In this research which is based on synoptic Climatology procedures including synoptic maps interpretation and statistical analysis, the air masses influencing Iran, in three stations: Tehran, Shiraz and Kermanshah based on the temperature and dew point, daily upper air data surface, 850,700,500 hecopascal for the years 1981-90 were studied.Also in this research in order to identify the air masses, based on the conservation of air mass properties law, the following parameters: potential temperature, wet bulb potential temperature and mixing ration were selected, and Calculated. statistical analyses such as principal component and cluster analysis were done. Given all the information above the results are as follow:For Tehran station 4 Shiraz 3 and Kermanshah 3 factors were extracted, and based on these factors hierarchical cluster analysis for aggregate days was applied and then mean analysis for all data was done. With comparing the average value of all parameters in each groups and comparing them with the data of Aswan, Ashkhabad and Mediterranean air mass properties, these air masses tmA.,cA,cP,mT,cT and med. were determined.And then based on the data of potential temperature and mixing ratio of 500 hectopascal index days was extracted and tephigrams for these days were prepared and their synoptic maps were studied.

Separating clods from potato tubers is one of the most challenging jobs in a potato harvester. In this study, an acoustic-based intelligent system was developed for high speed discriminating between potato tubers and clods. About 500kg mixture of potato tubers and clods were put on the belt conveyer and impacted on a steel plate in four different velocities. The resulting acoustic signals were recorded, processed and potential features were extracted from the analysis of sound signals in both time and frequency domains. A multilayer perceptron (MLP) neural network with a back propagation algorithm was used for pattern recognition. Altogether, 17 potential discriminating features were selected and fed as input vector to artificial neural network (ANN) models. Optimal network was selected based on mean square error, correct detection rate and correlation coefficient. At the velocity of 1 ms-1 of the belt, detection accuracy of the presented system was about 97.3% and 97.6% for potato and clod, respectively. Detection accuracy decreased by increasing belt velocity. A potato harvester by using this system can perform at capacity of 20 ton hr-1 by accuracy of about 97%.

Harvesting practices of potato is one of the most difficult, troublesome and sensitive stages. Potatoes enter the machine with huge amounts of clod and stones: In the meantime separation of potato some stone and clod, which are the same size as of that the potatoes, shall be confide red a major problem. Many methods of study by investigators are based on physical properties of potato, stone and clod. In the present research with regards to the farm conditions and economic situation of the Iranian farmers, the beveled conveyor method is recommended. In this method, primarily a schematic design of the separating section of the stones and clod and the crop was produced and important parameters such as the surface angle, conveyor-belt motion speed and type of the conveyor, which play a key role in the separation stage, were studied. The test was carried out on the factorial basis and complete random block, using 3 angles factors in 5 levels (5, 10, 15.17.5 and 20 degrees) and the speed on 5 levels (100, 200, 300, 400 and 600 RPM) and the belt type with 2 surfaces (soft and rough) repeated 5 times. The SPSS software was used to analyze data. The result revealed that the ideal combination of the angle, speed and belt type for acquiring the highest separation rate and the least amount of mix clod, stones of the separated potatoes are the order, and the crop would be the angle 15 degrees, the speed of 300 RPM, with the use of soft belt, and the angle of 17.5 degrees and the speed of 200 RPM is recommended for a rough belt.

Introduction: Melon (Cucumis melo L. ) is one of the most important vegetables in Cucurbitaceae family and one of the most important economic crops in the Torbat-e Jam city (Longitude: 60 ̊ 48', latitude: 35 ̊ 31', altitude: 928 m). Growth and yield of agricultural crops are affected by biotic and abiotic environmental stresses. Cold stress can be one of the most important environmental factors reducing crops yield. Cold acclimation in plant is a complex process involving many morphological, physiological and biochemical changes, including a significant reduction in tissue hydration during cold hardening. Melatonin (MEL, N-acetyl-5-methoxytryptamine) is a conserved substance, which has been discovered in all living organisms, from bacteria to mammals. MEL regulates the growth of root, shoot, and explant, activates seed germination and rhizogenesis, and delays leaf senescence. In addition, the most frequently mentioned functions of MEL are related to various abiotic stresses such as drought, radiation, low/high temperature, heavy metals, and salinity stresses. Materials and Methods: In order to investigate the effect of PEG priming and melatonin on cold stress resistance of melon seedlings, a factorial experiment was conducted in a completely randomized design with three replications in Torbat-e-Jam University. In this experiment polyethylene glycol 6000 was used to produce drought stress at three levels (0, 0. 18 and 0. 58 MPa) and melatonin was used at two levels (0 and 200 μ mol). When melon seedlings were at 4 leaf stage, the amount of polyethylene glycol was added to the irrigation solution for a week and to prevent drought stress, drought stress was increased for 3 days and increased one third of the required concentration daily. Recovery was performed for three days after drought stress and during this period melatonin was added to the irrigation solution at the required concentration. Seedlings were then exposed to cold stress (T0: non-stress and T1: cold conditions). Control plants were kept in greenhouse conditions. Results and Discussion: Comparison of the mean results showed that there was an increasing trend in proline production by increasing drought stress. The highest amount of proline (0. 80 µ mol g-1 FW) was recorded at the highest level of drought pretreatment with no melatonin and without cold stress (D2M0T0), and then a decreasing trend in proline production was observed. The results showed that melatonin significantly increased leaf relative water content compared to the control. Interaction effects of drought pretreatment and temperature showed that there was a trend of decrease in relative water content by increasing drought pretreatment. Ghanbari and Sayyari (8) reported that drought pretreatment stress maintains relative water content of tomato seedlings under cold stress conditions. Drought pretreatment significantly reduced the amount of chlorophyll a and total chlorophyll. The results showed that the highest levels of drought pretreatment stress (D2) and melatonin (M1) maintained chlorophyll a under cold stress conditions. Results showed that the amount of chlorophyll b was decreased by drought pretreatment stress, but it increased by melatonin application in all compounds. Based on the results, it was found that only simple effects of treatments at 1% of probability level had significant effects on soluble sugars content. Comparison of the mean simple effects of drought pretreatment showed that under drought stress the amount of soluble sugars increased significantly and the highest sugar content was recorded at the highest drought stress level. The amount of soluble sugars in plants under cold stress also increased significantly. Melatonin application also significantly increased the amount of soluble sugars. Kabiri et al. (19) reported that the use of melatonin increased soluble sugars in Moldavian balm seedlings under osmotic stress which is similar to this study results. It was found that melatonin significantly increased phenolic compounds under stress conditions and significantly decreased electrolyte leakage.

One of the steps in potato harvesting is to separate potato from clod and stone that cannot be entirely done by the harvester machines based on the friction and vibration and must be completed by labors. In the present study, image processing technique was used to detect the potato tubers and separate them from clod and stone. Four hundred potato, 100 stone and 100 clod specimens were randomly selected. After selecting the optimum imaging condition, the image of all specimens was individually acquired. An image processing algorithm was designed for preprocessing and extracting different color and texture features in MATLAB. From the features extracted, nine useful features were selected for classification. To classify the specimens, the support vector machine was used by considering two strategies: 3-way (potato, clod and stone) and 2-way (potato and non-potato). The correct classification rate of 3-way and 2-way strategies was obtained 98. 67 and 99 %, respectively with the same mean square error (0. 0017). According to this result, a machine vision system can be implemented for separating potato from clod and stone.

Hypothesis: Among available bioplastics, polylactide exhibits superior properties, including high modulus, good processability, and compostability. However, some disadvantages, such as brittleness and low thermal resistance limit its application in some fields. In order to overcome the limitations of polylactide, it is usually modified by addition of nanoparticles or by blending with other thermoplastics. Methods: Polylactide/polyethylene (PLA/PE)-based nanocomposites were prepared using 4 phr commercially modified montmorillonite (Cloisite 30B or Cloisite 20A) and polyethylene-g-maleic anhydride compatibilizer (PE-g-MA) by melt blending technique. The structure and morphology development and also the cold crystallization kinetics of the samples were investigated using X-ray diffraction (XRD), field emission scanning electron microscopy (FE-SEM), differential scanning calorimetry (DSC) techniques and melt linear and non-linear viscoelastic measurements. Findings: The XRD results showed that polymer intercalation into the clay galleries in PLA/PE/Cloisite 30B was higher than that in PLA/PE/Cloisite 20A. The rheological results along with the calculated data of wetting parameter showed that Cloisite 20A in blend nanocomposite could reduce the droplet size through three different mechanisms a: localization of the organoclay in the interface b: increasing the viscosity of PLA matrix and c: decreasing the extent of coalescence process. The FE-SEM micrographs showed that the nodular morphology of PLA/PE/Cloisite 30B changed to a non-nodular morphology in PLA/PE/Cloisite20A. The melt linear and non-linear viscoelastic measurements showed that a stronger 3D-network structure was formed in PLA/PE/Cloisite 20A compared to that in PLA/PE/Cloisite 30B. It was implied that the crystallization rate followed the Avrami equation with the exponent n of around 2. The results also showed that the addition of compatibilizer (PE-g-MA) into PLA/PE/Cloisite 20A or PLA/PE/Cloisite30B decreased modified crystallization rate constant (Zc), because the cold crystallization rates of compatibilized blend nanocomposites were lower than those of uncompatibilized blend nanocomposites and the role of compatibilizer in the transfer of partial organoclays from PLA matrix into the droplets.

Introduction: Mulch tillage system is an intermediate system which covers some of disadvantages of no tillage and conventional tillage systems. In farms in which tillage is done with a chisel plow, runoff and soil erosion have a less important relation to moldboard and disk plow and naturally absorption of rainfall will be developed. Thus, the mulch tillage system is an appropriate alternative to conventional tillage and no tillage (Backingham and Pauli, 1993). The unwanted vibration in machinery and industry mainly processes most harmful factors, for example: bearing wear ,cracking and loosening joints. ...

Introduction: Stereo vision is an approach to 3D information from multiple 2D views of a scene. The 3D information can be extracted from a pair image, as known stereo pair by estimating the relative depth of points in the scene. Soil aggregate size distribution is one of the most important issues in the agriculture sector which highly affects energy consumed for preparing the field before planting. Mean weight diameter of clods is a standard metric for determining clod (big aggregates) size. Conventional methods are based on sieving soil samples to calculate the MWD. However, they are faced with several challenges in larger scales and practical applications. Furthermore, due to inherent limitations of soil environment and also being a tedious work, traditional methods would beuse to estimate the metric higher or lower than actual value. As new methods, researchers are using computer vision techniques as virtual sieve so that the size of clods can be determined via processing digital images which have been taken from soil surface. Although, image-based methods have solved many of previous problems, their accuracy is not so high due to the complexity of soil environment and overlapping colds, and needs to be improved. In order to overcome the mentioned challenges, in the current study stereo vision method was developed so that it is possible to extract the third dimension information as height of clods which helps us to categorize clods into their own class. Materials and Methods: In this study, the W3-Fujifilm stereo camera equipped with two 10-megapixel CCD sensors for both left and right lenses, and baseline spacing of 7. 5 cm was used. The distance between the camera lens and the ground was also set to 60 cm. In order to get three components of soil clods including (x, y, z), point cloud was investigated. For this, local features were extracted using a SIFT feature detector. The SIFT algorithm is robust against scale, rotation and illumination changes, so that these specifications have made it as a strong tool in the field of stereo vision. Then, the extracted features (keypoints) were matched between two stereo pair images by means of Brute Force algorithm and the location of all corresponding points were determined and point cloud was obtained. At the final stage, three features including length, width and height of all six classes of soil clods were entered into a linear classifier entitled discriminant analysis. This classifier as a linear separator classified these six classes based on appropriate functions in a 5 dimensional space. Results and Discussion: Results of classification model showed that the height (thickness) of clods have more distinguishing different soil clods. The reason for this refers to the event of overlapping, because most of clods were touched each other after sieving. Consequently, the length and width of clods had not significant effect in soil aggregates classification. In order to analysis the result of soil aggregate classification, confusion matrix was calculated and the overall classification accuracy was achieved 83. 7%. The lowest and highest accuracy were obtained for class 1 (the littlest class) and class 6 (the biggest class), respectively due to their low and high height from the soil surface. Conclusions: In this research, the basic geometrical features including length, width and height were extracted from stereo pair digital images via stereo vision techniques to classify six classes of soil clods. This aim was reached by 3-D reconstruction of image data, so that the height of each image as the third component of (x, y, z) was obtained as well as the length and width. The results of classification indicated that the stereo vision technique had the satisfactory performance in determining the aggregate size distribution which is one of the most important indices for tilled soil quality.

Reduction of tillage operations in conventional systems and its replacement with a reduced tillage and conservation systems is recommended in order to sustainable production in agriculture and preservation of soil and water resources. In this research, the effect of applying three types of conventional tillage machines with different geometry that have less rigidity of soil tillage than conventional tillage machines (Chisel packer, combined tiller and offset disc) on the performance parameters of tillage including Specific draft, specific tillage energy, and weighted average diameter of clods, roughness of soil surface and percent of crop residue on the soil surface. Tillage depth is fixed in 20 cm, forward speed was in three levels (2. 5, 4. 5 and 6 km/h) and tillage machinery type was in three levels. Double Split plot in Randomized Complete Block Design used in the study in which tillage depth (main factor), forward speed (secondary factor) and the tiller type (sub-secondary factor) were the independent variables. The results showed that the amount of residues remaining on the soil surface after soil tillage was negatively correlated with not only special draft but also with other indices (weighted average diameter of clods and roughness of soil surface), and crop residue after soil tillage was applied as a criterion for comparing soil tillage machinery. The results of the comparison of the ranking of treatments based on two methods (amount of crop residues after soil tillage and the Component Analysis method) showed that the combined tiller and the offset disc at low velocity and Chisel packer at high velocity is recommended for conservation systems of tillage.