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  • 1 Institute of Viticulture and Enology, Hungarian University of Agriculture and Life Sciences, Villányi str. 29-43, 1118Budapest, Hungary
  • | 2 Kun Szőlő Nursery and Table Grape, Kővágótöttös, Hungary
Open access

Abstract

Quantitative evaluation of the horticultural crops has high importance to identify cultivars, describe the effect of the growing location and cultivation technology or define consumer's preference regarding the size and shape. Fruit traits of the grapevine (Vitis vinifera L.) are mainly described by the bunch and berry morphology notably bunch and berry size, weight and shape. Ampelographers particularly evaluate the berry based on the seed number as it influences size and consumers' decision. In this study, berry morphological traits of the grapevine cultivar ‘Italia’ was investigated based on digital image analysis. Samples were collected from two vineyards in Hungary with different ecological and cultivation circumstances. Altogether 12 traits were investigated: weight, seed number, size and shape attributes. Results showed that berry morphological traits – except from the shape attributes – are not differing between the two sampling locations. In accordance with previous studies, seed number – ranging from 0 to 4 – had noticeable effect on the size attributes.

Abstract

Quantitative evaluation of the horticultural crops has high importance to identify cultivars, describe the effect of the growing location and cultivation technology or define consumer's preference regarding the size and shape. Fruit traits of the grapevine (Vitis vinifera L.) are mainly described by the bunch and berry morphology notably bunch and berry size, weight and shape. Ampelographers particularly evaluate the berry based on the seed number as it influences size and consumers' decision. In this study, berry morphological traits of the grapevine cultivar ‘Italia’ was investigated based on digital image analysis. Samples were collected from two vineyards in Hungary with different ecological and cultivation circumstances. Altogether 12 traits were investigated: weight, seed number, size and shape attributes. Results showed that berry morphological traits – except from the shape attributes – are not differing between the two sampling locations. In accordance with previous studies, seed number – ranging from 0 to 4 – had noticeable effect on the size attributes.

Introduction

Vegetable and fruit species have numerous quality parameters, and consumers are mainly affected by the size, the typical colour and the shape (Predieri et al., 2004). Grapevine (Vitis vinifera L.) is one of the most important horticultural crops where appearance, for example bunch size and compactness, berry size, shape and colour highly influence the table grape consumers' decision. Description of the berry size already appeared in literature in the 16th century. Szikszai (1590) mentioned ‘uva spionia’ (‘nagyszemű szőlő’ = large-berried grape). Parkinson (1629) introduced ‘small blacke grape’, which, according to the illustration, referred to the berry size. Duhamel (1768) described many cultivars in his book: Traité des arbres fruitiers for example ‘Chasselas doré’, ‘Muscat blanc’ and ‘Cornichon blanc’. The colourful paintings showed divers berry shapes and sizes and in some cases, parthenocarpy-like berries are possible to identify in the figures, which is a widely investigated phenomenon in grapevine and causes divers berry sizes within bunches. Today both ampelographic literatures (Barbagallo et al., 2011) and official descriptors, as the descriptor list of the International Organisation of Vine and Wine (OIV) detail berry size classes (OIV, 2009), which are measured for example with ruler (Frege, 1804), with calliper (Kircherer et al., 2013) or with digital image analysis (Roscher et al., 2014). There are several image analysis software dealing with mostly the size of different parts of horticultural crops. ‘Tomato Analyzer’ (TA) for example evaluates the perimeter and the area, the maximum width and height, the width at mid height and the height at mid width also. The program can evaluate different fruit shape indexes, the Fruit shape index external I, which is the ratio of the maximum height and maximum width, and Fruit shape index external II, being the ratio of the height mid width and width mid height. The Proximal fruit blockiness and Distal fruit blockiness show the ratio of the upper (proximal) and the lower (distal) thirds ratios to the width mid height. Furthermore, the asymmetry of the fruit can be described with the help of the software (Rodríguez et al., 2010).

The purpose of this study was to evaluate the berry morphological traits of the grapevine cultivar ‘Italia’ influenced by the seed number of the berries, and compare samples collected from two different vineyards.

Materials and methods

Samples of Vitis vinifera L. cv. ‘Italia’ were collected from the Institute for Viticulture and Oenology of the Hungarian University of Agriculture and Life Sciences (Kecskemét, Hungary) and from the Kun Szőlő Nursery and Table Grape Plantation (Kővágótöttös, Hungary). Ten bunches in full maturity were collected as it is recommended by the International Organisation of Vine and Wine (descriptor: OIV220) (OIV, 2009) and of each bunch 15–15 berries were randomly sampled from the middle third of the bunches. The chosen berries were numbered, and after that pedicels and receptacles were gently removed. Samples were individually weighed on an Ohaus Explorer Pro EP114C analytical balance (Ohause Corporation, Pine Brook, NJ USA), and then the berries were halved. Seeds of the halved berries were then counted and noted. Digitalization of one section per each berry was carried out with Epson V370 scanner (Seico Epson Corporation, Japan) at 200 dpi. TIFF images were then analysed with the ‘Tomato Analyser’ (TA) software according to the protocol reported by Brewer et al. (2006). In this study, 10 traits were evaluated with the ‘Tomato Analyzer’ as basic measurements (Perimeter, Area, Width Mid-height, Maximum Width, Height Mid-width, Maximum Height, Curved Height), fruit shape indexes (Fruit Shape Index External I, Fruit Shape Index External II, Curved Fruit Shape Index). Detailed description of the investigated traits are introduced in Hurtado et al. (2013). Statistical analysis of the data: summary statistics and ANOVA was carried out with the PAST (Hammer et al., 2001) software.

Results

Morphological characteristics of the grapevine cultivar ‘Italia’ in different locations: Results showed that in the case of the investigated size traits there were no significant differences of the values of the two locations. Seed numbers of the berries were similar (Table 1). Berries with one seed were the most frequent in Kecskemét and Kővágótöttös with 37.16% and 38%, respectively, while the 4-seeded berries were the less frequent with 4.05% and 2.67%. The mean berry weight was 6.13 g, while the mean value of the samples of Kecskemét was 5.98 g and 6.29 g of Kővágótöttös (Table 2). The mean value of perimeter was 77.18 mm, 77.64 mm of Kecskemét and 76.78 mm of Kővágótöttös. There were differences yet not significant in the case of the area, the mean value was 416.72 mm2, the mean of Kecskemét's representatives was 418.1 and 415.78 mm2 those of Kövágótőttös. The average values of Width mid-height and Maximum width were very similar (WMH: 20.87 mm, MW: 21.18 mm), furthermore in the case of Kecskemét and Kővágótöttös there was no dissimilarity of the two traits either. The mean of Height Mid-width was 24.35 mm, while the average of Kecskemét valued 24.73 mm and Kővágótöttös's was 24 mm. The Maximum Height of the samples was 24.95 mm in average, and it was 25.44 mm and 24.55 mm of the two different locations. The mean of Curved Height was 28.08 mm, and the two locations had 28.57 mm and 27.62 mm in average. On the other hand, the examined shape traits, namely Fruit Shape Index External I and II had significant differences according to the growing sites, the mean value of Fruit Shape Index External I was 1.15, while that of Fruit Shape Index External II was 1.17. The average of the samples of Kecskemét was 1.21 and 1.2, while they were 1.15 and 1.14 of the Kővágótöttös's values. The growing sites had no significant effects on the samples in the case of the third investigated shape trait. The mean value of the Curved Fruit Shape Index was 1.33, Kecskemét valued 1.37, while Kővágótöttös's average was 1.3.

Table 1.

Frequency of berries with different seed numbers in the two investigated locations

Seed number per berryKecskemétKővágótöttös
07.43%4.67%
137.16%38%
231.76%36%
319.59%18.67%
44.05%2.67%
Table 2.

Summary statistics of the berry morphological traits in the two investigated locations

Morphological traitLocationMeanMin.Max.Stand. dev.Coeff. var.
Size traits
WeightKecskemét5.98a1.099.91.728.36
Kővágótöttös6.29a1.711.161.8729.8
PerimeterKecskemét77.64a55.594.87.399.52
Kővágótöttös76.78a56.0795.747.7710.12
AreaKecskemét418.1a216.64588.6377.418.51
Kővágótöttös415.78a226.61593.6478.5218.88
Width Mid-heightKecskemét20.68a14.8625.022.1210.23
Kővágótöttös21.07a14.8627.692.2410.62
Maximum WidthKecskemét21a15.1125.152.079.84
Kővágótöttös21.36a15.2427.692.1610.13
Height Mid-widthKecskemét24.73a16.3830.732.911.75
Kővágótöttös24a15.7531.622.7911.64
Maximum HeightKecskemét25.44a18.5430.732.369.28
Kővágótöttös24.55a17.9131.752.5610.42
Curved HeightKecskemét28.57a20.8535.432.819.83
Kővágótöttös27.62a19.2236.23.1111.25
Shape traits
Fruit Shape Index External IKecskemét1.21b1.031.40.086.35
Kővágótöttös1.15a0.961.450.097.97
Fruit Shape Index External IIKecskemét1.2b0.861.40.097.92
Kővágótöttös1.14a0.831.490.119.68
Curved Fruit Shape IndexKecskemét1.37a1.151.640.17.5
Kővágótöttös1.3a1.051.670.118.17

Means followed by different letters indicate significant difference (P < 0.05).

The effect of the seed number on the morphological traits: Results showed that the investigated samples had 0 to 4 seeds per berry. Seedlessness was present in 6.04% of the samples while four seeds were also rare (3.35%). Most berries had one or two seeds (37.58% and 33.89% of the sample set respectively). Data showed that seed number had significant effect on the size and shape traits of the ‘Italia’ grapevine cultivar (Table 3). According to the investigation, seed number had significant effect (P < 0.05) on the berry weight. Differences were noticeable among the group of berries with different seed numbers except for the three- and four-seeded berries. Smallest berries were the seedless group with 2.8 g in average, while those with four seeds had 8.01 g. Coefficient of variation was the highest in the case of seedless berries (38.51%) meaning that this group was the most divers in weight. The most uniform berry weight was observed in the case of four-seeded berries (CV = 11.64%). Size traits showed the same pattern. Perimeter was the smallest in the case of seedless berries (63.69 mm) while the largest were the four-seeded ones (84.94 mm). Variability of the perimeter was the highest in the case of seedless berries (CV = 10.05%) while the lowest CV value was observed in the case of the four-seeded ones (CV = 5.6%). Area of the berries also significantly changed caused by the different seed numbers. Trend in the change was the same as in the case of weight and perimeter. Size was evaluated according to the width and length too. These traits showed the same tendency as above. Lowest values were recorded in the case of seedless berries while the highest values in the case of the four-seeded ones. Variation of the values within each group (CV values) were the lowest in the four-seeded berries while the highest values were recorded in the case of the seedless ones. Shape of the berries were evaluated according to the fruit shape index calculated by the ratio of the width and length at different positions of the berries. Low variability was observed in the fruit shape index, CV values ranged from 5.21% to 9.93% and there was no significant difference among the samples with different seed numbers.

Table 3.

Summary statistics of the different seed number groups of the ‘Italia’ grapevine cultivar

Morphological traitSeed numberMeanMin.Max.Stand. dev.Coeff. var.
Size traits
Berry weight (g)02.8a1.095.231.0838.51
15.14b2.459.461.0821.09
26.59c3.49.671.0916.56
38d4.9211.161.3817.31
48.01d6.179.410.9311.64
Perimeter (mm)063.69a55.574.46.410.05
172.51b59.895.746.178.51
279.34c63.893.345.426.83
383.56d72.6991.94.675.59
484.94d80.1893.844.765.6
Area (mm2)0274.11a216.64387.7753.9719.69
1365.82b226.61513.5551.9414.2
2437.78c287.84549.5849.811.38
3495.43d375593.6454.9611.09
4496.69d447.93588.6342.658.59
Width Mid-height (mm)017.08a14.8619.941.7110.01
119.6b14.8624.511.698.6
221.25c17.2724.771.517.1
323.01d19.4327.691.617
422.96d20.5724.511.295.61
Maximum Width (mm)017.3a15.1120.071.7410.05
119.96b15.2424.771.648.22
221.57c17.6524.771.466.77
323.2d19.8127.691.576.77
423.33d21.4624.641.024.36
Height Mid-width (mm)020.08a17.9124.512.0910.42
122.72b15.7526.82.3610.37
225.07c1629.462.379.47
326.63d21.9731.621.957.31
426.28cd19.0530.232.9211.11
Maximum Height (mm)020.61a17.9124.642.110.19
123.39b19.1827.941.898.1
225.85c21.0831.241.867.2
326.96d22.6131.751.776.57
427.13cd25.5330.731.615.93
Curved Height (mm)023.16a19.2226.582.4810.71
126.35b20.9332.052.469.33
228.87c23.4434.632.247.76
330.57d25.0936.22.27.18
430.62cd28.1333.822.056.68
Shape traits
Fruit Shape Index External I01.2a1.061.40.086.88
11.18a0.971.410.097.28
21.2a0.991.450.18.01
31.17a0.961.360.097.6
41.16a1.091.280.065.21
Fruit Shape Index External II01.19a1.071.310.076.26
11.16a0.851.480.119.15
21.18a0.831.490.129.93
31.16a0.951.360.18.22
41.14a0.931.290.18.56
Curved Fruit Shape Index01.33a1.111.580.129.27
11.33a1.131.640.118.06
21.35a1.091.670.128.79
31.32a1.051.570.17.68
41.32a1.191.410.085.8

Means followed by different letters indicate significant difference (P<0.05).

Discussion

‘Italia’ is one of the most important and most studied among the grapevine cultivars. Italia has several somatic mutations resulting new cultivars with variable morphological traits (Maia et al., 2009). Beside these bud sports, a large number of clones are in cultivation. Characterization and discrimination of these sports and clones have high importance (Fanizza et al., 2003), which can be carried out both with morphological and molecular approaches too. There are more than 150 traits in the OIV (2009) descriptor list, within this 20 are dealing with the berry morphology. Size is described by the length and width of the berries, where 5 classes are available for length: very short (up to about 8 mm), short (about 13 mm), medium (about 18 mm), long (about 23 mm) and very long (about 28 mm and more), and for the width too: very narrow (up to about 8 mm), narrow (about 13 mm), medium (about 18 mm), wide (about 23 mm) and very wide (about 28 mm and more). In this study, we found that berry size of the ‘Italia’ grapevine cultivar ranged from medium to large categories.

Width can be evaluated at various positions of the berry. In the TA, there is a possibility to investigate the width at the half of the height and at the widest position. In the case of ‘Italia’ we found only minor difference between these data, but berry shape in many cultivars are not spherical, but ellipsoid, obovoid, ovoid or in extreme cases, horn or finger shaped. In the latter categories, size traits measured at different positions can vary significantly. According to this, it would be important to highlight in the reports and descriptor lists at which position the berry width is measured at all.

Grapevine berry size is changing during the development according to a double sigmoid curve. Final size depends on the cultivar (Bényei and Lőrincz, 2005), pruning bud-load (Intrieri et al., 2001), special canopy management treatments (Carreño et al., 1998) or for example the seed number. Grapevine berry has usually (0) 1 to 4 (5) seeds. In this study, we found that ‘Italia’ have 0 to 4 seeds in the berries. Authors in the early 18th century were already dealing with the seed number of the cultivars and in some cases highlighted the seedlessness as Miller (1724) mentioned ‘the Currant grape is something larger than the former, is a very good fruit has no seeds’. Today this phenomenon is a central aim of the table grape breeding (Royo et al., 2018). In this study, we found that 6.04% of the samples had no seeds, while most of the berries had 1 or 2 seeds (37.58% and 33.89% respectively), which result is in accordance with Sabir (2011), who found that seed number ranges from 1.84 to 1.98 per berry according to the pollinator.

Calculation of the berry size traits provides primary data (width, length), but further data can help to calculate surface area too. Skin surface of the grapevine has high importance to evaluate for example anthocyanin content, which is a key factor of red wine production. Díaz-Pérez et al. (2007) calculated bell pepper skin surface based on photocopy, while Barbagallo et al. (2011) applied a LICOR LI 3100 area meter for the evaluation of the skin surface of Syrah grapevine cultivar. Image analysis of the berry size traits can be a possible way for the skin surface measurements too.

Conclusions

Grapevine berry shape and size are traits affected by many factors. Such factors could be environmental or developmental factors. According to this research it can be stated that seed number influences significantly the size of the grapevine berries.

Acknowledgement

This research was supported by the Ministry of Innovation and Technology within the framework of the Thematic Excellence Programme 2020, National Challenges Subprogramme (TKP2020-NKA-16) and Institutional Excellence Subprogram (TKP2020-IKA-12) for research on plant breeding and plant protection.

References

  • Barbagallo, M.G., Guidoni, S., and Hunter J.J. (2011). Berry size and qualitative characteristics of Vitis vinifera L. cv. Syrah. South African Journal of Enology and Viticulture, 32(1): 129136.

    • Search Google Scholar
    • Export Citation
  • Bényei, F. and Lőrincz, A. (2005). Borszőlőfajták, csemegeszőlő-fajták és alanyok. Mezőgazda Kiadó, Budapest, p. 314.

  • Brewer, M. T., Lang, L., Fujimura, K., Dujmovic, N., Gray, S., and van der Knaap, E. (2006). Development of a controlled vocabulary and software application to analyze fruit shape variation in tomato and other plant species. Plant Physiology, 141: 1525.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Carreno, J., Faraj, S., and Martinez, A. (1998). Effects of girdling and covering mesh on ripening, colour and fruit characteristics of ‘Italia’ grapes. The Journal of Horticultural Science and Biotechnology, 73(1): 103106.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Díaz-Pérez, J.C., Muy- Rangel, M.D., and Mascorro, A.G. (2007). Fruit size and stage of ripeness affect postharvest water loss in bell pepper fruit (Capsicum annuum L.). Journal of the Science of Food and Agriculture, 87: 6873.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Duhamel, du M. (1768). Traité des arbres fruitiers: contenant leur figure, leur description, leur culture, &c, Vol 2. Paris. 261.

  • Fanizza, G., Chaabane, R., Ricciardi, L., and Resta, P. (2003). Analysis of a spontaneous mutant and selected clones of cv. Italia (Vitis vinifera) by AFLP markers. Vitis, 42(1): 2730.

    • Search Google Scholar
    • Export Citation
  • Frege, M.C.A. (1804). Versuch einer Classification der Wein-Sorten nach ihre beeren. Meissen. 171.

  • Hammer, O., Harper, D.A.T., and Ryan, P.D. (2001). Past: paleontological Statistics software package for education and data analysis. Paleontologia Electronica, 4(1): 9.

    • Search Google Scholar
    • Export Citation
  • Hurtado, M., Vilanova, S., Plazas, M., Gramazio, P., Herraiz, F.J., Andújar, I., and Prohens, J. (2013). Phenomics of fruit shape in eggplant (Solanum melongena L.) using Tomato Analyzer software. Scientia Horticulturae, 164: 625632.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Intrieri, C., Poni, S., Lia, G., and Del Campo, M.G. (2001). Vine performance and leaf physiology of conventionally and minimally pruned Sangiovese grapevines. Vitis, 40(3): 123130.

    • Search Google Scholar
    • Export Citation
  • Kircherer, A., Roscher, R., Herzog, K-., Simon, S., Förstner, W., and Töpfer, R. (2013). BAT (Berry Analysis Tool): a high-throughput image interpretation tool to acquire the number, diameter, and volume of grapevine berries. Vitis, 52(3): 129135.

    • Search Google Scholar
    • Export Citation
  • Maia, S.H.Z, Mangolin, C.A., Collet, S.A.O., and Machado, M.F.P.S. (2009). Genetic diversity in somatic mutants of grape (Vitis vinifera) cultivar Italia based on random amplified polymorphic DNA. Genetics and Molecular Research, 8(1): 2838.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Miller, P. (1724). The gardeners and florists dictionary: of a complete system of horticulture containing. London. 501.

  • OIV (2009). OIV descriptor list for grape varieties and Vitis species, 2nd ed. Office International de la Vigne et du Vin, Paris, France.

    • Search Google Scholar
    • Export Citation
  • Parkinson, J. (1629). Paradisi in sole paradisus terrestris.Humfrey Lownes and Robert Young, London, p. 650.

  • Predieri, S., Dris, R., and Rapparini, F. (2004). Influence of growing conditions on yield and quality of cherry: II. Fruit quality. Food, Agriculture & Environment, 2(1): 307309.

    • Search Google Scholar
    • Export Citation
  • Rodríguez, G., Strecker, J., Brewer, M., Gonzalo, M. J., Anderson, C., Lang, L., Sullivan, D., Wagner, E., Strecker, B., Drushal, R., Dujmovic, N., Fujimuro, K., Jack,A., Njanji, I., Thomas, J., Gray, S., and van der Knaap, E. (2010). Tomato analyzer user manual version 3.

    • Search Google Scholar
    • Export Citation
  • Roscher, R., Herzog, K., Kunkel, A., Kicherer, A., Töpfer, R., and Förstner, W. (2014). Automated image analysis framework for high-throughput determination of grapevine berry sizes using conditional random fields. Computers and Electronics in Agriculture, 100: 148158.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Royo, C., Torres-Pérez, R., and Carbonell-Bejerano, P. (2018). The major origin of seedless grapes is associated with a missense mutation in the MADS-box gene VviAGL11. Plant Physiology, 177: 12341253.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Sabir, A. (2011). Influences of self- and cross-pollinations on berry set, seed characteristics and germination progress of grape (Vitis vinifera cv. Italia). International Journal of Agriculture and Biology, 13(4): 591594.

    • Search Google Scholar
    • Export Citation
  • Szikszai, F.B. (1590). Nomenclatura seu dictionarium Latino Ungaricum per clarissimum virum D. Basilium Fabricium Szikszavianum, Debrecini.

    • Search Google Scholar
    • Export Citation
  • Barbagallo, M.G., Guidoni, S., and Hunter J.J. (2011). Berry size and qualitative characteristics of Vitis vinifera L. cv. Syrah. South African Journal of Enology and Viticulture, 32(1): 129136.

    • Search Google Scholar
    • Export Citation
  • Bényei, F. and Lőrincz, A. (2005). Borszőlőfajták, csemegeszőlő-fajták és alanyok. Mezőgazda Kiadó, Budapest, p. 314.

  • Brewer, M. T., Lang, L., Fujimura, K., Dujmovic, N., Gray, S., and van der Knaap, E. (2006). Development of a controlled vocabulary and software application to analyze fruit shape variation in tomato and other plant species. Plant Physiology, 141: 1525.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Carreno, J., Faraj, S., and Martinez, A. (1998). Effects of girdling and covering mesh on ripening, colour and fruit characteristics of ‘Italia’ grapes. The Journal of Horticultural Science and Biotechnology, 73(1): 103106.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Díaz-Pérez, J.C., Muy- Rangel, M.D., and Mascorro, A.G. (2007). Fruit size and stage of ripeness affect postharvest water loss in bell pepper fruit (Capsicum annuum L.). Journal of the Science of Food and Agriculture, 87: 6873.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Duhamel, du M. (1768). Traité des arbres fruitiers: contenant leur figure, leur description, leur culture, &c, Vol 2. Paris. 261.

  • Fanizza, G., Chaabane, R., Ricciardi, L., and Resta, P. (2003). Analysis of a spontaneous mutant and selected clones of cv. Italia (Vitis vinifera) by AFLP markers. Vitis, 42(1): 2730.

    • Search Google Scholar
    • Export Citation
  • Frege, M.C.A. (1804). Versuch einer Classification der Wein-Sorten nach ihre beeren. Meissen. 171.

  • Hammer, O., Harper, D.A.T., and Ryan, P.D. (2001). Past: paleontological Statistics software package for education and data analysis. Paleontologia Electronica, 4(1): 9.

    • Search Google Scholar
    • Export Citation
  • Hurtado, M., Vilanova, S., Plazas, M., Gramazio, P., Herraiz, F.J., Andújar, I., and Prohens, J. (2013). Phenomics of fruit shape in eggplant (Solanum melongena L.) using Tomato Analyzer software. Scientia Horticulturae, 164: 625632.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Intrieri, C., Poni, S., Lia, G., and Del Campo, M.G. (2001). Vine performance and leaf physiology of conventionally and minimally pruned Sangiovese grapevines. Vitis, 40(3): 123130.

    • Search Google Scholar
    • Export Citation
  • Kircherer, A., Roscher, R., Herzog, K-., Simon, S., Förstner, W., and Töpfer, R. (2013). BAT (Berry Analysis Tool): a high-throughput image interpretation tool to acquire the number, diameter, and volume of grapevine berries. Vitis, 52(3): 129135.

    • Search Google Scholar
    • Export Citation
  • Maia, S.H.Z, Mangolin, C.A., Collet, S.A.O., and Machado, M.F.P.S. (2009). Genetic diversity in somatic mutants of grape (Vitis vinifera) cultivar Italia based on random amplified polymorphic DNA. Genetics and Molecular Research, 8(1): 2838.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Miller, P. (1724). The gardeners and florists dictionary: of a complete system of horticulture containing. London. 501.

  • OIV (2009). OIV descriptor list for grape varieties and Vitis species, 2nd ed. Office International de la Vigne et du Vin, Paris, France.

    • Search Google Scholar
    • Export Citation
  • Parkinson, J. (1629). Paradisi in sole paradisus terrestris.Humfrey Lownes and Robert Young, London, p. 650.

  • Predieri, S., Dris, R., and Rapparini, F. (2004). Influence of growing conditions on yield and quality of cherry: II. Fruit quality. Food, Agriculture & Environment, 2(1): 307309.

    • Search Google Scholar
    • Export Citation
  • Rodríguez, G., Strecker, J., Brewer, M., Gonzalo, M. J., Anderson, C., Lang, L., Sullivan, D., Wagner, E., Strecker, B., Drushal, R., Dujmovic, N., Fujimuro, K., Jack,A., Njanji, I., Thomas, J., Gray, S., and van der Knaap, E. (2010). Tomato analyzer user manual version 3.

    • Search Google Scholar
    • Export Citation
  • Roscher, R., Herzog, K., Kunkel, A., Kicherer, A., Töpfer, R., and Förstner, W. (2014). Automated image analysis framework for high-throughput determination of grapevine berry sizes using conditional random fields. Computers and Electronics in Agriculture, 100: 148158.

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  • Royo, C., Torres-Pérez, R., and Carbonell-Bejerano, P. (2018). The major origin of seedless grapes is associated with a missense mutation in the MADS-box gene VviAGL11. Plant Physiology, 177: 12341253.

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  • Sabir, A. (2011). Influences of self- and cross-pollinations on berry set, seed characteristics and germination progress of grape (Vitis vinifera cv. Italia). International Journal of Agriculture and Biology, 13(4): 591594.

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  • Szikszai, F.B. (1590). Nomenclatura seu dictionarium Latino Ungaricum per clarissimum virum D. Basilium Fabricium Szikszavianum, Debrecini.

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The author instruction is available in PDF.
Please, download the file from HERE.

 

 

Senior editors

Editor(s)-in-Chief: Felföldi, József

Chair of the Editorial Board Szendrő, Péter

Editorial Board

  • Beke, János (Szent István University, Faculty of Mechanical Engineerin, Gödöllő – Hungary)
  • Fenyvesi, László (Szent István University, Faculty of Mechanical Engineering, Gödöllő – Hungary)
  • Szendrő, Péter (Szent István University, Faculty of Mechanical Engineering, Gödöllő – Hungary)
  • Felföldi, József (Szent István University, Faculty of Food Science, Budapest – Hungary)

 

Advisory Board

  • De Baerdemaeker, Josse (KU Leuven, Faculty of Bioscience Engineering, Leuven - Belgium)
  • Funk, David B. (United States Department of Agriculture | USDA • Grain Inspection, Packers and Stockyards Administration (GIPSA), Kansas City – USA
  • Geyer, Martin (Leibniz Institute for Agricultural Engineering and Bioeconomy (ATB), Department of Horticultural Engineering, Potsdam - Germany)
  • Janik, József (Szent István University, Faculty of Mechanical Engineering, Gödöllő – Hungary)
  • Kutzbach, Heinz D. (Institut für Agrartechnik, Fg. Grundlagen der Agrartechnik, Universität Hohenheim – Germany)
  • Mizrach, Amos (Institute of Agricultural Engineering. ARO, the Volcani Center, Bet Dagan – Israel)
  • Neményi, Miklós (Széchenyi University, Department of Biosystems and Food Engineering, Győr – Hungary)
  • Schulze-Lammers, Peter (University of Bonn, Institute of Agricultural Engineering (ILT), Bonn – Germany)
  • Sitkei, György (University of Sopron, Institute of Wood Engineering, Sopron – Hungary)
  • Sun, Da-Wen (University College Dublin, School of Biosystems and Food Engineering, Agriculture and Food Science, Dublin – Ireland)
  • Tóth, László (Szent István University, Faculty of Mechanical Engineering, Gödöllő – Hungary)

Prof. Felföldi, József
Institute: Physics-Control Department, Szent István University
Address: 1118 Budapest Somlói út 14-16
Phone: +36 1 305 7206
E-mail: Felfoldi.Jozsef@etk.szie.hu

Indexing and Abstracting Services:

  • CABI

2020  
Scimago
H-index
8
Scimago
Journal Rank
0,197
Scimago
Quartile Score
Environmental Engineering Q4
Industrial and Manufacturing Engineering Q3
Mechanical Engineering Q4
Scopus
Cite Score
33/69=0,5
Scopus
Cite Score Rank
Environmental Engineering 126/146 (Q4)
Industrial and Manufacturing Engineering 269/336 (Q3)
Mechanical Engineering 512/596 (Q4)
Scopus
SNIP
0,211
Scopus
Cites
53
Scopus
Documents
41
Days from submission to acceptance 122
Days from acceptance to publication 40
Acceptance rate 86%

 

2019  
Scimago
H-index
6
Scimago
Journal Rank
0,123
Scimago
Quartile Score
Environmental Engineering Q4
Industrial and Manufacturing Engineering Q4
Mechanical Engineering Q4
Scopus
Cite Score
18/33=0,5
Scopus
Cite Score Rank
Environmental Engineering 108/132 (Q4)
Industrial and Manufacturing Engineering 242/340 (Q3)
Mechanical Engineering 481/585 (Q4)
Scopus
SNIP
0,211
Scopus
Cites
13
Scopus
Documents
5

 

Progress in Agricultural Engineering Sciences
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Progress in Agricultural Engineering Sciences
Language English
Size B5
Year of
Foundation
2004
Publication
Programme
2021 Volume 17
Volumes
per Year
1
Issues
per Year
1
Founder Magyar Tudományos Akadémia
Founder's
Address
H-1051 Budapest, Hungary, Széchenyi István tér 9.
Publisher Akadémiai Kiadó
Publisher's
Address
H-1117 Budapest, Hungary 1516 Budapest, PO Box 245.
Responsible
Publisher
Chief Executive Officer, Akadémiai Kiadó
ISSN 1786-335X (Print)
ISSN 1787-0321 (Online)

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