Radiographic Imaging as a Quality Index Proxy for Brachiaria brizantha Seeds
Abstract
:1. Introduction
2. Results
2.1. Water Content and 1000-Seed Weight
2.2. Internal Seed Morphology Assessment Employing the X-ray Technique
2.3. Germination Test
2.4. Anatomical Seed Characterization
3. Discussion
4. Material and Methods
4.1. Assay Implementation
4.2. The 1000-Seed Weight Test
4.3. Water Content
4.4. Internal Seed Morphology Assessments Employing the X-ray Technique
4.5. Germination Test
4.6. Morphoanatomical Characterizations
4.7. Biochemical Analyses
4.8. Statistical Analyses
5. Conclusions
Author Contributions
Funding
Acknowledgments
Conflicts of Interest
References
- Bosi, C.; Sentelhas, P.C.; Huth, N.I.; Pezzopane, J.R.M.; Andreucci, M.P.; Santos, P.M. APSIM-Tropical Pasture: A model for simulating perennial tropical grass growth and its parameterisation for palisade grass (Brachiaria brizantha). Agric. Syst. 2020, 184, 102917. [Google Scholar] [CrossRef]
- Goldewijk, K.K.; Beusen, A.; Doelman, J.; Stehfest, E. New anthropogenic land use estimates for the Holocene: HYDE 3.2. Earth Syst. Sci. Data 2017, 9, 927–953. [Google Scholar] [CrossRef] [Green Version]
- Low, S. Signal grass (Brachiaria decumbens) toxicity in grazing ruminants. Agriculture 2015, 5, 971–990. [Google Scholar] [CrossRef] [Green Version]
- Mateus, G.P.; Crusciol, C.A.C.; Pariz, C.M.; Borghi, E.; Costa, C.; Martello, J.M.; Franzluebbers, A.J.; Castilhos, A.M. Sidedress nitrogen application rates to sorghum intercropped with tropical perennial grasses. Agron. J. 2016, 108, 433–447. [Google Scholar] [CrossRef]
- Crusciol, C.A.C.; Nascente, A.S.; Borghi, E.; Soratto, R.P.; Martins, P.O. Improving soil fertility and crop yield in a tropical region with palisadegrass cover crops. Agron. J. 2015, 107, 2271–2280. [Google Scholar] [CrossRef]
- Foley, J.A.; Ramankutty, N.; Brauman, K.A.; Cassidy, E.S.; Gerber, J.S.; Jhonston, M.; Muelller, N.D.; O’Connell, C.; Ray, D.K.; West, P.C.; et al. Solutions for a cultivated planet. Nature 2011, 478, 337–342. [Google Scholar] [CrossRef] [Green Version]
- Medeiros, A.D.; Silva, L.J.; Silva, J.M.; Dias, D.C.F.S.; Pereira, M.D. IJCropSeed: An open-access tool for high-throughput analysis of crop seed radiographs. Comput. Electron. Agric. 2020, 175, 105555. [Google Scholar] [CrossRef]
- Vilela, L.; Martha Junior, G.B.; Macedo, M.C.M.; Marchao, R.L.; Guimaraes Júnior, R.; Pulrolink, K.; Maciel, G.A. Integrated crop-livestock systems in the Cerrado region. Pesq. Agropec. Bras. 2011, 46, 1127–1138. [Google Scholar] [CrossRef]
- Meng, L.S.; Wang, Y.B.; Loake, G.J.; Jiang, J.H. Seed Embryo Development Is Regulated via an AN3-MINI3 Gene Cascade. Front. Plant. Sci 2016, 7, 1645. [Google Scholar] [CrossRef] [Green Version]
- Xia, Y.; Xu, Y.; Li, J.; Zhang, C.; Fan, S. Recent advances in emerging techniques for non-destructive detection of seed viability: A review. Artif. Intell. Agric. 2019, 1, 35–47. [Google Scholar] [CrossRef]
- Gargiulo, L.; Grimberg, A.; Valencia, R.R.C.; Carlsson, A.S.; Mele, G. Morpho-densitometric traits for quinoa (Chenopodium quinoa Willd.) seed phenotyping by two X-ray micro-CT scanning approaches. J. Cereal Sci. 2019, 90, 102829. [Google Scholar] [CrossRef]
- Ahmed, M.R.; Yasmin, J.; Collins, W.; Cho, B.K. X-ray CT image analysis for morphology of muskmelon seed in relation to germination. Biosyst. Eng. 2018, 175, 183–193. [Google Scholar] [CrossRef]
- Mahajan, S.; Mittal, S.K.; Das, A. Machine vision based alternative testing approach for physical purity, viability and vigour testing of soybean seeds (Glycine max). J. Food Sci. Technol. 2018, 55, 3949–3959. [Google Scholar] [CrossRef]
- Yan, D.; Duermeyer, L.; Leoveanu, C.; Nambara, E. The Functions of the Endosperm During Seed Germination. Plant. Cell Physiol. 2014, 55, 1521–1533. [Google Scholar] [CrossRef]
- Medeiros, A.D.; Silva, L.J.; Ribeiro, J.P.O.R.; Ferreira, K.C.; Rosas, J.T.F.; Santos, A.A.; Silva, C.B. Machine Learning for Seed Quality Classification: An Advanced Approach Using Merger Data from FT-NIR Spectroscopy and X-ray Imaging. Sensors 2020, 20, 4319. [Google Scholar] [CrossRef]
- Rahman, A.; Cho, B.K. Assessment of seed quality using non-destructive measurement techniques: A review. Seed Sci. Res. 2016, 26, 285–305. [Google Scholar] [CrossRef]
- Abud, H.F.; Cicero, S.M.; Gomes Junior, F.G. Imagens radiográficas e relação da morfologia interna e potencial fisiológico de sementes de brócolis. Acta Sci. Agron. 2018, 40, 1–9. [Google Scholar]
- Medeiros, A.D.; Araújo, J.O.; Zavala-León, M.J.; Silva, L.J.; Dias, D.C.F.S. Parameters based on x-ray images to assess the physical and physiological quality of Leucaena leucocephala seeds. Ciênc. Agrotec. 2018, 42, 643–652. [Google Scholar] [CrossRef]
- He, X.; Feng, X.; Sun, D.; Liu, F.; Bao, Y.; He, Y. Rapid and nondestructive measurement of rice seed vitality of different years using near-infrared hyperspectral imaging. Molecules 2019, 24, 2227. [Google Scholar] [CrossRef] [Green Version]
- Jyoti; Malik, C.P. Seed deterioration: A review. Int. J. Life Sci. Bt Pharm. Res. 2013, 2, 374–385. [Google Scholar]
- Arruda, N.; Cicero, S.M.; Gomes-Junior, F.G. Análise radiográfica para avaliar a estrutura da semente de Crotalaria juncea L. J. Seed Sci. 2016, 38, 161–168. [Google Scholar] [CrossRef] [Green Version]
- Al-Turki, T.A.; Baskin, C.C. Determination of seed viability of eight wild Saudi Arabian species by germination and X-ray tests. Saudi J. Biol. Sci. 2017, 24, 822–829. [Google Scholar] [CrossRef] [Green Version]
- Medeiros, A.D.; Martins, M.S.; Silvia, L.J.; Pereira, M.D.; León, M.J.Z.; Dias, D.C.F.S. X-ray imaging and digital processing application in non-destructive assessing of melon seed quality. J. Seed Sci. 2020, 42, e202042005. [Google Scholar] [CrossRef]
- Oliveira, G.E.; Von, P.R.G.; de Andrade, T.; Pinho, E.V.R.; Santos, C.D.; Veiga, A.D. Physiological quality and amylase enzyme expression in maize seeds. Ciênc Agrotec. 2013, 37, 40–48. [Google Scholar] [CrossRef] [Green Version]
- Medeiros, J.C.; Sales, J.F.; Zuchi, J.; Nascimento, K.J.T.; Silva, F.H.L.; Castro, S.T.; Costa, A.C.; Rodrigues, A.A. A multivariate approach to the physical and physiological quality of hybrid corn seeds affected by Molicutes and MRFV. Euphytica 2021, 217, 96. [Google Scholar] [CrossRef]
- Regras Para Análise de Sementes; Ministério da Agricultura e Reforma Agrária, Coordenação de Laboratório Vegetal: Brasília, Brasil, 2009; ISBN 978-85-99851-70-8.
- Karnovsky, M.J. A formaldehyde-glutaraldehyde fixative of high osmolality for use in electron-microscopy. J. Cell Biol. 1965, 27, 137A–138A. [Google Scholar]
- O’Brien, T.P.; Feder, N.; Mccully, M.E. Polychromatic staining of plant cell walls by toluidine blue O. Protoplasma 1964, 59, 368–373. [Google Scholar] [CrossRef]
- Bernfeld, P. [17] Amylases α and β. In Methods in Enzymology; Academic Press: Cambridge, MA, USA, 1955; Volume 1, pp. 149–152. [Google Scholar]
- Tárrago, J.F.; Nicolás, G. Starch Degradation in the Cotyledons of Germinating Lentils. Plant. Physiol 1976, 58, 618–621. [Google Scholar] [CrossRef] [Green Version]
- Kishorekumar, A.; Jaleel, C.A.; Manivannan, P.; Sankar, B.; Sridharan, R.; Panneerselvam, R. Comparative effects of different triazole compounds on growth, photosynthetic pigments and carbohydrate metabolism of Solenostemon rotundifolius. Colloids Surf. B Biointerfaces 2007, 60, 207–212. [Google Scholar] [CrossRef]
- Bradford, M.N. A rapid and sensitive method for the quantitation of microgram quantities of protein utilizing the principle of protein-dye binding. Analyt. Biochem 1976, 72, 248–254. [Google Scholar] [CrossRef]
- Hotelling, H. Analysis of a complex of statistical variables into principal components. J. Educ. Psychol. 1933, 24, 417–441. [Google Scholar] [CrossRef]
- Ferreira, D.F. Estatística Multivariada; Editora Ufla: Lavras, Brazil, 2008; 662p. [Google Scholar]
Cultivars | Area (mm2) | Circularity | Relative Density (Grey.Pixel−1) | Filling (%) |
---|---|---|---|---|
Marandu | 8.33 ± 0.07 a | 0.69 ± 0.003 b | 70.73 ± 0.69 a | 92 ± 0.23 a |
Piatã | 7.25 ± 0.04 b | 0.76 ± 0.003 a | 68.31 ± 1.19 a | 89 ± 0.58 b |
Xaraés | 6.63 ± 0.07 c | 0.68 ± 0.004 b | 58.43 ± 1.18 b | 84 ± 0.57 c |
One-Way ANOVA | ||||
F (t-test) | 193.83 ** | 132.08 ** | 38.86 ** | 76.19 ** |
p | 0.00825 | 0.00000 | 0.00000 | 0.00000 |
Cultivars | GSI | Germination (%) | Abnormal | Non-Germination |
---|---|---|---|---|
Marandu | 4.0 ± 0.57 a | 70 ± 4.55 a | 8 ± 1.31 a | 22 ± 1.60 b |
Piatã | 4.3 ± 0.07 a | 71 ± 1.73 a | 12 ± 4.86 a | 28 ± 1.31 b |
Xaraés | 2.3 ± 0.21 b | 48 ± 4.99 b | 14 ± 1.93 a | 40 ± 1.89 a |
One-Way ANOVA | ||||
F (t-test) | 30.3671 ** | 10.9039 * | 0.1614 ns | 9.1294 ** |
p | <0.0001 | 0.0039 | 0.8534 | 0.0068 |
Variables | Unit | Description |
---|---|---|
Area | mm2 | Selection area obtained in square pixels and later converted to square millimeters. |
Circularity | Circularity | Circularity = 4·π·Area/Perimeter2. Values of 1.0 indicate a perfect circle and values tending to 0 suggest an elongated shape. |
Filling | % | Determined by dividing the area effectively filled with high-density tissue (gray levels above the initially defined threshold) by the total area of each seed. |
Relative density | grey.pixel−1 | Defined as the sum of the gray values of all pixels in a selected area divided by the number of pixels in the selection. |
Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations. |
© 2022 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
Share and Cite
Vieira Campos, L.; Almeida Rodrigues, A.; de Fátima Sales, J.; Almeida Rodrigues, D.; Carvalho Vasconcelos Filho, S.; Lino Rodrigues, C.; Alves Vieira, D.; Tomaz de Castro, S.; Rubio Neto, A. Radiographic Imaging as a Quality Index Proxy for Brachiaria brizantha Seeds. Plants 2022, 11, 1014. https://doi.org/10.3390/plants11081014
Vieira Campos L, Almeida Rodrigues A, de Fátima Sales J, Almeida Rodrigues D, Carvalho Vasconcelos Filho S, Lino Rodrigues C, Alves Vieira D, Tomaz de Castro S, Rubio Neto A. Radiographic Imaging as a Quality Index Proxy for Brachiaria brizantha Seeds. Plants. 2022; 11(8):1014. https://doi.org/10.3390/plants11081014
Chicago/Turabian StyleVieira Campos, Leonardo, Arthur Almeida Rodrigues, Juliana de Fátima Sales, Douglas Almeida Rodrigues, Sebastião Carvalho Vasconcelos Filho, Cássia Lino Rodrigues, Dheynne Alves Vieira, Stella Tomaz de Castro, and Aurélio Rubio Neto. 2022. "Radiographic Imaging as a Quality Index Proxy for Brachiaria brizantha Seeds" Plants 11, no. 8: 1014. https://doi.org/10.3390/plants11081014