Abdanan Mehdizadeh, S. & Banhazi, T.M. (2015). Evaluating droplet distribution of spray-nozzles for dust reduction in livestock buildings using machine vision. International Journal of Agricultural and Biological Engineering, 8(5), 58-64.
Abdanan Mehdizadeh, S., Nouri, M., Soltani Kazemi, M. & Amraei, S. (2016). None-destructive investigation of the quality factors in citrus juice during storage using digital image processing. Iranian Food Science and Technology Research Journal (In Press). (In Farsi)
Abdanan Mehdizadeh, S., Sandell, G., Golpour, A. & Karimi Torshizi, M.A. (2014). Early determination of Pharaoh Quail sex after hatching using machine vision. Bulletin of Environment. Pharmacology and Life Sciences, 1, 105–114.
Abdolahzare, Z. & Abdanan Mehdizadeh, S. (2014). Study of seed spacing uniformity and seed falling dynamics of a pneumatic planter under laboratory conditions using machine vision. Journal of Researches in Mechanics of Agricultural Machinery, 3(2), 19-28. (In Farsi)
Aglave, V.A., Patil, S.B. & Sambre, N.B. (2012). Imaging technique to measure leaf area, disease severity and chlorophyll content: a survay paper. Journal of Computing Technologies, 1(3), 191-208.
Ahmad, I.S. & Reid, J.F. (1996). Evaluation of colour representations for maize images. Journal of Agricultural and Engineering Research, 63(3), 185-196.
Ahmadzadeh Gharah Gwiz, K., Mirlatifi, S.M. & Mohammadi, K. (2010). Comparison of artificial intelligence systems (ANN & ANFIS) for reference evapotranspiration estimation in the extreme arid regions of Iran.Journal of Water and Soil, 24(4), 679-689. (In Farsi)
Altinkut, A., Kazan, K., Ipekci, Z. & Gozukirmizi, G. (2001). Tolerance to paraquat is correlated with the associated with water stress tolerance in segregation F2 populations of barley and wheat. Euphytica, 121, 81-86.
Anami, B.S., Pujari, J.D. & Yakkundimath. R. (2011). Identification and classification of normal and affected agriculture/horticulture produce based on combined color and texture feature extraction. International Journal of Computer Applications in Engineering Sciences, 1(3), 356-360.
Barnes, M., Duckett, T., Cielniak, G., Stroud, G. & Harper, G. (2010). Visual detection of blemishes in potatoes using minimalist boosted classifiers. Journal of Food Engineering, 98(3), 339-346.
Berger, B., Parent, B. & Tester, M. (2010). High throughput shoot imaging to study drought responses. Journal of Experimental Botany, 61(13), 3519–3528.
Chen, X., Xun, Y., Li, W. & Zhang, J. (2010). Combining discriminant analysis and neural networks for corn variety identification. Computer and Electronics in Agriculture, 71, 48–53.
CIE. (2004) CIE Technical Report: Colorimetry, 3th ed., Vienna: CIE Central Bureau. Publication CIE no. 15.2.
Damas, M., Prados, A.M., Gomez, F. & Olivares G. (2001). HidroBus system: fieldbus for integrated management of extensive areas of irrigated land. Microprocessors and Microsystems, 25(3), 177-184.
Erdem, Y., Shirali, S., Erdem, T. & Kenar, D. (2006). Determination or crop water stress index for irrigation scheduling of Bean (Phaseolus vulgaris L.). Journal Agriculture and Forest, 30, 195-202.
Escos, J., Alados, C.L., Pugnaire, F.I., Puigdefabregas, J. & Emlen, J. (2000). Stress resistance strategy in an arid land shrub: interactions between developmental instability and fractal dimension. Journal of Arid Environments, 45(4), 325–336.
Font, L. & Farkas, I. (2007). Wilting detection in greenhouse plants by image processing. ISHS Acta Horticulturae 801: International Symposium on High Technology for Greenhouse System Management, 669-676.
Foucher, P., Revollon, P., Vigouroux, B. & Chasseriaux, G. (2004). Morphological image analysis for the detection of water stress in potted Forsythia. Biosystems Engineering, 89 (2), 131–138.
Freund, Y. & Schapire, R. (1999). A short introduction to boosting. Journal of Japanese Society for Artificial Intelligence, 14 (5), 771–780.
Gandomi, A.H., & Alavi, A.H. (2012). A new multi-gene genetic programming approach to nonlinear system modeling. Part I: materials and structural engineering problems. Neural Computing and Applications, 21(1), 171-187.
Gonzalez, R.C., Woods, R.E. & Eddins, S.L. (2004) Digital image processing using MATLAB. Pearson Education India.
Gratani, L. & Varone, L. (2004). Leaf key traits of Erica arborea L., Erica multifolia L. and Rosmarinus officinalis L. Co-occuring in Mediter ranean maquis. Flora, 199, 58-69.
Haisman, D., Clarke, M. (1975). The interfacial factor in the heat-induced conversion of chlorophyll to pheophytin in green leaves. Journal of the Science of Food and Agriculture, 26, 1111-1126.
Heidari, N., Poor Yousef, M. & Tavakoli, A. (2014). Effects of drought stress on photosynthesis, its parameters and relative water content of anise (Pimpinella anisum L.). Journal of Plant Researches, 27(5), 829-839. (In Farsi)
Hendrawan, Y. & Murase, M. (2011). Bio inspired feature selection to select informative image features for determining water content of cultured Sunagoke moss. Expert Systems with Applications, 38(11), 14321–14335.
Hetzroni, A., Miles, G.E., Engel, B.A., Hammer, P.A. & Latin, R.X. (1994). Machine vision monitoring of plant health. Adv. Space Res, 14(11), 203–212.
HunterLab. (2001) Application note. Insight on Color, 13, pp. 1-4.
Igathinathane, C., Prakash, V.S.S., Padma, U., Ravi Babu, G. & Womac, A.R. (2006). Interactive computer software development for leaf area measurement. Computers and Electronics in Agriculture, 51(1), 1-16.
Kacira, M., Ling, P.P. & Short, T.H. (2002). Machine vision extracted plant movement for early detection of plant water stress. Transactions of the ASAE (American Society of Agricultural Engineers), 45(4), 1147–1153.
Kurata, K. & Yan, J. (1996). Waterstr ess estimation of tomato canopy based on machine vision. Acta Horticulturae, 440, 389–394.
Lampert, C.H., Blaschko, M.B., & Hofmann, T. (2009). Efficient subwindow search: A branch and bound framework for object localization. IEEE Transactions on Pattern Analysis and Machine Intelligence, 31(12), 2129-2142.
Leemans, V., Magein, H. & Destain, M.F. (2002). On line fruit grading according to their external quality using machine vision. Biosystems Engineering, 83(4), 397–404.
Leinonen, I. & Jones, H.G. (2004). Combining thermal and visible imagery for estimating canopy temperature and identifying plant stress. Journal of Experimental Botany, 55(401), 1423–1431.
Luzuriaga, D.A. & Balaban, M.O. (2002). Color machine vision system: an alternative for color measurement. In Proceedings of the world congress of computers in agriculture and natural resources, Iguacu Falls, Brazil. 13–15 March. pp. 93–100.
Neelamma, K.P., Virendra, S.M. & Ravi, M.Y. (2011). Color and texture based identification and classification of food grains using different color models and haralick features. International Journal on Computer Science and Engineering (IJCSE), 3, 3669-3680.
Revollon, P., Chasseriaux, G., Riviere, L.M. & Gardet, R. (1998). The use of image processing for tracking the morphological modification of Forsythia following an interruption of watering. In: Proceedings of International Conference on Agricultural Engineering, Oslo, pp. 872–873.
Ritchie, S.W., Nguyen, H.T. & Holaday, A.S. (1990). Leaf water content and gas exchange parameters of two wheat genotypes differing in drought resistance. Crop Science, 30, 105-111.
Salehi Salmi, M.R. (2015) Turfgrass (Identification, Establishment, Maintenance). pp. 188. Iran. (In Farsi)
Schapire, R. & Singer, Y. (1998). Improved boosting algorithms using confidence-rated predictions. In: Proceedings of the 11th Annual Conference on Computational Learning Theory, pp. 80–91.
Seginer, L., Elster, R.T., Goodrum, J.W. & Rieger, M.W. (1992). Plant wilt detection by computer vision tracking of leaf tips. Transactions of American Society of Agricultural Engineering, 35(5), 1563-1567.
Shen, H., Li, S., Gu, D. & Chang, H. (2012). Bearing defect inspection based on machine vision. Measurement, 45(4), 719-733.
Shoor, M., Behzadi, M. & Goldani, M. )2012(. Study of Rooting, Quantitative and Anatomical Traits on Two Coleus Spices in High Level Carbon Dioxide. Journal of Horticultural Science, 26(3), 277-285.
Steet, J.A., Tong, C.H. (1996a). Degradation kinetics of green color and chlorophylls in peas by colorimetry and HPLC. Journal of Food Science, 61, 924-928.
Tuberosa, R. & Salvi, S. (2006). Genomics based approaches to improve drought tolerance of crops. Trends in Plant Science, 11(8), 405–412.
Ushada, D., Murase, H. & Fukuda, H. (2007). Non-destructive sensing and its inverse model for canopy parameters using texture analysis and artificial neural network. Computers and Electronics in Agriculture, 57(2), 149–165.
Vezhnevets, A. (2006). GML AdaBoost MATLAB Toolbox, from http:// research.graphicon.ru.
Visen, N.S., Jayas, D.S., Paliwal, J. & White, N.D.G. (2004). Comparison of two neural network architectures for classification of singulated cereal grains. Journal of Canadian Biosystem Engineering, 46(3), 7-14.