scholarly journals Análisis Prospectivo de la Detección Hiperespectral de Cultivos de Arroz (Oryza Sativa L.)

2018 ◽  
Vol 3 (1) ◽  
pp. 69
Author(s):  
Jorge Serrano ◽  
José Fábrega ◽  
Evelyn Quirós ◽  
Javier Sánchez- Galán ◽  
José Ulises Jiménez
Keyword(s):  
Principal Component Analysis ◽  
Oryza Sativa ◽  
Oryza Sativa L ◽  
Principal Component ◽  
Field Measurements ◽  
Spectral Signature ◽  
Prospective Analysis ◽  
Rice Cultivars ◽  
Crop Varieties ◽  
Nm Range

The objective of this work is to perform a prospective analysis of the wavelengths that can be used to recognize a rice crop due to its phenological status and variety. For this purpose, field measurements of spectral signature in the 350 nm -1049 nm range were employed. The rice cultivars FCA 616FL and IDIAP 54-05 were used. The study site was located in the Juan Hombrón area in the Coclé province, Panama. A principal component analysis (PCA) was carried out, which resulted in the lengths 728.16, 677.89 and 785.48 nm let phenological differentiation within the cultivar FCA 616FL and IDIAP 54-05, the lengths 729.72, 814.58 and 666.81 nm let distinguish between crop varieties FCA 616FL and IDIAP 54-05 in vegetative phase.Keywords: Rice, reflectance, hyperspectral signature, phonological state.

scholarly journals Assessment of Morphometric Diversity for Yield and Yield Attributing Traits in Rice (Oryza sativa L.) for Tolerance to Heat Stress

2020 ◽  
pp. 29-49
Author(s):  
S. Sandeep ◽  
M. Sujatha ◽  
L. V. Subbarao ◽  
C. N. Neeraja
Keyword(s):  
Principal Component Analysis ◽  
Oryza Sativa ◽  
Heat Stress ◽  
Oryza Sativa L ◽  
Pollen Viability ◽  
Principal Component ◽  
Component Analysis ◽  
Spikelet Fertility ◽  
Breeding Programmes ◽  
Heat Tolerant

The present investigation entitled “Assessment of morphometric diversity for yield and yield attributing traits in rice (Oryza sativa L.) for tolerance to heat stress” was carried out with objective of assessing genetic divergence in 200 germplasm of rice for eleven characters at ICRISAT, Patencheru, Hyderabad. The genotypes were grouped into fifteen clusters in Tocher’s method, cluster analysis and principal component analysis, out of the 11 characters studied, number of grains per panicle, plant height, pollen viability and spikelet fertility contributed 96.73 per cent of the total divergence and these traits were found to be important potent factors for genetic differentiation in genotypes. Principal component analysis identified five principal components, which contributed for 78.66 percent % of cumulative variance. The overall results of the study revealed that crossing using the genotypes under cluster V and XI and cluster XI and XIII could be exploited by hybridization programme to yield good recombinants because they had maximum inter cluster distance and possessing high genetic diversity for the characters viz. panicle length, number of grains per panicle and single plant yield. The genotypes of cluster I, II, IV, VI, VII, VIII, XI, XII and XIII showed high spikelet fertility percentage. Hence the genotypes of these clusters can be used in breeding programmes for development of heat tolerant varieties. Euclidean2 method indicated that genotypes of cluster III and IX exhibited high spikelet fertility percentage which can be utilized in development of heat tolerant cultivars. The results of principal component analysis revealed that genotypes of cluster I, cluster IV, cluster V, cluster VIII, cluster IX, cluster XI, cluster XII and cluster XV exhibited highest spikelet fertility percentage. Hence, the genotypes of the clusters can be used in breeding programmes for the development of heat tolerant varieties. 

scholarly journals GENETIC DIVERSITY AND PARENTS SELECTION FROM EXOTIC RICE (Oryza sativa L.) GERMPLASM

2012 ◽  
Vol 25 (1) ◽  
pp. 11-16
Author(s):  
A. A. Mamun ◽  
N. A. Ivy ◽  
M. G. Rasul ◽  
M. M. Hossain
Keyword(s):  
Genetic Diversity ◽  
Principal Component Analysis ◽  
Cluster Analysis ◽  
Oryza Sativa ◽  
Oryza Sativa L ◽  
Principal Component ◽  
Component Analysis ◽  
Rice Genotypes ◽  
Total Divergence ◽  
Five Axes

Genetic divergence among fifty exotic rice genotypes along with two check varieties were estimated using D2 and principal component analysis. The study was undertaken to select suitable donor parents for use in improved breeding program of Bangabandhu Sheikh Mujibur Rahman Agricultural University in 2009. Principal component analysis (PCA) revealed that the first five axes accounted for 58.10% of the total variation. As per cluster analysis, the genotypes were grouped into seven clusters consisting 11, 16, 7, 11, 1, 2 and 4 genotypes which revealed that there exist considerable diversity among the genotypes. Considering the magnitude of genetic distance, contribution of different characters towards the total divergence and magnitude of cluster means for different characters, the genotypes RG-BU-08-057, 61, 65, 67, 69, 71, 85, 86, 88, 94, 96, 98 and 99 might be selected as a suitable parent for future hybridization program.DOI: http://dx.doi.org/10.3329/bjpbg.v25i1.17007

scholarly journals Pengenalan Pola Daun untuk Membedakan Tanaman Padi dan Gulma Menggunakan Metode Principal Components Analysis (PCA) dan Extreme Learning Machine (ELM)

2020 ◽  
Vol 1 (1) ◽  
pp. 44-51
Author(s):  
Ahmad Izzuddin ◽  
M. Rizal Wahyudi
Keyword(s):  
Principal Component Analysis ◽  
Oryza Sativa ◽  
Principal Components Analysis ◽  
Extreme Learning Machine ◽  
Principal Components ◽  
Oryza Sativa L ◽  
Principal Component ◽  
Alternanthera Sessilis ◽  
Components Analysis ◽  
Learning Machine

Perkembangan ilmu pengetahuan serta pesatnya teknologi memberikan banyak manfaat bagi manusia dalam menjalankan aktifitasnya. Pemanfaatan ilmu pengetahuan dan teknologi tersebut di berbagai bidang termasuk di bidang pertanian. Pengembangan potensi pertanian suatu daerah dapat dioptimalkan melalui perkembangan ilmu pengetahuan dan teknologi itu sendiri. Salah satunya dengan pengenalan pola citra digital. Pengenalan pola bertujuan menentukan kelompok atau kategori pola berdasarkan ciri-ciri yang dimiliki oleh pola tersebut. Dengan kata lain, pengenalan pola membedakan suatu objek dengan objek lain. Dengan menggunakan metode ektraksi ciri Principal Component Analysis dan metode klasifikasi Extreme Learning Machine penulis melakukan penelitian untuk membedakan tanaman padi dan tanaman gulma. Implementasi PCA dan ELM mampu membedakan tanaman gulma dengan padi (Oryza sativa L) dalam hal ini gulma yang digunakan adalah jawan (Echinochloa cruss-galli) dan kremah (Alternanthera sessilis). Berdasarkan hasil pengujian yang dilakukan 8 kali running dengan merubah jumlah hidden neuron diperoleh nilai akurasi paling tinggi sebesar 91,67 % dengan menggunakan 10, 15, 30, 35, 40 hidden neuron, sedangkan untuk nilai akurasi paling rendah sebesar 58% dengan jumlah hidden neuron 5. Waktu yang dibutuhkan ELM untuk melakukan pelatihan dan pengujian sangat singkat 0.374 detik dan 0.500 detik pengukuran dilakukan dimulai dari running program sampai proses running program selesai.

scholarly journals Genetic Diversity of Drought Tolerant Upland Rice (Oryza sativa L.) Genotypes

2016 ◽  
Vol 13 (2) ◽  
pp. 133-139 ◽  
Author(s):  
S Hossain ◽  
M Salim ◽  
M S Akter ◽  
S Afroz ◽  
M S Noman
Keyword(s):  
Genetic Diversity ◽  
Principal Component Analysis ◽  
Oryza Sativa ◽  
Genetic Divergence ◽  
Upland Rice ◽  
Oryza Sativa L ◽  
Principal Component ◽  
Drought Tolerant ◽  
Cluster Distance ◽  
Total Divergence

Genetic divergence of thirty three drought tolerant rice (Oryza sativa L.) genotypes were studied through Mohalanobis’s D2 and principal component analysis for twelve characters. The genotypes were grouped into seven clusters. The cluster I and II were comprised of the maximum number of genotypes (eight) in each followed by cluster V containing five genotypes. The highest inter-cluster distance was in between cluster III and I (368.64) indicating a wide genetic diversity between these two clusters followed by clusters VII and III (346.04). The lowest inter-cluster distance was in between cluster IV and II (42.46) followed by cluster VI and IV (63.29) indicating that the genotypes of these clusters were genetically close. The intra cluster distance in the entire seven clusters was less, which indicate that the genotypes within the same clusters were closely related. Considering the magnitude of genetic distance, contribution of different characters towards the total divergence and magnitude of cluster means for different characters performance, BR 7391-2B-7, BR 7392-2B-25, BR 6855-2B-11-3-4, BR 6855-2B-11-3-5  from cluster I, genotypes BR 6976-2B-15, and Morichbati from cluster III, genotypes BR 7187-2B-2-5 from cluster IV, genotype BR 7187-2B-2-3 and BR 7181-2B-35-2 from cluster V and genotypes BR 6855-2B-11-3-7 and BRRI dhan 42 from the cluster VII are likely to perform better if used in hybridization program.The Agriculturists 2015; 13(2) 133-139

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