توسعه یک مدل مبتنی بر شبکه عصبی مصنوعی برای تخمین میزان آهن فعال در برگ انگور

Arnal Barbedo, J. G. (2013). Digital image processing techniques for detecting, quantifying and classifying plant diseases. SpringerPlus, 2(1), 660. doi:  https://doi.org/10.1186/2193-1801-2-660

Asraf, H. M., Nooritawati, M. T., & Rizam, M. S. (2012). A comparative study in kernel-based support vector machine of oil palm leaves nutrient disease. Procedia Engineering, 41, 1353-1359. doi: https://doi.org/10.1016/j.proeng.2012.07.321

Bienfait, H. F., & Mark, F. V. D. (1983). Phytoferritin and its role in iron metabolism. In: Metals and Micronutrients: Uptake and Utilization by plants. Pp. 111/123. Academic Press. New York.

Costa, J. M., Grant, O. M., & Chaves, M. M. (2013). Thermography to explore plant–environment interactions. Journal of experimental botany, 64(13), 3937-3949. doi: https://doi.org/10.1093/jxb/ert029

Estefan, G., Sommer, R., & Ryan, J. (2013). Methods of soil, plant, and water analysis. A manual for the West Asia and North Africa region, 3(2).

Ghosal, S., Blystone, D., Singh, A. K., Ganapathysubramanian, B., Singh, A., & Sarkar, S. (2018). An explainable deep machine vision framework for plant stress phenotyping. Proceedings of the National Academy of Sciences, 115(18), 4613-4618. doi: https://doi.org/10.1073/pnas.1716999115

Gorbe, E., & Calatayud, A. (2012). Applications of chlorophyll fluorescence imaging technique in horticultural research: A review. Scientia Horticulturae, 138, 24-35. doi: https://doi.org/10.1016/j.scienta.2012.02.002

Han, K. A. M., & Watchareeruetai, U. (2019, July). Classification of nutrient deficiency in black gram using deep convolutional neural networks. In 2019 16th International Joint Conference on Computer Science and Software Engineering (JCSSE) (pp. 277-282). IEEE. doi:  https://doi.org/10.1109/JCSSE.2019.8864224

Hochmuth, G. (2011). Iron (Fe) nutrition of plants. University of Florida If as Extension. Sl, 353, 1-8.

Hu, J., Li, D., Chen, G., Duan, Q., & Han, Y. (2012). Image segmentation method for crop nutrient deficiency based on fuzzy c-means clustering algorithm. Intelligent Automation & Soft Computing, 18(8), 1145-1155. doi: https://doi.org/10.1080/10798587.2008.10643318

Imani, A., Hosseinpour, S., Keyhani, A., & Azimzadeh, M. (2020). Modeling and Optimization of Oligonucleotide-Based Nanobiosensor Using Artificial Neural Network and Genetic Algorithm Based Procedure. Iranian Journal of Biosystems Engineering, 51(1), 171-181. (In Persian with English Abstract). https://dx.doi.org/10.22059/ijbse.2019.290631.665231

Meyer, G. E., Mehta, T., Kocher, M. F., Mortensen, D. A., & Samal, A. (1998). Textural imaging and discriminant analysis for distinguishingweeds for spot spraying. Transactions of the ASAE, 41(4), 1189-1197. doi: https://doi.org/10.13031/2013.17244

Misra, A., & Sharma, S. (2006). Critical Fe concentration and productivity of Java citronella. Rev Bras Plant Med, 8, 54-58.

Neaman, A., & Aguirre, L. (2007). Comparison of different methods for diagnosis of iron deficiency in avocado. Journal of Plant Nutrition, 30(7), 1097-1108. doi: https://doi.org/10.1080/01904160701394550

Römheld, V. (1987). Different strategies for iron acquisition in higher plants. Physiologia Plantarum, 70(2). doi:  https://doi.org/10.1111/j.1399-3054.1987.tb06137.x

Römheld, V. (2000). The chlorosis paradox: Fe inactivation as a secondary event in chlorotic leaves of grapevine. Journal of plant nutrition, 23(11-12), 1629-1643. doi: https://doi.org/10.1080/01904160009382129

Rout, G. R., & Sahoo, S. (2015). Role of iron in plant growth and metabolism. Reviews in Agricultural Science, 3, 1-24. doi: https://doi.org/10.7831/ras.3.1

Sun, J., Mao, H., & Yang, Y. (2009). THE RESEARCH ON THE JUDGMENT OF PADDY RICE’S NITROGEN DEFICIENCY BASED ON IMAGE. In Computer and Computing Technologies in Agriculture II, Volume 2: The Second IFIP International Conference on Computer and Computing Technologies in Agriculture (CCTA2008), October 18-20, 2008, Beijing, China 2 (pp. 1049-1054). Springer US. doi: https://doi.org/10.1007/978-1-4419-0211-5_30

Sun, Y., Gao, J., Wang, K., Shen, Z., & Chen, L. (2018). Utilization of machine vision to monitor the dynamic responses of rice leaf morphology and colour to nitrogen, phosphorus, and potassium deficiencies. Journal of Spectroscopy, 2018. doi: https://doi.org/10.1155/2018/1469314

Tewari, V. K., Kumar, A. A., Kumar, S. P., Pandey, V., & Chandel, N. S. (2013). Estimation of plant nitrogen content using digital image processing. Agric Eng Int: CIGR Journal, 15(2), 78-86.

Vasconcelos, M. W., & Grusak, M. A. (2014). Morpho-physiological parameters affecting iron deficiency chlorosis in soybean (Glycine max L.). Plant and soil, 374, 161-172. doi: https://doi.org/10.1007/s11104-013-1842-6

Vesali, F., Omid, M., Kaleita, A., & Mobli, H. (2015). Development of an android app to estimate chlorophyll content of corn leaves based on contact imaging. Computers and Electronics in Agriculture, 116, 211-220. doi: https://doi.org/10.1016/j.compag.2015.06.012

Yu, K. Q., Zhao, Y. R., Li, X. L., Shao, Y. N., Liu, F., & He, Y. (2014). Hyperspectral imaging for mapping of total nitrogen spatial distribution in pepper plant. PloS one, 9(12), e116205. doi: https://doi.org/10.1371/journal.pone.0116205

Zohlen, A. (2000). Use of 1, 10‐phenanthroline in estimating metabolically active iron in plants. Communications in Soil Science and Plant Analysis, 31(3-4), 481-500. doi: https://doi.org/10.1080/00103620009370451