|
|
| |
| Articles
by
M.R. Naghavi |
Total Records (
4 ) for
M.R. Naghavi |
|
 |
| |
|
| |
J. Khazaei
,
M.R. Naghavi
,
M.R. Jahansouz
and
G. Salimi-Khorshidi
|
| |
Crop growth is a multifactorial nonlinear process and different kinds of models have been developed to predict crop yield. In recent years, crop growth models have become increasingly important as major components of agriculture-related decision-support systems. Moreover, clustering is a multivariate analysis technique widely adopted in agricultural studies. Using this method, different genotypes (accessions) of crops can be classified and characterized. This paper discusses the use of soft computing techniques such as artificial neural networks (ANN) and fuzzy logic based approaches in regression and clustering problems. The ANN technology was used for modeling the correlation between crop yield and 10 yield components of chickpea (Cicer arietinum L.). Also, the fuzzy c-means (FCM) clustering technique was used for the classification of 362 chickpea genotypes based on their agronomic and morphological traits. The ANN performed very well. Among the various ANN structures, a model of good performance was produced by 10–14–3–1 structure with a training algorithm of back-propagation and hyperbolic tangent transfer function in the hidden and output layers. The model was able to predict the chickpea yield data of 0.32 to 14.38 g plant–1 with a RMSE and T value of 0.0195 g plant–1 and 0.988, respectively. T statistics measures the scattering around line (1:1). When T is close to 1.0, the fitting is desirable. The mean absolute error, relative error, and coefficient of determination between actual and predicted data were 0.0109 g plant–1, –1.07%, and 0.991, respectively. The ANN model predicted 90.3% of the yield data with relative errors ranging between ±5%. The consequent reduction in the number of training data from 250 to 50, decreased the training RMSE, but increased the prediction error. It was found that even with a few number of patterns in the training dataset (50 patterns), the prediction error of the ANN model was in the range of acceptance for yield modeling. Obviously, with 25 x 103 iterations, the ANN models with 5 and 10 input variables gave almost the same estimation of the chickpea yield. The results of clustering showed that the FCM clustering technique can be successfully applied to classify chickpea genotypes in terms of agronomic and morphological traits. |
|
|
| |
|
| |
M. Motalebi
,
M. Keshavarzi
and
M.R. Naghavi
|
| |
Twenty-five accessions of Triticum durum originating from different geographical areas of Iran and 10 durum cultivars from European countries were evaluated for high molecular weight glutenin subunit (HMW-GS) composition using SDS-PAGE. The data indicated the prevalence of the null allele (52%) and 2* subunit (48%) at the Glu-A1 and four alleles, namely 14+15 (32%), 13+16 (28%) and 7+20 (24%) represented about 80% of the alleles at Glu-B1 locus in the Iranian durum compared to European durum. This information can be a valuable reference for designing breeding program for the improvement of breed and pasta making quality of bread and durum wheat, respectively in Iran.
|
|
|
| |
|
| |
M. Ranjbar
,
M.R. Naghavi
,
A. Zali
and
M.J. Aghaei
|
| |
Abstract: Principal component and cluster analyses were used to evaluate the pattern of morphological variation in 122 accessions of Aegilops crassa for 14 quantitative characters. With the principal component analysis, the first five principal components with eigenvalues more than 1 contributed 69.5% of the variability amongst accessions, whereas PC6 to PC14 were less than unity. Plant height, stem diameter, spike length and number of spikelets per spike were the most important characters in the first principal component. The germplasm was grouped into five clusters using cluster analysis. Although each cluster had some specific characteristics of its own, but clusters were not clearly separated when plotted by the first two principal components. Mahalanobis distances (D2) determined that plant height, stem diameter, spike length, number of spikelets per spike, node width, seed length, seed width and flowering date characters as the most important characters in differentiating the accessions. The morphological variation of Aegilops crassa accessions obtained in this study provides useful information for the future collection and makes these genetic resources more accessible to breeders. |
|
| |
|
| |
M. Farkhari
,
M.R. Naghavi
,
S.A. Pyghambari
and
Sabokdast
|
| |
Genetic variation of 28 populations of jointed goatgrass (Aegilops cylindrica Host.), collected from different parts of Iran, were evaluated using both RAPD-PCR and SDS-PAGE of seed proteins. The diversity within and between populations for the three-band High Molecular Weight (HMW) subunits of glutenin pattern were extremely low. Out of 15 screened primers of RAPD, 14 primers generated 133 reproducible fragments which among them 92 fragments were polymorphic (69%). Genetic similarity calculated from the RAPD data ranged from 0.64 to 0.98. A dendrogram was prepared on the basis of a similarity matrix using the UPGMA algorithm and separated the 28 populations into two groups. Confusion can happen between populations with the same origin as well as between populations of very diverse geographical origins. Our results show that compare to seed storage protein, RAPD is suitable for genetic diversity assessment in Ae. cylindrica populations. |
|
|
|
|
| |
|
