امکان سنجی استفاده از بینی الکترونیک و هوش مصنوعی جهت تشخیص واریته های گندم

Aghili nategh, N., Anvar, A., Dalvand,M. J. (2020). Detection of Ripeness Grades of Strawberry Using an E-nose. Journal of Researches in Mechanics of Agricultural Machinery, 9(2), 71-80. (In Persian).

Adab, H., Farajzadeh, M., Filekash, A., & Esmaili, R. (2015). preparation of autumn rapeseed yield map using perceptron neural network, case study: Sabzevar city. Quarterly scientific research journal of geographical space, 13th year, number 02, page -173.( In Persian)

Bagherpour, H.,& Shamohammadi, S. (2023). Identification of different wheat cultivars using two deep convolutional neural networks. The 15^th National & 1^st International Congress of Biosystem Mechanical Engineering & Agricultural Mechanization, Tehran. (In Persian).

FAO. FOASTAT, Food and Agriculture Data. 2023. Available online: http://www.fao.org/faostat .

Guo, Z., Guo, C., Chen, Q., Ouyang, Q., Shi, J., El-Seedi, H. R., & Zou, X. (2020). Classification for Penicillium expansum spoilage and defect in apples by e-nose combined with chemometrics. Sensors, 20(7), 2130.‏

Javaheri, M.A. (2022). Identification of bread and durum wheat cultivars suitable for planting in the Arzoiyeh region of Kerman province. Agricultural Sciences Research in Dry Areas, 4(1), 78-95. . (In Persian).

Jiang,X., Bu,Y, Han,L., Tian,J., Hu,X., Zhang,X., Huang,D., Luo,H. (2023). Rapid nondestructive detecting of wheat varieties and mixing ratio by combining hyperspectral imaging and ensemble learning. Food Control, 150(),

Khorramifar, A., Rasekh, M., Karami, H., Malaga-Toboła, U., & Gancarz, M. (2021). A machine learning method for classification and identification of potato cultivars based on the reaction of MOS type sensor-array. Sensors, 21(17), 5836.‏

Karami, H., Rasekh, M., & Mirzaee‐Ghaleh, E. (2020). Application of the E‐nose machine system to detect adulterations in mixed edible oils using chemometrics methods. Journal of Food Processing and Preservation, 44(9), e14696.‏

Liu,W., Liu,Y., Honga,f., Lia,J.,, Jianga,Q., Konga,L., Liu,C., Zheng,L. (2025). Identification of varieties of wheat seeds based on multispectral . Food Physics,2,1-12.

Mu, F., Gu, Y., Zhang, J., & Zhang, L. (2020). Milk source identification and milk quality estimation using an e-nose and machine learning techniques. Sensors, 20(15), 4238.‏

Majidi, M., Separi, M., Ahmadi, M. S., & Nasrolahbeigi, M. B. (2019). Evaluation of yield and yield components of new wheat cultivars in comparison with Sardari wheat. Plant Production and Genetics, 1(1 ), 45-52. (In Persian).

Que, H., Zhao, X., Sun, X., Zhu, Q., Huang, M. (2023). Identification of wheat kernel varieties based on hyperspectral imaging technology and grouped convolutional neural network with feature intervals.Infrared Physics & Technology,Volume 131, 104653.

Song, S., Yuan, L., Zhang, X., Hayat, K., Chen, H., Liu, F., & Niu, Y. (2013). Rapid measuring and modelling flavour quality changes of oxidised chicken fat by e-nose profiles through the partial least squares regression analysis. Food chemistry, 141(4), 4278-4288.‏

Sharma,A., Singh,T.,  Mittal Garg,N. (2024). Nondestructive Identification of Wheat Seed Variety and Geographical Origin Using Near-Infrared Hyperspectral Imagery and Deep Learning.Journal of Chemosensor,38(10).

Sun,Q ., Zhang,R., Liu,Y., Niu, L., Liu,H., Ren, P., Xu, B., Guo,B.,, Zhang, B. .(2024). Insights into “wheat aroma”: Analysis of volatile components in wheat grains cultivated in saline–alkali soil. Food Research International, Volume 183,  114211

Rasooli Sharabiani, V., Kisalaei, A., & Khorramifar, A. (2024). Using E-nose to Detect Potato Varieties using Chemometric Methods. Journal of Environmental Science Studies, 8(4), 7358-7364. (In Persian).

Roshan Moghaddam, M., Amiri Chayjan, R., & Aghili Nategh, N. (2021). Assessment and Comparison of Changes in Aroma, Color and Vitamin C of Dried Orange Slices in Freeze and Hot Air Dryers. Agricultural Mechanization, 6(3), 41-47. (In Persian).

Tyagi, B. S., Foulkes, J., Singh, G., Sareen, S., Kumar, P., Broadley, M. R., ... & Singh, G. P. (2020). Identification of wheat cultivars for low nitrogen tolerance using multivariable screening approaches. Agronomy, 10(3), 417.‏

Tahri, K., Tiebe, C., El Bari, N., Hübert, T., & Bouchikhi, B. (2017). Geographical classification and adulteration detection of cumin by using electronic sensing coupled to multivariate analysis. Procedia technology, 27, 240-241.‏

Wei Liu,W., Liu,Y., Hong,F., Li,J., Jiang, Q., Kong,L., Liu,C., Zheng,L.(2024). Identification of varieties of wheat seeds based on multispectral imaging combined with improved YOLOv5.Preprint.

Zheng, X. Z., Lan, Y. B., Zhu, J. M., Westbrook, J., Hoffmann, W. C., & Lacey, R. E. (2009). Rapid identification of rice samples using an e-nose. Journal of Bionic Engineering, 6(3), 290-297.

Zhang,K., Zhang,C., Gao,L., Zhuang,H., Feng,T., & Xu,G.(2021). Analysis of volatile flavor compounds of green wheat under different treatments by GC-MS and GC-IMS. Journal of  Food Biochemistry, 1-17.