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Gholipoor, M., Rohani, A., Torani, S.. (1391). Optimization of traits to increasing barley grain yield using an artificial neural network. , 7(1), 1-18. doi: 10.22069/ijpp.2012.918
M. Gholipoor; A. Rohani; S. Torani. "Optimization of traits to increasing barley grain yield using an artificial neural network". , 7, 1, 1391, 1-18. doi: 10.22069/ijpp.2012.918
Gholipoor, M., Rohani, A., Torani, S.. (1391). 'Optimization of traits to increasing barley grain yield using an artificial neural network', , 7(1), pp. 1-18. doi: 10.22069/ijpp.2012.918
Gholipoor, M., Rohani, A., Torani, S.. Optimization of traits to increasing barley grain yield using an artificial neural network. , 1391; 7(1): 1-18. doi: 10.22069/ijpp.2012.918

Optimization of traits to increasing barley grain yield using an artificial neural network

International Journal of Plant Production
مقاله 2، دوره 7، شماره 1، فروردین 2013، صفحه 1-18    اصل مقاله (200.9 K)
نوع مقاله: Research Paper
شناسه دیجیتال (DOI): 10.22069/ijpp.2012.918
نویسندگان
M. Gholipoor* 1؛ A. Rohani2؛ S. Torani3
1Department of Crop Sciences, Shahrood University of Technology, P.O. Box 36155-316, Shahrood, Iran
2bDepartment of Farm Machinery Engineering, Shahrood University of Technology, P.O. Box 36155-316, Shahrood, Iran
3aDepartment of Crop Sciences, Shahrood University of Technology, P.O. Box 36155-316, Shahrood, Iran
چکیده
The grain yield (Y) of crops is determined by several Y components that reflect
positive or negative effects. Conventionally, ordinary Y components are screened for
the highest direct effect on Y. Increasing one component tends to be somewhat
counterbalanced by a concomitant reduction in other component (s) due to
competition for assimilates. Therefore, it has been suggested that components be
manipulated in conjunction with other traits to break the competition-resulting
barrier. The objective of this study is to optimize the effective components in
conjunction with certain participant traits for increased barley Y using an artificial
neural network (ANN) and a genetic algorithm (GA) as an alternative procedure.
Two field experiments were carried out separately at the Agriculture Research
Station located in Gonbade Kavous (37o16' N, 55o12' E and 37 asl), Iran. Ten
genotypes were grown in each experiment, and the Y and certain traits/components
were measured. Among the components/traits, those with a significant direct effect
and/or correlation with Y were selected as effective for the ANN and GA analysis.
The results indicate that the remobilization of stored pre-anthesis assimilates to grain
(R1), crop height (R2), 1,000-grains weight (R3), grain number per ear (R4),
vegetative growth duration (R5), grain-filling duration (R6), grain-filling rate (R7),
and tiller number (R8) were effective. The R2 for the training and test phases was
0.99 and 0.94, respectively, which reveals the capability of the ANN to predicting Y.
The optimum values obtained by GA were 14.2%, 104.34 cm, 36.9 g, 41.9, 100 d, 48
d, 1.22 mg seed-1 day-1, and 3.38 plant-1 for R1 through R8, respectively. The
optimization increased the potential Y to 5791 kg ha-1, which was higher than that
observed for the genotypes (3527 to 5163 kg ha-1).
کلیدواژه‌ها
barley؛ Grain yield؛ Yield components؛ Genetic Algorithm؛ Artificial neural network
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