Abdipour, M., Younessi-Hmazekhanlu, M., Ramazani, S.H.R. and omidi, A.H. 2019. Artificial neural
networks and multiple linear regression as potential methods for modeling seed yield of safflower
(Carthamus tinctorius L.). Ind. Crop Prod. 127: 185-194.
Abrougui, K., Gabsi, K., Mercatoris, B., Khemis, C., Amami, R. and Chehaibi, S. 2019. Prediction of
organic potato yield using tillage systems and soil properties by artificial neural network (ANN) and
multiple linear regressions (MLR). Soil Till. Res. 190: 202-208.
Alvarez, R. 2009. Predicting average regional yield and production of wheat in the Argentine Pampas by
an artificial neural network approach. Eur. J. Agro. 30: 70-77.
Anouar, F., Mannino, M., Casals, M., Fougereux, J. and Demilly, D. 2001. Carrot seeds grading using a
vision system. Seed Sci. Thechnol. 29: 215-225.
Anysz, H., Zbiciak, A. and Ibadov, N. 2016. The influence of input data standardization method on
prediction accuracy of artificial neural networks. Procedia. Eng. 153: 66-70.
Arefi, A., Motlagh, A.M. and Khoshroo, A. 2011. Recognition of weed seed species by image processing.
J. Food Agric. Environ. 9: 379-383.
Bagheri, A., Eghbali, L. and Sadrabadi Haghighi, R. 2019. Seed classification of three species of
amaranth (Amaranthus spp.) using artificial neural network and canonical discriminant analysis. J.
Agric. Sci. 157: 333-341.
Bagheri, M., Rashed Mohassel, M. and Golzarian, M. 2015. Comparison of the efficiency of three types
of artificial neural networks in identifying seeds of twenty weed species. Iranian J. Weed Ecol. 3: 31-
39.
Cervantes, E., Martín, J.J. and Saadaoui, E. 2016. Updated methods for seed shape analysis. Scientifica.
10.
Chtioui, Y., Bertrand, D. and Barba, D. 1998. Feature selection by a genetic algorithm. Application to
seed discrimination by artificial vision. J. Sci. Food Agric. 76: 77-86.
Chtioui, Y., Bertrand, D., Dattée, Y. and Devaux, M.F. 1996. Identification of seeds by colour imaging:
Comparison of discriminant analysis and artificial neural network. J. Sci. Food Agric. 71: 433-441.
Dubey, B., Bhagwat, S., Shouche, S. and Sainis, J. 2006. Potential of artificial neural networks in varietal
identification using morphometry of wheat grains. Biosyst. Eng: 95, 61-67.
Eghbali, L., Sadrabadi Haghighi, R., Moein Rad, H. and Bagheri, A. 2013. Identification of seeds of
different species of Amaranthus spp. Using machine vision approach and artificial neural networks.
Iranian J. Seed Res. 2: 74-85.
Eizenga, G.C., Ali, M., Bryant, R.J., Yeater, K.M., McClung, A.M. and McCouch, S.R. 2014.
Registration of the rice diversity panel 1 for genomewide association studies. J. Plant Regist. 8: 109-
116.
Elizondo, D., McClendon, R. and Hoogenboom, G. 1994. Neural network models for predicting
flowering and physiological maturity of soybean. Trans. ASAE. 37: 981-988.
Granitto, P.M., Navone, H.D., Verdes, P.F. and Ceccatto, H.A. 2002. Weed seeds identification by
machine vision. Comput. Electron. Agric. 33: 91-103.
Hoyo, Y. and Tsuyuzaki, S. 2013. Characteristics of leaf shapes among two parental Drosera species and
a hybrid examined by canonical discriminant analysis and a hierarchical Bayesian model. Am. J. Bot.
100: 817-823.
Kasabov, N.K. 1996. Foundations of neural networks, fuzzy systems, and knowledge engineering. The
MIT Press.
Kaul, M., Hill, R.L. and Walthall, C. 2005. Artificial neural networks for corn and soybean yield
prediction. Agric. Syst. 85: 1-18.
Lippmann, R. 1994. Book Review:" Neural Networks, A Comprehensive Foundation", by Simon Haykin.
Int. J. Neural Syst. 5: 363-364.
Melesse, A.M. and Hanley, R.S. 2005. Artificial neural network application for multi-ecosystem carbon
flux simulation. Ecol. Modell. 189: 305-314.
Nemes, A., Schaap, M. and Wösten, J. 2003. Functional evaluation of pedotransfer functions derived
from different scales of data collection. Soil Sci. Soc. Am. J. 67: 1093-1102.
Olesen, M.H., Carstensen, J.M. and Boelt, B. 2011. Multispectral imaging as a potential tool for seed
health testing of spinach (Spinacia oleracea L.). Seed Sci. Technol. 39: 140-150.
Pourreza, A., Pourreza, H., Abbaspour-Fard, M.-H. and Sadrnia, H. 2012. Identification of nine Iranian
wheat seed varieties by textural analysis with image processing. Comput. Electron. Agric. 83: 102-
108.
Rahmani, E., Khalili, A. and Liaghat, A. 2008. Quantitative Survey of Drought Effects on Barley Yield in
East Azerbaijan by Classical Statistical Methods. J. Sci. Technol. Agric. Nat. Resour.
Shahin, M. and Symons, S. 2003. Lentil type identification using machine vision. Can. Biosyst. Eng. 45:
3.5-3.11.
Shrestha, S., Deleuran, L.C., Olesen, M.H. and Gislum, R. 2015. Use of multispectral imaging in varietal
identification of tomato. Sensors. 15: 4496-4512.
Somaratne, S., Seneviratne, G. and Coomaraswamy, U. 2005. Prediction of soil organic carbon across
different land-use patterns. Soil Sci. Soc Am. J. 69: 1580-1589.
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