it means that the first two PCs are sufficient to explain the total variance of the data set. two group of pepper are well differentiated by PCA method. Therefore, it can be concluded that e-Nose has a good response to the smell of 2 group of pepper and they can be distinguished from each other, which shows the high accuracy of the electronic nose in detecting the smell of different products.The correlation loadings plot diagram can show the relationships between all variables. The loading diagram shows the relative role of the sensors for each principal component. The inner ellipse shows 50% and the outer ellipse shows 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, the sensors located on the outer circle have a greater role in data classification and it is clear that the three sensors MQ4, MQ9 and TGS822 have played an important role in identifying 2 group of pepper.The correlation loadings plot diagram can show the relationships between all variables. The loading diagram shows the relative role of the sensors for each principal component. The inner ellipse shows 50% and the outer ellipse shows 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, the sensors located on the outer circle have a greater role in data classification and it is clear that the three sensors MQ4, MQ9 and TGS822 have played an important role in identifying 2 group of pepper. Unlike the PCA method, the LDA method can extract multi-sensor information to optimize resolution between classes. Therefore, this method was used to detect 2 group of pepper based on the output response of sensors. The results of detection of cultivars were equal to 100%.The electronic nose has the ability to be used and exploited as a fast and non-destructive method to distinguish sweet and hot pepper from leaf odor. Using this method in identifying sweet and hot pepper will be very useful for consumers, especially processing units and food industries in order to select appropriate cultivars. Since the detection of pepper using an electronic nose has not yet been researched, the promising results of this study can be widely applied in the sorting industry.

Wheat is one of the most important grains in the human food basket, which is known as a major source of energy, protein and fiber due to its valuable nutrients. The post-harvest stage of the wheat crop is explained in two ways: either it is sent to food processing factories or it is stored in silos for sale at regular intervals. Various parameters represent the quality of wheat grain that the percentage of purity of the mass is one of the main factors affecting the purchase price of the product. Several types of non-wheat grains, including germinated grains, broken grains, legumes, weed seeds, insect-damaged grains, foreign matter (pebbles, straw), etc. are the main sources of impurities in wheat. Researchers have always tried to develop computer-based solutions for impurities in wheat grain to be able to develop automated wheat grain separators. Image processing based on morphology, color and texture characteristics of grains has been used for various applications in the grain industry, including grain quality assessment and wheat classification. Various grading systems based on image processing have been studied. The presence of various impurities at the time of wheat harvest is one of the important factors in reducing the quality of wheat, so it seems necessary to detect impurities in wheat grain. The quality of wheat has a significant effect on its marketability. In addition, if wheat is used as a crop seed, the impurities in the mass will be a determining factor in the yield of the future crop.

In statistical analysis of data, situations are sometimes encountered in which the relationship between problem variables is very complex. This makes it difficult to analyze and process the data, so that sometimes no definite relationship can be found between the variables. In these cases, instead of purely theoretical research, applied research is done. Artificial neural networks are one of the solutions that, by processing experimental data, discover the knowledge or law behind the data, and transfer it to the network structure. In this study, the possibility of identifying wheat in wheat grain mass and estimating the amount of impurities in the mass, based on video processing using two types of artificial neural network (ANN) algorithms and hybrid genetic algorithm (GA) has been investigated. For this study, the code related to the artificial neural network with two hidden layers and the number of different neurons in each layer was written in MATLAB software. This code was used to identify and classify each component in the wheat grain mass. The main task of ANN is to learn the structure of the model data set. To achieve this, the network is trained with examples of related outcomes to generalize the capability. Multilayer artificial neural networks (MLPs) are the most common ANN models. In the present study, to reduce the computational load and increase the accuracy of the results, as well as to save time, some parameters that can be changed in the genetic algorithm were extracted as a fixed number using trial and error method. Among these parameters is the number of layers in the main structure of the neural network.

A hidden layer with a number of neurons 2 to 12 was used as an even number. It should be noted that the number of neurons above this amount of computational time increased dramatically and did not have much effect on classification accuracy. Another parameter in this field is the Max Reproduction factor (Max Generation) which according to the results of trial and error for this factor, the results showed that increasing this value more than 30 has little effect on classification accuracy and decreases the mean squared error. And only increases the computation time, so a constant value of 30 was considered for this parameter. 4 values of 50, 100, 150 and 200 were used for the Pop Size parameter. Values above 200 dramatically increased computational volume and processing time, so values over 200 were omitted. Values less than 50 also reduced classification accuracy, and values less than 50 were excluded from the analysis process. After preparing the video of mass movement on the conveyor belt, using MATLAB software and image processing toolbox, 17 shape features, 12 color features and 6 texture features were extracted from each grain sample in the image. The data obtained from the image processing section were classified into five categories: wheat, barley, oats, straw and weed seeds. Two types of artificial neural network (ANN) algorithms, feeder (newff) and feeder (newcf), and hybrid genetic algorithm (GA) were used to achieve the highest classification accuracy and minimum error.

Techniques related to image segmentation were used to separate objects within the image. In this stage of image processing, an attempt is made to separate interconnected objects using a variety of morphological and color methods in the image. In fact, the purpose of separating interconnected objects in the image is to make it possible to examine the individual objects in the image separately and extract the different characteristics of each of them. The results showed that from 36 different artificial neural network (ANN) structures, the 5-4-10-35 structure for the newff algorithm with 100 and 89.74% accuracy for training and testing conditions, respectively, with a processing time of 10.39 seconds and the structure 5-8-10-35 for newcf algorithm was obtained with 100% accuracy for training conditions and 87.17% for test conditions with a processing time of 44.94 seconds. On the other hand, the results of the hybrid GA algorithm showed the highest classification accuracy with 95.55% and 86.66% for training and testing, respectively, in a structure in which 8 neurons in the hidden layer with a population size of 200 were used. Was obtained. According to the obtained results, the use of video processing using ANN artificial neural network and newff algorithm due to high accuracy and lower computation time is a powerful tool for detecting impurities in wheat grain mass. Therefore, the use of artificial neural network with the help of video processing has the ability to classify wheat grains and can be used in a practical way. Given the importance of grain mass velocity in the discussion of industrial application, it is suggested that higher grain mass velocities be investigated in a similar way.

Potato with the scientific name Solanum tuberosum. L is a plant that is cultivated as an important crop in all countries and is known in the human diet as a source of carbohydrates, proteins, and vitamins. This product is native to South America and originally from Peru. Potato is the fourth crop in the food basket of the people after wheat, rice and corn, which sometimes replaces rice in Iran and is in the second place, which shows its importance in meeting the nutritional needs of the people. According to the reports of the Food and Agriculture Organization of the United Nations, the area under potato cultivation in Iran in 2019 was more than 164 thousand hectares and the harvest from this area was about 5.32 million tons. In the food industry, this product is converted into a variety of products such as baked potatoes, fried potatoes, potato chips, potato starch, dried fries and so on. Due to the increasing expectations for food products with high quality and safety standards, accurate, fast and purposeful determination of food characteristics is essential. In the potato crop, quality evaluation after the harvest stage seems necessary to provide a reliable and uniform product to the market, because the potato, like many other products, has a non-uniform quality and care during the harvest stage. - Be. In addition, food safety and desirability play an important role in the food industry and is directly related to people's health. In addition, a large part of the potatoes used in the processing industry, potatoes are stored, so given the importance of this food and the demand of the people throughout the year, only through optimal and long-term storage can meet the needs of applicants. Was responsive. Potatoes for the processing industry must have some requirements such as low sugar content, dry matter and high specific gravity, high antioxidants, light skin color and no germination. Stored potatoes may be sweetened, rotted, dehydrated, and sprouted during storage. Post-harvest storage conditions can cause changes in chemical composition and product quality. Therefore, the management of potato tubers is very important in all stages of production and storage. Potato changes in storage depend on the variety, storage conditions. Although potato storage is very necessary for domestic and industrial use, due to its chemical and physical changes in the warehouse, it should be said that the characteristics of high quality potatoes in commercial exchanges of this product include more than 70 to 80% of tubers. Light-colored, uniform, firm, no bruising, no scaling, no cracks, no insect damage, rot and greening. Post-harvest storage conditions can cause many changes in the chemical composition of the potato tuber, resulting in changes in the quality characteristics of the final product. Sugar and starch are the main components that are affected by postharvest metabolism in the potato tuber and may ultimately affect their texture, sensory and cooking properties. The quality of potatoes and, consequently, the quality of processed products, depends significantly on the cultivar and environmental conditions, both during the growing season and during storage.

First, potatoes were prepared in 5 different cultivars and stored at 4-10 ° C. Data collection included measuring sugar and carbohydrate levels during storage.The sugar content of each sample was measured in three replications using a liquid refractometer available at Mohaghegh Ardabili University. To do this, first some water was taken from the samples and after pouring into the microtube, it was placed in a refrigerated centrifuge, and after rotating at a speed of 1800 rpm for 2 minutes, the impurities were deposited and the pure liquid was separated. After reaching ambient temperature, the liquid was placed on a refractometer and its sugar level was read in terms of brix.The amount of carbohydrates in the samples was extracted using the equipment available in the central laboratory of Mohaghegh Ardabili University. This operation was performed by the Skigel method. Glucose was used to prepare the standard curve. Consecutive dilution of glucose Preparation and color development at 490 nm for different concentrations of glucose were controlled and one ml of distilled water was used as a blank. This standard curve was used to calculate the total concentration of carbohydrates in the samples.For each sample, sampling was performed in three replications and the amount of absorption wavelength was obtained, then the amount of carbohydrates was calculated by placing the wavelength in Equation (1).

According to the analysis of variance table, the interaction effect of cultivar and storage period on potato sugar content was significant at 1% probability level. You can see the changes in the sugar content of potato cultivars along the storage valley in Figure 4. According to Figure 4, the highest amount of sugar is related to Sprite cultivar and the lowest amount is related to Jali cultivar. Meanwhile, the sugar content of Agria and Jeli cultivars was the same at the time of harvest. Also, after 1 month from the time of potato harvest, the sugar content of Agria and Marfona cultivars were equal and this equality continued until the end of storage period. The reason for the difference in sugar content between different cultivars is mainly related to the type of soil, fertilizer and toxin used. According to the chart, the amount of sugar in all 5 potato cultivars during the storage period first decreases and then with increasing storage period, the amount of sugar also increases.According to the analysis of variance table, the interaction of cultivar and storage period on the amount of potato carbohydrates was also significant at the level of 1% probability. Carbohydrate variations of potato cultivars along the storage valley are shown in Figure 5. According to Figure 5, the highest amount of carbohydrates is related to Sante cultivar and the lowest is related to Marfona cultivar. Also, at the end of the storage period, Marfona and Agria cultivars had the same amount of carbohydrates. As you can see, the amount of potato carbohydrates has decreased over time and with increasing storage time. Among these cultivars, the carbohydrate content of Marfona and Agria cultivars was higher than other cultivars. Also, carbohydrate changes

Potato is an important vegetable that grows all over the world and is considered as an important product in developing and developed countries for human diet as a source of carbohydrates, proteins, and vitamins. This product is native to South America and its origin is from Peru, and 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. Meanwhile, high-performance artificial neural network can be used to classify cultivars. Artificial neural network can classify and detect cultivars, is flexible and is used in most agricultural products. Azizi conducted a study on 120 potatoes in 10 different cultivars using a visual and image processing machine with a MATLAB R2012 software toolbox to detect texture, shape parameters and potato cultivars. First, potato cultivars were classified using LDA method, which obtained 66.7% accuracy. This method also erred in distinguishing the two cultivars Agria and Savalan and also classified the two cultivars Fontane and Satina in other classes. They also used artificial neural networks to classify potato cultivars, in which the network was 82.41% accurate with one hidden layer and 100% accurate with two hidden layers. In this study, it was found that different types of potatoes can be identified and identified with a very high level of accuracy using the three color characteristics, textural and morphological features extracted by the visual machine and the use of a non-linear classifier artificial neural network. Categorized.By determining and examining the existing relations between the force and the deformation of agricultural products up to the point of surrender, the range of forces harmful to fruit can be determined so that harvesting and transportation machines are designed in such a way that the forces from them do not exceed this range. On the other hand, one of the ways to determine the degree of ripeness of the fruit is to touch and press it with the thumb, which is an experimental way and depends on the skill of the person touching. The mechanical penetration test of the fruit can be an indicator to check the ripeness of the fruit by quantifying this diagnosis and using this diagnosis to determine the optimal harvest time.

First, 5 different potato cultivars were prepared from Ardabil Agricultural Research Center and kept at a temperature of 4-10 ° C. One day later, 21 samples of each potato cultivar were prepared using a cutting cylinder and then data were collected. To determine the toughness of the samples, the Centam device available in Mohaghegh Ardabili University was used. Each potato cultivar was subjected to compressive force at three levels of loading speed of 10, 40 and 70 mm / min with 7 repetitions. Then the amount of toughness was calculated according to Equation (1). Then linear diagnostic analysis (LDA) and artificial neural networks (ANN) were used to classify potato cultivars. LDA is a supervised method used to find the most distinctive special vectors, maximizing the ratio of 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, the logarithmic sigmoid transfer function and Lunberg-Marquardt learning method were used (Figure 4), and the error value 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. All of the calculations and matrix classification were performed using MATLAB R2018a and X 10.4 Unscrambler software.Toughness in 5 different potato cultivars was obtained using Centam machine and Equation 1. The values obtained for the toughness of 5 potato cultivars were analyzed using Mstatc software. The results of analysis of variance were significant for the toughness of 5 different potato cultivars at the level of 1% and its coefficient of variation was 2.28. LDA and ANN methods were used to detect potato cultivars based on the values calculated for toughness. Detection results of cultivars using LDA were equal to 70.48% (Figure 6). Also, the accuracy of ANN method according to the perturbation matrix was equal to 72.4% (Figure 7).

In this study, the amount of toughness for 5 different potato cultivars was calculated using Centam machine available in Mohaghegh Ardabili University with the help of Equation 1. Chemometrics methods including LDA and ANN were used for qualitative and quantitative analysis of data to identify and classify potato cultivars. Thus, LDA and ANN were able to identify and accurately classify different potato cultivars with an accuracy of over 70%. The obtained toughness has the ability to be used as a method to distinguish different potato cultivars. The use of this method in identifying potato cultivars will be very useful for factories such as chips factory and processing units, and it is also expected that similar methods related to mechanical properties such as crispness and stiffness with the help of chemometrics methods to optimize production and The processing of agricultural products should be used in the food industry, which has led to more customer friendliness and, in addition, can reduce agricultural waste.

Presence of foreign materials with the product is one of the important problems of wheat production. The economic value of the produced wheat and the degree of purity of the produced seeds increases with the separation of wheat mass impurities. Hence, in this research, a gravity separator table was used to remove impurities from wheat bulk. The machine has adjusting five parameters of air velocity, frequency of oscillation, amplitude of oscillation, longitudinal slope and latitudinal slope of the table. The effect of these parameters was studied to achieve maximum impurity separation from wheat bulk. Statistical analysis was performed in two factorial experiments based on completely randomized design. In the first experiment, the effects of three parameters of longitudinal slope, latitudinal slope and frequency of oscillation of the table were investigated and in the second experiment the effect of two other parameters was investigated. Also, using dimensional analysis, a dimensionless number parameter was obtained which was effective in evaluating the effect and reducing the number of parameters. The results showed that the maximum separation of impurities from wheat bulk was 87.03% at longitudinal slope of 2.5 °, latitudinal slope of 1.5 °, frequency of oscillation of 395 cycles per minute, amplitude of oscillation of 5 mm and air velocity of 6.75 m/s,. Also, with increasing longitudinal slope from 2.5 ° to 4.5 °, latitudinal slope from 0.75 °  to 2.5 ° (in most cases) and the amplitude of oscillation of the table from 5 to 7 mm, the separation of impurities was reduced and with increasing the air velocity from 5.25 to 6.75 m/s the separation of impurities was increased.

Optimal design for sourcing, grading and other post-harvest operations requires the availability of appropriate information about their physical properties. Also, achieving a high quality product requires the fight against weeds and impurities in the product. Therefore, in this research, some physical properties of wheat and barley were measured. A gravity separator was also used to separate the barley in the wheat mass. This machine has five customizable parameters includes air velocity, frequency of oscillation, amplitude of oscillation, longitudinal slope and latitudinal slope of the table. The effect of these parameters was investigated in the form of two factorial experiments in a completely randomized design to achieve maximum separation of barley from wheat mass. The results of experiments showed that in the conditions of air velocity of 6.75 m / s, the oscillation amplitudes of 5 mm, the longitudinal slope of 2.5 degree, the latitudinal slope 0.75 degree and the frequency of oscillation of 395 cycl / min, the maximum separation of barley from wheat mass to 20.6 % was achieved. In most cases, with increasing latitudinal slope from 0.75 degree to 2.52 degree , the longitudinal slope of the table from 2.5 degree to 4.5 degree and with the increase of the frequency up to 435 cycl / min, the separation of barley from the mass of wheat decreases. Also, the minimum amount of barley separation from wheat mass in condition of oscillation frequency of 455 cycl / min, l o n g i t u d i n a l slope of 4.5 degree, latitud in a l lope of 1.5 degree, oscillation a mplitude of 5 mm and air velocity of 6 m/s was obtained 9.417 %.

This paper presents the thin layer drying behavior of Nepeta L. by a solar hybrid dryer. Experiments were carried out at the air temperatures of 40ºC, 50ºC, 60ºC, 70ºC and air velocity of 1m/s, 1.5 and 2 m/s. 8 different thin layer drying models were fitted to experimental data. The high values of coefficient of determination and the low values of reduced chi-square and root mean square error indicated that the Aghbashlo et al. model could satisfactorily describe the drying curve of Nepeta L.. According to resultsThe drying rate increased with an increase in the drying air temperature and drying air velocity. Also the effect of the air velocity on the drying time at a low temperature is greater than that at a high temperature. According to the results analysis of variance the effect of air temperaturedrying and air drying rate at probability level 1 percent and the Interactive effects of temperature and air velocity, was not significant, and the highest amount of essential oil related to a temperature of 40 ° C and air velocity of 1.5 m/s was achieved with approximately 0.54 cc and increasing the temperature from 40 ° C to 70 ° C Significantly decreased the amount of essential oil.

Machine vision technology can be used to detect the location of weeds around the main crop in a field as the machine moves, in order to decrease the losses of using herbicides. The purpose of this research is to determine the accuracy of image processing method in discriminating weeds from potato crop according to color features. Therefore, by performing a research which conducted in university of Mohaghegh Ardabili research field, three factors including: environmental light condition, type of weed (Shalambig, Pichack and Wheat), and level of crop growth, were investigated on discrimination accuracy. The results of this research were showed that there are no significant differences between two types of environmental light conditions. However, the main and interactive effects of two factors of type of weeds and level of crop growth was significant on discriminating system performance. According to this study, the first stage of crop growth is the best time for the visual tests (middle of the June) and among the three types of popular weed in the region, it is possible to discriminate the wheat from potato leaves with a reasonable accuracy according to RGB color model. By this method, it is possible to determine the location of weeds around the main crop by maximum accuracy of 95% dependent to different condition of treatments.

IntroductionLentil (Lens culinaris Medik) is an important and highly nutritious crop belonging to the family of legumes. Lentil is cultivated worldwide but competition with weeds is a problem affecting production and can reduce performance by more than 80%. Euphorbia helioscopia weed is a major weed in lentil cultivation. In the first place, it is necessary to have a thorough and comprehensive knowledge about the characteristics of the lentils and the accompanying impurities to mechanize the process of automatizing activities related to lentils such as warehousing, sorting (grading), packaging, transportation and other activities. On the other hand, physical and aerodynamic properties of agricultural products were always regarded as significant because those are the basis for designing and construction of agricultural and machineries, transport equipment, grading and processing of agricultural products. Designing agricultural machineries was impossible without regarding these parameters, or it would lead to weak results. The study of physical properties and separation of Euphorbia helioscopia weed from lentil by a gravity separator is of extreme importance. Since no study has been done to date, in this work physical properties of lentil and Euphorbia helioscopia weed are investigated. Moroever, the effects of different parameters of a gravity separator and their influence on the separation of Euphorbia helioscopia weed from lentil seeds are evaluated.
Materials & MethodsIn the present research, lentil samples were taken from farms in Ardebil province (Bileh-Savar cultivar) and transferred to the seed technology laboratory at the University of Tehran. In addition, some physical properties of lentil and weed of Euphorbia helioscopia including mass of 1000 seeds, volume, true and bulk densities and porosity and static coefficient of friction (two types of friction surface: galvanized iron sheet and particle board) were measured. In addition, in this research, a gravity separator apparatus was used for separating weed of Euphorbia helioscopia from lentils. A Laboratory Gravity Separator Type LA-K (Westrup A/S Denmark) was used to separate Euphorbia helioscopia weed from lentil seeds. Influence of parameters of machine table (longitudinal and latitudinal slopes, oscillation frequency and amplitude and velocity of air) have been studied for obtaining of maximum separation of weed of Euphorbia helioscopia from lentils. Data analysis and comparison of means were done by using MSTAT-C software and Duncan's Multiple Range Test.
Results & DiscussionThe obtained results show the main effects oscillation of frequency, latitudinal slope, and longitudinal slope, the mutual binary effect of latitudinal and longitudinal slope, the mutual binary effect of the latitudinal slope and the frequency of oscillation were significant at a 1% and the mutual binary effect of the longitudinal slope and the frequency of oscillation significant at a 5%. However, the mutual triple effect of oscillation frequency, longitudinal slope, and latitudinal slope was not significant. Furthermore, results showed that increase of latitudinal slope of table and increase of longitudinal slope from 1.25 to 2 degrees increased separation of weed of Euphorbia helioscopia from lentils. The results of the experiment showed that the separation of Euphorbia helioscopia weed from lentils get to maximum 14.2 percent .After determining most suitable amplitude and velocity of air, using data from the longitudinal slope, latitudinal slope and dimensionless number was used to calculate mathematical relations of separation percentage of Euphorbia helioscopia weed from lentil clumps using Datafit software.
Conclusion1. Physical properties obtained for lentil were as weight of 1000 seeds (57.03 g), true density (1.214 g cm-3), bulk density (0.782 g cm-3), coefficient of static friction (galvanized iron sheet (0.394 degrees) and particle board (0.37 degrees)), porosity (35.59 %), and the Euphorbia helioscopia weed including weight of 1000 seeds (5.69 g), true density (0.857 g cm-3), bulk density (0.538 g cm-3), coefficient of static friction (galvanized iron sheet (0.32 degrees) and particle board (0.40 degrees)), porosity (37.2 %).
2. Increased latitudinal slope of the table from 0.5° to 1° and longitudinal slope of the table from 1.25° to 2° result in increased separation percentage of wild oat weed from lentil seeds.
3. The results demonstrated that at settings of longitudinal slope of 2°, latitudinal slope 1°, and frequency of oscillation 400 cycles min-1, the maximum separation was 14.2%. In this case, the output lentil seeds contained the least amount of wild oat weeds.

This study was carried out to determine some mechanical properties of barley seed including Deformation, Rapture Force, Rapture Energy and toughness as a function of moisture content (10.5%, 12.5%, 14.5% , 16.5%, db), loading rate (5, 15, 25, 35 mm.min-1) and grain size (small, medium, large). Tests were performed at 7 replications by the means of a factorial base plot experiment. The results showed significant effect of moisture content on all the properties (p>0.01).Also there is significant effect of grain size on rapture force, rapture energy and deformation and significant effect of loading rate on deformation at 1% level of probability. The interaction effect of moisture content- loading rate was significant on deformation and toughness (p>0.01) and on rapture force and rapture energy (p>0.05). The more percentage of moisture content, the more mean amount all the measure properties of barley seed. The mean amounts of rapture force and deformation increased linearly as the moisture content increased.

Today, development of intelligent systems in different operations of food and agricultural processing is one of the prime requests in food industry. In this research, a series of machine vision tests, were carried out to investigate the factors influence on walnut kernel discriminant system based on dimension and color (standard method). The research had been focused on discriminating the kernels in 3 quality groups of halves, pieces and small pieces and 3 color groups of Very Light, Light and Light Amber in a common variety. The prediction features were the major and minor diameters of kernel, and color features of Red, Green, Blue, Hue, Saturation, Value, L, a and b channels from three color models and the direction of lighting. In comparison of two lighting methods, back lighting showed more accuracy (94.3%) than the top lighting (91%), however, the difference was not significant. In addition, it was possible to extract the dimension and color data in image capturing in top lighting. The results showed that the accuracy and speed of discrimination based on dimensions was more than that the color identifying. According to the results, it was possible to identify halves with 98.1% accuracy in a mean of 0.31 s. While, the maximum rate of discrimination in color identification was equal to 76.2% in 1.91 s for detecting Light kernels. Due to overlapping of data of different color models, and according to the results of linear discriminant analysis, it is possible to identify Very Light kernels, only by taking account of V parameter (from HSV model) in less than 0.6 seconds with accuracy of 81%. In comparing different color models, the HSV and Lab have the maximum and minimum accuracy in color discriminating of walnut kernels, respectively. The results showed that it is possible to discriminate walnut kernels based on color and dimensional features according to standard method, in less than 2 seconds (for each kernel), under top lighting condition. This information may be used in design and development of walnut grading machines in food industries.