Sunflower petals: Some physical properties and modeling distribution of their number, dimensions, and mass Article Swipe
Amir Hossein Mirzabe
,
Golam Reza Chegini
,
Javad Khazaei
·
YOU?
·
· 2018
· Open Access
·
· DOI: https://doi.org/10.1016/j.inpa.2018.03.002
YOU?
·
· 2018
· Open Access
·
· DOI: https://doi.org/10.1016/j.inpa.2018.03.002
Sunflower petal is one of the parts of the sunflower which has drawn attention and has several applications these days. These applications justify getting information about physical properties, mechanical properties, drying trends, etc. in order to design new machines and use new methods to harvest or dry the sunflower petals. For three varieties of sunflower, picking force of petals was measured; number of petals of each head was counted; unit mass and 1000-unit mass of fresh petals were measured and length, width, and projected area of fresh petals were calculated based on image processing technique; frequency distributions of these parameters were modeled using statistical distribution models namely Gamma, Generalized Extreme Value (G. E. V), Lognormal, and Weibull. Results of picking force showed that with increasing number of days after appearing the first petal on each head from 5 to 14 and decreasing loading rate from 150 g min−1 to 50 g min−1 values of picking force were decreased for three varieties, but diameter of sunflower head had different effects on picking force for each variety. Length, width, and number of petals of Dorsefid variety ranged from 38.52 to 95.44 mm, 3.80 to 9.28 mm and 29 to 89, respectively. The corresponding values ranged from 34.19 to 88.18 mm, 4.28 to 10.60 mm and 21 to 89, respectively for Shamshiri variety and ranged from 44.47 to 114.63 mm, 7.03 to 20.31 mm and 29 to 89 for Sirena variety. Results of frequency distribution modeling indicated that in most cases, G. E. V and Weibull distributions had better performance than other distributions. Keywords: Sunflower (Helianthus annus L.) petal, Picking force, Image processing, Fibonacci sequence, Lucas sequence
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Metadata
- Type
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- Language
- en
- Landing Page
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- OA Status
- gold
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- 13
- References
- 37
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- OpenAlex ID
- https://openalex.org/W2791839339
All OpenAlex metadata
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- OpenAlex ID
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https://openalex.org/W2791839339Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.1016/j.inpa.2018.03.002Digital Object Identifier
- Title
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Sunflower petals: Some physical properties and modeling distribution of their number, dimensions, and massWork title
- Type
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articleOpenAlex work type
- Language
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enPrimary language
- Publication year
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2018Year of publication
- Publication date
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2018-03-22Full publication date if available
- Authors
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Amir Hossein Mirzabe, Golam Reza Chegini, Javad KhazaeiList of authors in order
- Landing page
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https://doi.org/10.1016/j.inpa.2018.03.002Publisher landing page
- Open access
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YesWhether a free full text is available
- OA status
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goldOpen access status per OpenAlex
- OA URL
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https://doi.org/10.1016/j.inpa.2018.03.002Direct OA link when available
- Concepts
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Petal, Sunflower, Log-normal distribution, Weibull distribution, Mathematics, Head (geology), Horticulture, Sunflower seed, Botany, Statistics, Biology, PaleontologyTop concepts (fields/topics) attached by OpenAlex
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13Total citation count in OpenAlex
- Citations by year (recent)
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2025: 11, 2021: 1, 2020: 1Per-year citation counts (last 5 years)
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37Number of works referenced by this work
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.89, | 197, 215 |
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| abstract_inverted_index.L.) | 264 |
| abstract_inverted_index.The | 199 |
| abstract_inverted_index.V), | 113 |
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| abstract_inverted_index.dry | 46 |
| abstract_inverted_index.for | 158, 172, 217, 235 |
| abstract_inverted_index.had | 166, 254 |
| abstract_inverted_index.has | 11, 15 |
| abstract_inverted_index.mm, | 189, 207, 226 |
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| abstract_inverted_index.one | 3 |
| abstract_inverted_index.the | 5, 8, 47, 130 |
| abstract_inverted_index.use | 40 |
| abstract_inverted_index.was | 59, 67 |
| abstract_inverted_index.3.80 | 190 |
| abstract_inverted_index.4.28 | 208 |
| abstract_inverted_index.7.03 | 227 |
| abstract_inverted_index.9.28 | 192 |
| abstract_inverted_index.area | 84 |
| abstract_inverted_index.days | 127 |
| abstract_inverted_index.each | 65, 134, 173 |
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| abstract_inverted_index.head | 66, 135, 165 |
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| abstract_inverted_index.most | 246 |
| abstract_inverted_index.rate | 143 |
| abstract_inverted_index.than | 257 |
| abstract_inverted_index.that | 122, 244 |
| abstract_inverted_index.unit | 69 |
| abstract_inverted_index.were | 77, 88, 100, 156 |
| abstract_inverted_index.with | 123 |
| abstract_inverted_index.10.60 | 210 |
| abstract_inverted_index.20.31 | 229 |
| abstract_inverted_index.34.19 | 204 |
| abstract_inverted_index.38.52 | 186 |
| abstract_inverted_index.44.47 | 223 |
| abstract_inverted_index.88.18 | 206 |
| abstract_inverted_index.95.44 | 188 |
| abstract_inverted_index.Image | 268 |
| abstract_inverted_index.Lucas | 272 |
| abstract_inverted_index.These | 20 |
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| abstract_inverted_index.after | 128 |
| abstract_inverted_index.annus | 263 |
| abstract_inverted_index.based | 90 |
| abstract_inverted_index.days. | 19 |
| abstract_inverted_index.drawn | 12 |
| abstract_inverted_index.first | 131 |
| abstract_inverted_index.force | 56, 120, 155, 171 |
| abstract_inverted_index.fresh | 75, 86 |
| abstract_inverted_index.image | 92 |
| abstract_inverted_index.order | 34 |
| abstract_inverted_index.other | 258 |
| abstract_inverted_index.parts | 6 |
| abstract_inverted_index.petal | 1, 132 |
| abstract_inverted_index.these | 18, 98 |
| abstract_inverted_index.three | 51, 159 |
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| abstract_inverted_index.which | 10 |
| abstract_inverted_index.114.63 | 225 |
| abstract_inverted_index.Gamma, | 107 |
| abstract_inverted_index.Sirena | 236 |
| abstract_inverted_index.better | 255 |
| abstract_inverted_index.cases, | 247 |
| abstract_inverted_index.design | 36 |
| abstract_inverted_index.drying | 30 |
| abstract_inverted_index.force, | 267 |
| abstract_inverted_index.models | 105 |
| abstract_inverted_index.namely | 106 |
| abstract_inverted_index.number | 61, 125, 178 |
| abstract_inverted_index.petal, | 265 |
| abstract_inverted_index.petals | 58, 63, 76, 87, 180 |
| abstract_inverted_index.ranged | 184, 202, 221 |
| abstract_inverted_index.showed | 121 |
| abstract_inverted_index.values | 152, 201 |
| abstract_inverted_index.width, | 81, 176 |
| abstract_inverted_index.Extreme | 109 |
| abstract_inverted_index.Length, | 175 |
| abstract_inverted_index.Picking | 266 |
| abstract_inverted_index.Results | 117, 238 |
| abstract_inverted_index.Weibull | 252 |
| abstract_inverted_index.effects | 168 |
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| abstract_inverted_index.harvest | 44 |
| abstract_inverted_index.justify | 22 |
| abstract_inverted_index.length, | 80 |
| abstract_inverted_index.loading | 142 |
| abstract_inverted_index.methods | 42 |
| abstract_inverted_index.min−1 | 147, 151 |
| abstract_inverted_index.modeled | 101 |
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| abstract_inverted_index.picking | 55, 119, 154, 170 |
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| abstract_inverted_index.trends, | 31 |
| abstract_inverted_index.variety | 183, 219 |
| abstract_inverted_index.Dorsefid | 182 |
| abstract_inverted_index.Weibull. | 116 |
| abstract_inverted_index.counted; | 68 |
| abstract_inverted_index.diameter | 162 |
| abstract_inverted_index.machines | 38 |
| abstract_inverted_index.measured | 78 |
| abstract_inverted_index.modeling | 242 |
| abstract_inverted_index.physical | 26 |
| abstract_inverted_index.sequence | 273 |
| abstract_inverted_index.variety. | 174, 237 |
| abstract_inverted_index.1000-unit | 72 |
| abstract_inverted_index.Fibonacci | 270 |
| abstract_inverted_index.Keywords: | 260 |
| abstract_inverted_index.Shamshiri | 218 |
| abstract_inverted_index.Sunflower | 0, 261 |
| abstract_inverted_index.appearing | 129 |
| abstract_inverted_index.attention | 13 |
| abstract_inverted_index.decreased | 157 |
| abstract_inverted_index.different | 167 |
| abstract_inverted_index.frequency | 95, 240 |
| abstract_inverted_index.indicated | 243 |
| abstract_inverted_index.measured; | 60 |
| abstract_inverted_index.projected | 83 |
| abstract_inverted_index.sequence, | 271 |
| abstract_inverted_index.sunflower | 9, 48, 164 |
| abstract_inverted_index.varieties | 52 |
| abstract_inverted_index.Lognormal, | 114 |
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| abstract_inverted_index.increasing | 124 |
| abstract_inverted_index.mechanical | 28 |
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| abstract_inverted_index.processing | 93 |
| abstract_inverted_index.sunflower, | 54 |
| abstract_inverted_index.technique; | 94 |
| abstract_inverted_index.varieties, | 160 |
| abstract_inverted_index.(Helianthus | 262 |
| abstract_inverted_index.Generalized | 108 |
| abstract_inverted_index.information | 24 |
| abstract_inverted_index.performance | 256 |
| abstract_inverted_index.processing, | 269 |
| abstract_inverted_index.properties, | 27, 29 |
| abstract_inverted_index.statistical | 103 |
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| abstract_inverted_index.corresponding | 200 |
| abstract_inverted_index.distributions | 96, 253 |
| abstract_inverted_index.respectively. | 198 |
| abstract_inverted_index.distributions. | 259 |
| cited_by_percentile_year.max | 99 |
| cited_by_percentile_year.min | 89 |
| corresponding_author_ids | https://openalex.org/A5002627050 |
| countries_distinct_count | 1 |
| institutions_distinct_count | 3 |
| corresponding_institution_ids | https://openalex.org/I23946033 |
| citation_normalized_percentile.value | 0.55812367 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |