کاربردهای بینایی ماشین در حوزه گیاه زعفران

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Minaei, S., Kiani, S., Ayyari, M., & Ghasemi-Varnamkhasti, M. (2017). A portable computer-vision-based expert system for saffron color quality characterization. Journal of applied research on medicinal and aromatic plants, 7, 124-130. https://doi.org/10.1016/j.jarmap.2017.07.004

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Mohammadi, M., & Karami, M. (2021). Detecting the location and angle of saffron flowers in the image using deep convolutional networks. Journal of Machine Vision and Image Processing, 8(3), 45-55. https://doi.org/20.1001.1.23831197.1400.8.3.4.2

Mohammadzadeh-Moghadam, M. (2016). Investigating the effect of various drying methods on the quality of saffron and classification of dried saffron using digital image processing and different classification algorithms Mashhad Ferdowsi University].

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Sereshti, H., Poursorkh, Z., Aliakbarzadeh, G., Zarre, S., & Ataolahi, S. (2018). An image analysis of TLC patterns for quality control of saffron based on soil salinity effect: A strategy for data (pre)-processing. Food chemistry, 239, 831-839.

Yasrebi, S. E., Zabah, I., Behzadian, B., Marousi, A., & Rezaei, R. (2019). Classification of saffron based on its apparent characteristics using artificial neural networks. Saffron agronomy and technology, 7(4), 521-535. https://doi.org/https://doi.org/10.22048/jsat.2019.149440.1316

Zeraatkar, M., Khalili, K., & Foorginejad, A. (2016). High‐Precision Laser Scanning System for Three‐Dimensional Modeling of Saffron Flower. Journal of Food Process Engineering, 39(6), 553-563.