Nitrogen use efficiency and root morphology of different potato (Solanum tuberosum L.) cultivars were compared in two field and greenhouse trials and root morphology response to nitrogen concentration was investigated. In field experiment, treatments consisted of four nitrogen application rates (0, 90, 180 and 270 kg ha-1) and six potato cultivars (Fontane, Kuras, Agria, Miriam, Cosmos and Picasso). Greenhouse experiment was conducted at sand culture method and effects of different nitrogen concentrations (100, 200 and 300 mg l-1) on root morphological characteristics were studied. Different nitrogen application rates affected total tuber yield and nitrogen efficiency of potato cultivars. Maximum tuber yield was obtained in 270 kg ha-1 N treatment. There was no significant difference between tuber yields in 180 and 270 kg ha-1 N treatments. Nitrogen use efficiency and agronomic efficiency of potato cultivars decreased with increasing nitrogen application. Cosmos, Kuras and Picasso had the highest yield, nitrogen use efficiency and agronomic efficiency among potato cultivars. In greenhouse trial, different nitrogen concentrations had significant effects on all root morphological characteristics, except root average diameter. In all potato cultivars, root volume, root fresh weight, root dry weight, total root area, total root length and shoot dry weight increased and root/shoot ratio and specific root weight decreased with increasing nitrogen concentration. Potato cultivars differed in root morphological traits and cultivars with higher nitrogen efficiency had larger and stronger root system in all nitrogen concentrations.

Terminal heat sterss considered as one of the major challenges of winter production of potato in warm areas of the country including the south of Kerman provience. To determine the suitable strategy for mitigating terminal heat stress injury, a split-split plot experiment based on randomized complete block design with three replications was conducted in South Kerman Agricultural Research and Education Center (Jiroft), Iran, for 2 years (2013 and 2014). The main factor was heat stress: normal (on time planting; Dec. 31) and terminal heat stress (late planting; Feb. 4) and three potato cultivars; Satina, Sante and Milva and four calcium applications; (without calcium nitrate application, foliar application of 2500 mg.l-1 calcium nitrate in two and three stages and soil application of 75 kg.ha-1 calcium nitrate in two stages) were in sub and sub-sub plots, respectively. Results showed that heat stress reduced marketable yield and the weight and number of marketable tubers per plant. Sante and Satina cultivars produced the highest marketable tuber yield in normal condition (47.15 and 44.9 ton.ha-1, respectively), but under heat stress conditions, the difference between marketable tuber yield of those cultivars was not significant. The application of calcium nitrate improved marketable, non-marketable and total tuber yield and its components in potato cultivars and soil application of 75 kg.ha-1 of calcium nitrate was significantly suprior than the other treatments. The interaction effect of terminal heat stress and calcium nitrate applications on marketable and total tuber yield, mean weight and number of marketable and non-marketable tuber was significant. Soil application of calcium nitrate (75 kg.ha-1) increased marketable tuber yield of potato cultivars under normal and terminal heat stress conditions (10.5 and 24.5%, respectively) that revealed the mitigating effect of calcium nitrate on terminal heat stress in potato cultivars. It concluded that soil application of calcium nitrate seems to be an appropriate method to reduce the adverse effects of terminal heat stress in winter potato production.

For investigation of potato starch properties، six potato cultivars were grown in Agricultural Research Field of Gorgan in 1381-2. Starches were extracted from these tubers and their physico-chemical properties were investigated in comparison with wheat starch. Dry matter and starch content and starch yield of potatoes were found to vary from 17. 2–22، 10. 6–14. 6 and 9. 2-12. 6 % (on fresh weight basis) and the maximum and minimum of these values related to Concord and Deraga varieties، respectively. No significant difference was found at reducing sugars among fresh potatoes.، Phosphorus content، viscosity and mean particle size were different among cultivars and were higher wheat starch، but amylose and protein content had no significant difference in potato starches whereas protein content of wheat starch was higher than potato starches.

The identification of potato cultivars is a recurrent objective of potato research. The research is prompted by the increasing number of potato cultivars and the importance of seed purity. In developing a reliable method for identification of the imported potato cultivars and determining their genetic relationship, the capacity of 10 polymorphic simple sequence repeat markers (SSRs) was evaluated for the analysis of 28 commercial cultivars of potato. The number of alleles detected at different loci ranged from 3 to 10 alleles with a total of 57 for all loci and a mean of 5.7 alleles per locus. In the 28 potato cultivars analyzed, the number of heterozygous genotypes per locus varied between 6 to 28 with an average number of heterozygous genotypes per locus of 18, considering the 10 loci studied. Based on the resulting dendrogram of jacquard's similarity coefficient and UPGMA analysis, the potato cultivars were placed in two major groups. However, the results from similarity coefficient confirmed the close phylogenetic relationships among members in each cluster. The dendrogram derived from SSRs data clustered together Kenebek, Florida and Atlantic which are known as American potato cultivars, but Stanbuli, an old cultivar in Iran, was placed in concert with European cultivars. This finding might be an indication that this cultivar along with other unidentified cultivars, growing in local fields, has been introduced from European countries to Iran. The results obtained illustrate the appropriate utility of SSRs to assess genetic relationships of potato cultivars and develop a PCR- based tool for evaluation of potato seed purity.

To evaluate the influence of nitrogen rate and split application method on tuber yield and other quantitative characteristics of different potato cultivars, a 2-yr experiment was conducted at Khorasan Razavi Agricultural and Natural Resources Research Center. Experimental treatments consisted of four nitrogen rates (0, 90, 180 and 270 kg N ha-1) with two split applications (emergence and hilling stages) or three split applications (planting, emergence and hilling stages) and six potato cultivars (Fontane, Kuras, Agria, Miriam, Cosmos and Picasso) that were arranged in a randomized complete block split-split plot design. Shoot dry weight, average stem length, tuber number per plant, total tuber yield, marketable tuber yield

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.

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 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.

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.