Modeling the seed yield of Ajowan (Trachyspermum ammi L.) using artificial neural network and multiple linear regression models

Industrial Crops and Products

Volumn 117, Issue , 2018, Pages 224-234

  Niazian, Mohsen ^a,b   Sadat Noori, Seyed Ahmad ^a   Abdipour, Moslem ^c  

^a UNIVERSITY OF TEHRAN   (Iran)

^b AGRICULTURAL BIOTECHNOLOGY RESEARCH INSTITUTE OF IRAN ABRII   (Iran)

^c Education and Extension Organization (AREEO)   (Iran)

Author keywords

Ajowan; Artificial intelligence; Medicinal plant; Multi layer perceptron; Selection criteria

Indexed keywords

LINEAR REGRESSION; MEAN SQUARE ERROR; NETWORK LAYERS; PLANTS (BOTANY);

AJOWAN; DRY WEIGHT; MEDICINAL PLANTS; MULTILAYERS PERCEPTRONS; MULTIPLE LINEAR REGRESSION MODELS; MULTIPLE REGRESSION MODELLING; ROOT MEAN SQUARE ERRORS; SEED YIELD; SELECTION CRITERIA; YIELD COMPONENTS;

MULTILAYER NEURAL NETWORKS;

ARTIFICIAL INTELLIGENCE; ARTIFICIAL NEURAL NETWORK; DRUG; HERB; MEDICINAL PLANT; MODELING; MULTIPLE REGRESSION; NUMERICAL MODEL; PLANT BREEDING;

TRACHYSPERMUM AMMI;

EID: 85043455365     PISSN: 09266690     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.indcrop.2018.03.013     Document Type: Article

References (48)

1. Aghbashlo, M., Mobli, H., Rafiee, S., Madadlou, A., The use of artificial neural network to predict exergetic performance of spray drying process: a preliminary study. Comput. Electron. Agric. 88 (2012), 32–43.
2. Anonymous, Neurosolutions V5.07 for Excel, Neurodimension. 2011 http://www.neurosolutions.com.
3. Arab, M.M., Yadollahi, A., Shojaeiyan, A., Ahmadi, H., Artificial neural network genetic algorithm as powerful tool to predict and optimize in vitro proliferation mineral medium for G × N15 rootstock. Front. Plant Sci., 7, 2016, 1526.
4. Ashraf, M., Orooj, A., Salt stress effects on growth, ion accumulation and seed oil concentration in an arid zone traditional medicinal plant ajwain (Trachyspermum ammi [L.] Sprague). J. Arid Environ. 64 (2006), 209–220.
5. Azamathulla, H.M., Ghani, A.A., Genetic programming for predicting longitudinal dispersion coefficients in streams. Water Res. Manage. 25 (2011), 1537–1544.
6. Baharlouei, A., Omid, M., Ahmadi, H., Rafiee, S.H., Predicting moisture content of Pistachio nuts (Akbari variety) with artificial neural network. Iran. J. Food Sci. Tech. 3:1 (2010), 45–56.
7. Bahmani, K., Izadi Darbandi, A., Ramshini, H.A., Moradi, N., Akbari, A., Agro-morphological and phytochemical diversity of various Iranian fennel landraces. Ind. Crops Prod. 77 (2015), 282–294.
8. Bhandari, M., Gupta, A., Variation and association analysis in coriander. Euphytica 58 (1991), 1–4.
9. Boskabady, M.H., Alitaneh, S., Alavinezhad, A., Carum copticum L.: a herbal medicine with various pharmacological effects. BioMed Res. Int., 2014, 11, 10.1155/2014/569087.
10. Cary, N.C., The SAS System for Windows. Release 9. 1. SAS Inst, North Carolina. 2004, SAS Institute.
11. Chegini, G.R., Khazaei, J., Ghobadian, B., Goudarzi, A.M., Prediction of process and product parameters in an orange juice spray dryer using artificial neural networks. J. Food Eng. 84:4 (2008), 534–543.
12. Chong, I.G., Jun, C.H., Performance of some variable selection methods when multicollinearity is present. Chemometr. Intell. Lab. Syst. 78:1 (2005), 103–112.
13. Cosge, B., Ipek, A., Gorbouz, B., Some phenotypic selection criteria to improve seed yield and essential oil percentage of sweet fennel (Foeniculum vulgare Mill var. Dulce). Tarim Bilimleri Dergisi 15:2 (2009), 127–133.
14. Dalkani, M., Darvishzadeh, R., Hassani, A., Correlation and sequential path analysis in Ajowan (Carum copticum L.). J. Med. Plant Res. 5:2 (2011), 211–216.
15. Dalkani, M., Hassani, A., Darvishzadeh, R., Determination of the genetic variation in Ajowan (Carum Copticum L.) populations using multivariate statistical techniques. Rev. Ciênc Agron. 43:4 (2012), 698–705.
16. Emamgholizadeh, S., Bateni, S.M., Jeng, D.S., Artificial intelligence-based estimation of flushing half-cone geometry. Eng. Appl. Artif. Intell. 26:10 (2013), 2551–2558.
17. Emamgholizadeh, S., Kashi, H., Marofpoor, I., Zalaghi, E., Prediction of water quality parameters of Karoon River (Iran) by artificial intelligence-based models. Int. J. Environ. Sci. Technol. 11:3 (2013), 645–656.
18. Emamgholizadeh, S., Parsaeian, M., Baradaran, M., Seed yield prediction of sesame using artificial neural network. Eur. J. Agron. 68 (2015), 89–96.
19. Erzin, Y., Rao, H., Singh, D., Artificial neural network models for predicting soil thermal resistivity. Int. J. Ther. Sci. Algor. 47 (2008), 1347–1358.
20. Fraser, J., Eaton, G.W., Application of yield component analysis to crop research. Field Crop Res. 39 (1983), 787–797.
21. Ghanshyam, N., Dodiya, S., Sharma, S.P., Jain, H.K., Dashora, A., Assessment of genetic variability, correlation and path analysis for yield and its components in ajwain (Trachyspermum ammi L.). J. Spices Aromatic Crop 24:1 (2015), 43–46.
22. Govindaraju, R.S., Artificial neural networks in hydrology. I: preliminary concepts. J. Hydrol. Eng. 5:2 (2000), 115–123.
23. Govindaraju, R.S., Artificial neural networks in hydrology. II: hydrologic applications. J. Hydrol. Eng. 5:2 (2000), 124–137.
24. Iquebal, M.A., Ansari, M.S., Sarika, S.P., Dixit, N.K., Aggarwal Verma, R.A.K., Jayakumar, S., Rai, A., Kumar, D., Locus minimization in breed prediction using artificial neural network approach. Stichting Int. Found. Anim. Gen. 45 (2014), 898–902.
25. Joshi, S.G., Medicinal Plants. 2000, Oxford and IBH Publishing Co. Pvt. Ltd., New Delhi, India 491 p.
26. Lal, R.K., Associations among agronomic traits and path analysis in fennel (Foeniculum vulgare Miller). J. Sustain. Agric. 30:1 (2007), 21–229.
27. Mansouri, A., Fadavi, A., Mortazavian, S.M.M., An artificial intelligence approach for modeling volume and fresh weight of callus-A case study of cumin (Cuminum cyminum L.). J. Theor. Biol. 397 (2016), 199–205.
28. Mansourian, S., Darbandi, E.I., Mohassel, M.H.R., Rastgoo, M., Kanouni, H., Comparison of artificial neural networks and logistic regression as potential methods for predicting weed populations on dryland chickpea and winter wheat fields of Kurdistan province, Iran. Crop Prot. 93 (2017), 43–51.
29. May, R.J., Dandy, G.C., Maier, H.R., Review of Input Variable Selection Methods for Artificial Neural Networks. 2011, InTech, Rijeka, Croatia 19–44 pp.
30. Mishra, R., Gupta, A.K., Lal, R.K., Jhang, T., Banerjee, N., Genetic variability, analysis of genetic parameters, character associations and contribution for agronomical traits in turmeric (Curcuma longa L.). Ind. Crops Prod. 76 (2015), 204–208.
31. Mohammadi Torkashvand, A., Ahmadi, A., Layegh Nikravesh, N., Prediction of kiwifruit firmness using fruit mineral nutrient concentration by artificial neural network (ANN) and multiple linear regressions (MLR). J. Integr. Agric. 16 (2017), 60345–60347.
32. Mokarram, M., Bijanzadeh, E., Prediction of biological and grain yield of barley using multiple regression and artificial neural network models. Aust. J. Crop Sci. 10:6 (2016), 895–903.
33. Moosavi, S. Gh., Seghatoleslami, M.J., Jouyban, Z., Ansarinia, E., Moosavi, S.A., Response morphological traits and yield of Ajowan (Carum copticum) to water deficit stress and nitrogen fertilizer. Biol. Forum. 7:1 (2015), 293–299.
34. Movagharnejad, K., Nikzad, M., Modelling of tomato drying using artificial neural network. Comp. Elect. Agric. 59 (2007), 78–85.
35. Naroui Rad, M.R., Koohkan, S., Fanaei, H.R., Pahlavan Rad, M.R., Application of Artificial Neural Networks to predict the final fruit weight and random forest to select important variables in native population of melon (Cucumis melo L.). Sci. Hortic. 181 (2015), 108–112.
36. Niazian, M., Sadat Noori, S.A., Galuszka, P., Tohidfar, M., Mortazavian, S.M.M., Genetic stability of regenerated plants via indirect somatic embryogenesis and indirect shoot regeneration of Carum copticum L. Ind. Crops Prod. 97 (2017), 330–3307.
37. Niazian, M., Sadat Noori, S.A., Tohidfar, M., Mortazavian, S.M.M., Essential oil yield and agro-morphological traits in some Iranian ecotypes of Ajowan (Carum copticum L.). J. Essent Oil Bear Pl 20:4 (2017), 1151–1156.
38. Niazian, M., Sadat-Noori, S.A., Abdipour, M., Tohidfar, M., Mortazavian, S.M.M., Image processing and artificial neural network-based models to measure and predict physical properties of embryogenic callus and number of somatic embryos in ajowan (Trachyspermum ammi (L.) Sprague). In Vitro Cell. Dev. Biol. Plant 54 (2018), 54–68.
39. Noori, S.A.S., Norouzi, M., Karimzadeh, G., Shirkool, K., Niazian, M., Effect of colchicine-induced polyploidy on morphological characteristics and essential oil composition of ajowan (Trachyspermum ammi L.). Plant Cell Tissue Organ Cult (PCTOC) 130 (2017), 543–551.
40. Panda, S., Ames, D., Panigrahi, S., Application of vegetation indices for agri-cultural crop yield prediction using neural network techniques. Remote Sens. 2 (2010), 673–696.
41. Quirk, T.J., Correlation and simple linear regression. Excel 2016 for Engineering Statistics, 2016, Springer International Publishing, 111–155.
42. Rosenblatt, Frank X., Principles of Neurodynamics: Perceptrons and the Theory of Brain Mechanisms. 1961, Cornell Aeronautical Lab Inc Buffalo NY. Spartan Books, Washington DC.
43. Safa, M., Samarasinghe, S., Nejat, M., Prediction of wheat production using artificial neural networks and investigating indirect factors affecting it case study in Canterbury Province, New Zealand. J. Agric. Sci. Tech. 17 (2015), 791–803.
44. Samadianfard, S., Nazemi, A.H., Ashraf Sadraddini, A., M5 model tree and gene expression programming based modeling of sandy soil water movement under surface drip irrigation. Agric. Sci. Dev. 3:5 (2014), 178–190.
45. Shahinfar, S., Mehrabani-Yeganeh, H., Lucas, C., Kalhor, A., Kazemian, M., Weigel, K.A., Prediction of Breeding Values for Dairy Cattle Using Artificial Neural Networks and Neuro-Fuzzy Systems. 2012, Hindawi Publishing Corporation Comput. Math. Methods Med., 1–9, 10.1155/2012/127130.
46. Silva, G.N., Tomaz, R.S., Sant'Anna, I.C., Nascimento, M., Bhering, L., Cruz, C.D., Neural networks for predicting breeding values and genetic gains. Sci. Agricola. 71:6 (2014), 494–498.
47. Tufail, M., Ormsbee, L.E., Teegavarapu, R., Artificial intelligence-based inductive models for prediction and classification of fecal coliform in surface waters. J. Environ. Eng. 134:9 (2008), 789–799.
48. Zhang, H., Hu, H., Zhang, X., Zhu, L., Zheng, K., Jin, Q., Zeng, F., Estimation of rice neck blasts severity using spectral reflectance based on BP-neural network. Acta Physiol. Plant. 33 (2011), 2461–2466.