Information and knowledge about the evolution of properties and quality attributes, that are so important for understanding post harvest behavior, is very limited due to the use of completely different frameworks in pre harvest and post harvest quality analyses. This study was conducted to determine firmness of two variety of pomegranate that are cultivated in Iran. The firmness was determined at top, middle and bottom positions of fruit by an Instron Universal Testing Machine with 5 mm, 6 mm and 8 mm diameter probes. The mechanical properties of bottom section were more than top and middle sections. Rupture energy and firmness measured with 8 mm probe was found to decrease during storage time. The mechanical properties of two varieties were found approximately the same. Effect of storage time and variety on mechanical properties was found insignificant (P < 0.05).

In this study mass and surface area of pomegranate fruit were predicted with using different physical characteristics in linear models classified as follows: (1) Single or multiple variable regressions of pomegranate dimensional characteristics, (2) Single or multiple variable regressions of pomegranate projected areas, (3) Single regression of pomegranate mass based on measured (actual) volume and volumes of shapes assumed (oblate spheroid and ellipsoid). The results showed that in the first classification of single variable mass modeling of pomegranate based on dimension, the highest determining coefficient was obtained as R 2 =0.95 based on geometric mean diameter M = -528 + 10.7 Dg while that was as R 2 =0.96 for multiple variable models. Also, there was a very good relationship between mass and measured volume of pomegranates for the two varieties with R 2 as 0.97 (highest R 2 value among all the models). At least, the models which predict mass of pomegranates based on estimated volume, the shape of pomegranates considered as spheroid and elliptical were found to be the most appropriate models.

In Iran, there is a high diversity of salvia species and accessions which includes 70 species that 40% of them are endemic. The objective of this investigation was to find a practical treatment for germination of salvia species, particularly, endemics and to find conservation issues and appropriate approaches. We observed that there was a huge diversity in color (RGB channels), seed area and 1000-seed weights among the population in this study, including 60 accessions (23 species) that thirteen of them (five species) are endemic of Iran. These accessions were soaked in four gibberllic acid (GA3) levels (0, 100, 150 and 200 mg/L). The germination rate and percentage of 62% of accessions were, extremely, increased in response to the GA3 treatment; nonetheless, some accessions did not germinate at all which indicates that there are demands for more efforts to conserve these accessions. Germination percentage of endemic species was significantly lower than non-endemic ones, indicating a serious concern for their conservation. A significant correlation between the 1000-seed weights and area under germination percentage curve (AUGPC) was found that indicates seeds were evolved to have more storage to survive for a long time until germination.

Due to the high sorting speed required during inspection and classification in packing lines, most of the current automatic systems, based on machine vision, are used. Fruit industries are not excluded about this fact. In this paper a method is proposed for detection healthy bananas and defective one. Our algorithm has 4 steps. First, we eliminated background using segmentation methods such as FCM, HCM, Kmeans. Then we extracted the boundaries of a sample banana using edge detection approach. After that, feature from surface of a sample was extracted. Finally, by using a neural network, healthy bananas and defective one was detected.