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Zhao,Yun; Guindo,Mahamed L.; Xu,Xing; Shi,Xiang; Sun,Miao; He,Yong. |
ABSTRACT We propose a segmentation algorithm for raisin extraction. The proposed approach consists of the following aspects. Deep learning is used to predict the number of raisins in each connected region, and the shape features such as the roundness, area, X-axis value for the centroid, Y-axis value for the centroid, axis length and perimeter of each region will be used to establish the prediction model. Morphological analysis, based on edge parameters including the polar axis, polar angle and angular velocity, is applied to search for the suitable break points that are useful for identifying the dividing lines between two adjacent raisins. To make our segmentation more accurate, some machine-learning algorithms such as the random forest (RF), support... |
Tipo: Info:eu-repo/semantics/article |
Palavras-chave: Raisin extraction; Segmentation algorithm; Deep learning; Image analysis; Food quality grading. |
Ano: 2019 |
URL: http://www.scielo.br/scielo.php?script=sci_arttext&pid=S0100-69162019000500639 |
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Yang,Jing; Zhu,Biao; Ni,Xiaolei; He,Yong. |
ABSTRACT Nitrogen (N) strongly affects plant growth. However, little is known about the effects of the ammonium/nitrate ratio on pakchoi (Brassica rapa), especially its glucosinolates (GSs) contents which are involved in plant defense and many of them benefit to human health. The aim of this study was to evaluate the effects of a constant N supply (8 mM) but with five ammonium/nitrate ratios (namely 0/8 mM, 2/6 mM, 4/4 mM, 6/2 mM and 8/0 mM) on the growth of pakchoi in a hydroponic system in 2 years. In both years, a higher biomass (dry weight) was in the 4/4 and 2/6 ammonium/nitrate treatments (2.3 and 2.2-fold compared to 8/0, respectively), with no significant difference in biomass between these two treatments. The biomass then decreased with increasing... |
Tipo: Info:eu-repo/semantics/article |
Palavras-chave: Brassica rapa; Biomass; Nitrogen form; Secondary metabolites. |
Ano: 2020 |
URL: http://www.scielo.br/scielo.php?script=sci_arttext&pid=S0102-05362020000300246 |
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