scholarly journals Multiscale Contour Steered Region Integral and Its Application for Cultivar Classification

IEEE Access ◽  
2019 ◽  
Vol 7 ◽  
pp. 69087-69100 ◽  
Author(s):  
Xiaohan Yu ◽  
Yongsheng Gao ◽  
Shengwu Xiong ◽  
Xiaohui Yuan
Keyword(s):  
Cultivar Classification

Cultivar classification of Apulian olive oils: Use of artificial neural networks for comparing NMR, NIR and merceological data

2017 ◽  
Vol 219 ◽  
pp. 131-138 ◽  
Author(s):  
Giulio Binetti ◽  
Laura Del Coco ◽  
Rosa Ragone ◽  
Samanta Zelasco ◽  
Enzo Perri ◽  
...  
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Olive Oils ◽  
Cultivar Classification ◽  
Artificial Neural

Machine learning for cultivar classification of apricots (Prunus armeniaca L.) based on shape features

2019 ◽  
Vol 256 ◽  
pp. 108524 ◽  
Author(s):  
Xi Yang ◽  
Ruoyu Zhang ◽  
Zhiqiang Zhai ◽  
Yujie Pang ◽  
Zuohui Jin
Keyword(s):  
Machine Learning ◽  
Prunus Armeniaca ◽  
Shape Features ◽  
Cultivar Classification ◽  
Prunus Armeniaca L

scholarly journals Flesh of pumpkin from ecological farming as part of fruit suitable for non-destructive cultivar classification using computer vision

2021 ◽  
Author(s):  
Ewa Ropelewska ◽  
Wioletta Popińska ◽  
Kadir Sabanci ◽  
Muhammet Fatih Aslan
Keyword(s):  
Computer Vision ◽  
Outer Surface ◽  
Color Space ◽  
Discriminative Models ◽  
Ecological Farming ◽  
Cultivar Classification ◽  
The Individual ◽  
Non Destructive ◽  
Total Accuracy ◽  
Color Channels

AbstractThe aim of this study was to build the discriminative models for distinguishing the different cultivars of flesh of pumpkin ‘Bambino’, ‘Butternut’, ‘Uchiki Kuri’ and ‘Orange’ based on selected textures of the outer surface of images of cubes. The novelty of research involved the use of about 2000 different textures for one image. The highest total accuracy (98%) of discrimination of pumpkin ‘Bambino’, ‘Butternut’, ‘Uchiki Kuri’ and ‘Orange’ was determined for models built based on textures selected from the color space Lab and the IBk classifier and some of the individual cultivars were classified with the correctness of 100%. The total accuracy of up to 96% was observed for color space RGB and 97.5% for color space XYZ. In the case of color channels, the total accuracies reached 91% for channel b, 89.5% for channel X, 89% for channel Z.

Cultivar classification inTulipaL. (Liliaceae)

1996 ◽  
Vol 45 (2) ◽  
pp. 183-198 ◽  
Author(s):  
L. W. D. VAN RAAMSDONK ◽  
T. DE VRIES
Keyword(s):  
Cultivar Classification

Multivariate analysis of the temporal variability of sugarcane ripening in south-eastern Brazil

2014 ◽  
Vol 65 (3) ◽  
pp. 300 ◽  
Author(s):  
Nilceu P. Cardozo ◽  
Paulo C. Sentelhas ◽  
Alan R. Panosso ◽  
Antonio S. Ferraudo
Keyword(s):  
Principal Components ◽  
Climatic Conditions ◽  
Quality Data ◽  
Clustering Methods ◽  
Local Climate ◽  
Eastern Brazil ◽  
Local Soil ◽  
Cultivar Classification ◽  
Early Ripening

Sugarcane ripening is a process controlled by cultivar characteristics and the interaction of genotypes with local climate. The objective of this study was to characterise the temporal variation of sugarcane ripening by assessing the multivariate structure contained in sugarcane quality data, and by correlating the results with local climatic conditions. Eight sugarcane cultivars were evaluated from March to October in Piracicaba, São Paulo State, Brazil. Characteristics related to the quality of raw sugarcane juice were submitted to statistical analysis by ANOVA, hierarchical and non-hierarchical (k-means) clustering methods, and principal components, in order to classify the cultivars into groups for each month of sampling. The ANOVA showed a clear difference (P < 0.001) among harvesting months for all sugarcane quality variables, which was reinforced by the cluster analysis for the whole dataset that selected groups according to the month of harvest. By analysing the quality variables by months, patterns of similarity among sugarcane cultivars were identified, which allowed three ripening groups to be established: early, middle and late. As the harvesting season progressed, the variations within each group, as well as among groups, were explained mainly by local soil-water availability conditions. The early ripening cultivars showed polarisable sugar (Pol) values >13% in early May, whereas these values were reached by the middle cultivars in July, and the late ones in August–September. However, the differences among groups tended to decrease through the harvest season, as expressed by the Euclidean distance, which decreased from 5.62 in March to 1.82 in September, when the water deficit reached the maximum accumulated value, totalling >130 mm. The non-hierarchical analyses (k-means) and principal components methods agreed, resulting in the identification of the same three main cultivar groups. The approach proposed for cultivar classification in this study is more complete than the usual analysis of Pol variation over time, since it allowed all of the variability contained in the sugarcane quality dataset to be analysed in an integrated way, providing a better understanding of the differences observed in the ripening of different cultivars.

scholarly journals Understanding Soybean Maturity Groups in Brazil: Environment, Cultivar Classification, and Stability

Crop Science ◽  
2009 ◽  
Vol 49 (3) ◽  
pp. 801-808 ◽  
Author(s):  
Luís Fernando Alliprandini ◽  
Claudiomir Abatti ◽  
Paulo Fernando Bertagnolli ◽  
José Elzevir Cavassim ◽  
Howard Lewis Gabe ◽  
...  
Keyword(s):  
Cultivar Classification ◽  
Maturity Groups

scholarly journals Morphological, physiological, pests and diseases responses of citrus seedling cultivars, and their contribution to cultivar classification under nursery house and open field

2021 ◽  
pp. 370
Author(s):  
Hardiyanto . ◽  
Nirmala F. Devy ◽  
S. Susanto ◽  
A. Sugiyatno ◽  
ME Dwiastuti ◽  
...  
Keyword(s):  
Open Field ◽  
Aphis Gossypii ◽  
Physiological Traits ◽  
Leaf Miner ◽  
Rice Hull ◽  
Average Percentage ◽  
Pests And Diseases ◽  
Plastic Bags ◽  
Cultivar Classification ◽  
Morphological And Physiological Traits

Information of morphological, physiological, and pests and diseases traits between Siam or Tangerine (C. nobilis L.) and Keprok or Mandarin (C. sinensis) seedlings group under nursery and open filed condition in Indonesia has limited. The contribution of morphological and physiological characters to cultivar classification of Siam and Mandarin group has also not been yet documented. The aims of this research were to evaluate the morphological, physiological, pests and diseases responses of citrus seedling cultivars, and their contribution to cultivar classification. This research was conducted at Tlekung Experimental Garden, Citrus and Subtropical Fruits Research Institute, Batu, East Java, Indonesia from February to December 2020. One-year-old budded seedlings were planted in plastic bags (size 15x30 cm) and put in both a nursery house and open field.  The plastic bags were filled with mixed media (rice hull, soil, and compost) with the ratio of 1:1:1. The experimental design was a Two Stage Nested Design consisted of two factors, these were factor A: locations (nursery house and open field) and factor B as a nested-on factor A: cultivars (Siam cv. Pontianak, Siam cv. Banjar, Siam cv. Madu, Keprok cv. Kacang, Keprok cv. Terigas, Keprok cv. Madura and Keprok cv. Gayo). The results showed that the highest flush growth percentage was showed by Keprok cv. Madura grown under open field condition, while the biggest rootstock diameter was obtained from Siam cv. Madu grown in nursery house.  Siam cv. Madu grown in open field also produced the highest root dry weight and stomata density. In terms of pests and diseases, aphids (Aphis gossypii) and leaf miner (Phyllocnistis citrella) have only been affected by locations, while for diseases was not found in this study. Contribution of morphological and physiological traits to citrus cultivars classification were about 64.70%. The average percentage of change in growth and develop capacity of Keprok group from open field to the nursery house increased by 2.35%, whereas for Siam one tended to decrease by 8.96%. In general, responses of morphological and/or physiological traits between Siam and mandarin group two locations were varied. Morphological and physiological traits may also useful for supporting genetically evaluation in improving citrus breeding programs.

scholarly journals Applicability of Leaf Morphology and Intersimple Sequence Repeat Markers in Classification of Tree Peony (Paeoniaceae) Cultivars

HortScience ◽  
2005 ◽  
Vol 40 (2) ◽  
pp. 329-334 ◽  
Author(s):  
Zhi-li Suo ◽  
Wen-ying Li ◽  
Juan Yao ◽  
Hui-jin Zhang ◽  
Zhi-ming Zhang ◽  
...  
Keyword(s):  
Leaf Morphology ◽  
Environmental Influences ◽  
Morphological Characteristics ◽  
Flower Color ◽  
Issr Markers ◽  
Middle Part ◽  
Sequence Repeat ◽  
Tree Peony ◽  
Cultivar Classification ◽  
Flower Form

Tree peony cultivars are usually classified according to flower characteristics (flower form and flower color) which are commonly affected by environmental influences and developmental levels. Judgment of flower forms may also depend on the observer. Precise and rapid cultivar identification methods are also required to manage cultivar collections as well as tree peony breeding programs. The objective of this paper is to analyze the discriminatory ability of leaf morphology and Intersimple sequence repeat (ISSR) marker systems for tree peony cultivars. As a result, although there exist large variations of leaf morphology of tree peony cultivars, the morphological characteristics of biternately compound leaves 3, 4, and 5 from the base of a shoot at the middle part of a plant are relatively stable with smaller variations within cultivars (2.7% to 27.1%, 16.8% on average) and with larger differentiations among cultivars (72.9% to 97.3%, 83.2% on average). Statistical and principal components analyses indicate that 12 leaf morphological characteristics are valuable for cultivar classification. ISSR markers present a precisely discriminatory power in tree peony cultivar classification without environmental influences. The cultivars with multiple flower forms, which makes it difficult to make judgment by means of a flower-form-based classification system, have been significantly characterized using leaf morphology or ISSR markers.

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