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Salehzadeh, H., Gholipoor, M., Abbasdokht, H., Baradaran, M.. (1394). Optimizing plant traits to increase yield quality and quantity in tobacco using artificial neural network. , 10(1), 97-108. doi: 10.22069/ijpp.2016.2556
H. Salehzadeh; M. Gholipoor; H. Abbasdokht; M. Baradaran. "Optimizing plant traits to increase yield quality and quantity in tobacco using artificial neural network". , 10, 1, 1394, 97-108. doi: 10.22069/ijpp.2016.2556
Salehzadeh, H., Gholipoor, M., Abbasdokht, H., Baradaran, M.. (1394). 'Optimizing plant traits to increase yield quality and quantity in tobacco using artificial neural network', , 10(1), pp. 97-108. doi: 10.22069/ijpp.2016.2556
Salehzadeh, H., Gholipoor, M., Abbasdokht, H., Baradaran, M.. Optimizing plant traits to increase yield quality and quantity in tobacco using artificial neural network. , 1394; 10(1): 97-108. doi: 10.22069/ijpp.2016.2556

Optimizing plant traits to increase yield quality and quantity in tobacco using artificial neural network

International Journal of Plant Production
مقاله 9، دوره 10، شماره 1، فروردین 2016، صفحه 97-108    اصل مقاله (714.79 K)
نوع مقاله: Research Paper
شناسه دیجیتال (DOI): 10.22069/ijpp.2016.2556
نویسندگان
H. Salehzadeh1؛ M. Gholipoor* 2؛ H. Abbasdokht3؛ M. Baradaran3
1PhD student, Department of Crop Sciences, Shahrood University, P.O. Box 36155-316, Shahrood, Iran.
2Faculty member, Department of Crop Sciences, Shahrood University, P.O. Box 36155-316, Shahrood, Iran.
3Faculty member, Department of Crop Sciences, Shahrood University, P.O. Box 36155-316, Shahrood, Iran
چکیده
There are complex inter- and intra-relations between regressors (independent variables) and
yield quantity (W) and quality (Q) in tobacco. For instance, nitrogen (N) increases W but
decreases Q; starch harms Q but soluble sugars promote it. The balance between (optimization
of) regressors is needed for simultaneous increase in W and Q components [higher potassium
(K), medium nicotine and lower chloride (Cl) contents in cured leaf]. This study was aimed to
optimize 10 regressors (content of N and soluble sugars in root, stem and leaf, leaf nicotine
content at flowering and nitrate reductase activity (NRA) at 3 phenological stages) for increased
W and Q components, using an artificial neural network (ANN). Two field experiments were
conducted to get diversified regressors, Q and W, using 2 N sources and 4 application patterns
in Tirtash and Oromieh. Treatments and 2 locations produced a wide range of variation in
regressors, W and Q components which is prerequisite of ANN. The results indicated that
configuration of 12 neurons in one hidden layer was the best for prediction. The obtained
optimum values of regressors (1.64%, 2.12% and 1.04% N content, 4.32%, 13.04% and 9.54%
soluble sugar content for leaf, stem and root, respectively; 2.31% nicotine content and NRA of
13.11, 4.74 and 4.70 µmol.NO2.g-1.h-1 for pre-flowering, flowering and post-flowering stages,
respectively) increased W by 3% accompanied by 4.75% K, 1.87% nicotine and 1.5% Cl
in cured leaf.

کلیدواژه‌ها
Artificial neural network؛ Optimization؛ Tobacco؛ quality
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