2012
DOI: 10.1590/s0101-31222012000400009
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Computerized imaging analysis of seedlings for assessment of physiological potential of wheat seeds

Abstract: -Nowadays, image analysis is one of the most modern tools in evaluating physiological potential of seeds. This study aimed at verifying the efficiency of the seedling imaging analysis to assess physiological potential of wheat seeds. The seeds of wheat, cultivars IAC 370 and IAC 380, each of which represented by five different lots, were stored during four months under natural environmental conditions of temperature (T) and relative humidity (RH), in municipality of Piracicaba, Stated of São Paulo, Brazil. For… Show more

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Cited by 13 publications
(12 citation statements)
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“…The studies on automated analysis of seedling images have basically compared results obtained from SVIS® with those from tests usually accepted for seed vigor evaluation, as observed with melon (Marcos-Filho et al, 2006), soybean , cucumber (Chiquito et al, 2012), sweet corn (Alvarenga et al, 2012), and wheat (Silva et al, 2012), among other crops.…”
Section: Sse (Index) ------------------------------------------------mentioning
confidence: 99%
“…The studies on automated analysis of seedling images have basically compared results obtained from SVIS® with those from tests usually accepted for seed vigor evaluation, as observed with melon (Marcos-Filho et al, 2006), soybean , cucumber (Chiquito et al, 2012), sweet corn (Alvarenga et al, 2012), and wheat (Silva et al, 2012), among other crops.…”
Section: Sse (Index) ------------------------------------------------mentioning
confidence: 99%
“…Recentemente, vários trabalhos de pesquisa indicam eficiência do uso de análise computadorizada de plântulas com o sistema SVIS ® na avaliação do vigor de sementes de berinjela (SILVA & CICERO, 2014), trigo (SILVA et al, 2012a), milho doce (ALVARENGA et al, 2012), crotalária (SILVA et al, 2012b), pepino (CHIQUITO et al, 2012), entre outras.…”
Section: Introductionunclassified
“…Yet, under E2 environmental conditions, the index value for seed development uniformity of the lot 3 was already the lowest from the second storage month; whereas the seeds of lot 5 showed the highest uniformity index throughout the two and eight storage months. However, other studies on the use of the software SVIS ® to assess the seed physiological potential have shown that the development uniformity index was not sufficiently sensitive to allow distinction between different seed lots of soybean Santos et al, 2011), cucumber (Chiquito et al, 2012, and wheat (Silva et al, 2012a); but was efficient to assess vigor level of seeds of melon (Marcos-Filho et al, 2006) and sunn hemp (Silva et al, 2012b).…”
Section: Field Seedling Emergence (%) Cold and Dry Chamber (E1)mentioning
confidence: 99%
“…(Hoffmaster et al, 2005;Marcos-Filho et al, 2009); corn (Zea mays L.) (Hoffmaster et al, 2005;Otoni and McDonald, 2005), melon (Cucumis melo L.) (Marcos-Filho et al, 2006), sweet corn (Zea mays L. var. saccharata Bailey) Alvarenga et al, 2012), cucumber (Cucumis sativus L.) (Chiquito et al, 2012); wheat (Triticum aestivum L.) (Silva et al, 2012a); and sunn-hemp (Crotalaria juncea L.) (Silva et al, 2012b). However, there is still no information on the efficiency of analyses performed by the software SVIS ® on assessing physiological potential of cotton seeds.…”
Section: Introductionmentioning
confidence: 99%