Atanassova, S., Nikolov, P., Valchev, N., Masheva, S. & Yorgov, D. (2019, February). Early detection of powdery mildew (Podosphaera xanthii) on cucumber leaves based on visible and near-infrared spectroscopy. In AIP conference proceedings (Vol. 2075, No. 1). AIP Publishing. https://doi.org/10.1063/1.5091341
Elangovan, K. & Nalini, S. (2017). Plant disease classification using image segmentation and SVM techniques. International Journal of Computational Intelligence Research, 13(7), 1821-1828.
Franke, J. & Menz, G. (2007). Multi-temporal wheat disease detection by multi-spectral remote sensing. Precision Agriculture, 8, 161-172. https://doi.org/10.1007/s11119-007-9036-y
Hayıt, T., Erbay, H., Varçın, F., Hayıt, F. & Akci, N. (2023). The classification of wheat yellow rust disease based on a combination of textural and deep features. Multimedia Tools and Applications, 82(30), 47405-47423. https://doi.org/10.1007/s11042-023-15199-y
Kazemi, H. & Momeni, H. (2015). Management of stripe rust disease of wheat (Applied instruction). Iranian Research Institute of Plan Protection. Registration No. 46869. (In Persian).
Khodabande, N. (2013). Cereals. University of Tehran Press, 10th Edition. (In Persian).
Kumar, R., Goyal, M. K., Ahmed, P. & Kumar, A. (2012, December). Unconstrained handwritten numeral recognition using majority voting classifier. In 2012 2nd IEEE International Conference on Parallel, Distributed and Grid
Computing (pp. 284-289). IEEE. https://doi.org/10.1109/PDGC.2012.6449833
Liu, W., Yang, G., Xu, F., Qiao, H., Fan, J., Song, Y. & Zhou, Y. (2018). Comparisons of detection of wheat stripe rust using hyperspectral and uav aerial photography. Acta Phytopathol. Sinica, 48(2), 223-227. https://doi.org/10.13926/j.cnki.apps.000170
McKinnon, T. & Hoff, P. (2017). Comparing RGB-based vegetation indices with NDVI for agricultural drone imagery. AGBX, 2, 1-17.
Qiu, R., Yang, C., Moghimi, A., Zhang, M., Steffenson, B. J. & Hirsch, C. D. (2019). Detection of fusarium head blight in wheat using a deep neural network and color imaging. Remote Sensing, 11(22), 2658. https://doi.org/10.3390/rs11222658
Riccardi, M., Mele, G., Pulvento, C., Lavini, A., d’Andria, R. & Jacobsen, S. E. (2014). Non-destructive evaluation of chlorophyll content in quinoa and amaranth leaves by simple and multiple regression analysis of RGB image components. Photosynthesis research, 120, 263-272. https://doi.org/10.1007/s11120-014-9970-2
Sankaran, S., Mishra, A., Ehsani, R. & Davis, C. (2010). A review of advanced techniques for detecting plant diseases. Computers and electronics in agriculture, 72(1), 1-13. https://doi.org/10.1016/j.compag.2010.02.007
Su, J., Liu, C., Coombes, M., Hu, X., Wang, C., Xu, X. & Chen, W. H. (2018). Wheat yellow rust monitoring by learning from multispectral UAV aerial imagery. Computers and electronics in agriculture, 155, 157-166. https://doi.org/10.1016/j.compag.2018.10.017
Su, J., Liu, C., Hu, X., Xu, X., Guo, L. & Chen, W. H. (2019). Spatio-temporal monitoring of wheat yellow rust using UAV multispectral imagery. Computers and electronics in agriculture, 167, 105035. https://doi.org/10.1016/j.compag.2019.105035
Torabi, M., Mardoukhi, V., Nazari, K., Afshari, F., Forootan, A. R., Ramai, M. A. & Kashani, A. S. (1995). Effectiveness of wheat yellow rust resistance genes in different parts of Iran. Cereal Rusts and Powdery Mildews Bulletin, 23(1), 9-12.
Wang, A., Zhang, W., & Wei, X. (2019). A review on weed detection using ground-based machine vision and image processing techniques. Computers and electronics in agriculture, 158, 226-240. https://doi.org/10.1016/j.compag.2019.02.005
Wang, H., Qin, F., Liu, Q., Ruan, L., Wang, R., Ma, Z. & Wang, H. (2015). Identification and disease index inversion of wheat stripe rust and wheat leaf rust based on hyperspectral data at canopy level. Journal of Spectroscopy, https://doi.org/10.1155/2015/651810
Wellings, C. R. (2011). Global status of stripe rust: a review of historical and current threats. Euphytica, 179(1), 129-141. https://doi.org/10.1007/s10681-011-0360-y
Whetton, R. L., Waine, T. W. & Mouazen, A. M. (2018). Hyperspectral measurements of yellow rust and fusarium head blight in cereal crops: Part 2: On-line field measurement. Biosystems engineering, 167, 144-158. https://doi.org/10.1016/j.biosystemseng.2018.01.004
Xu, P., Wu, G., Guo, Y., Yang, H. & Zhang, R. (2017). Automatic wheat leaf rust detection and grading diagnosis via embedded image processing system. Procedia Computer Science, 107, 836-841. https://doi.org/10.1016/j.procs.2017.03.177
Xue, J. & Su, B. (2017). Significant remote sensing vegetation indices: A review of developments and applications. Journal of sensors, https://doi.org/10.1155/2017/1353691
Yu, K., Anderegg, J., Mikaberidze, A., Karisto, P., Mascher, F., McDonald, B. A. & Hund, A. (2018). Hyperspectral canopy sensing of wheat septoria tritici blotch disease. Frontiers in plant science, 9, 1195. https://doi.org/10.3389/fpls.2018.01195
Zhang, D., Zhou, X., Zhang, J., Lan, Y., Xu, C. & Liang, D. (2018). Detection of rice sheath blight using an unmanned aerial system with high-resolution color and multispectral imaging. PloS one, 13(5), e0187470. https://doi.org/10.1371/journal.pone.0187470
Zheng, Q., Huang, W., Cui, X., Dong, Y., Shi, Y., Ma, H. & Liu, L. (2018). Identification of wheat yellow rust using optimal three-band spectral indices in different growth stages. Sensors, 19(1), 35. https://doi.org/10.3390/s19010035
Zheng, Q., Ye, H., Huang, W., Dong, Y., Jiang, H., Wang, C., ... & Chen, S. (2021). Integrating spectral information and meteorological data to monitor wheat yellow rust at a regional scale: A case study. Remote Sensing, 13(2), 278. https://doi.org/10.3390/rs13020278