Abbasgholipour, M., Omid, M., Keyhani, A. & Mohtasebi, S. S. (2011). Color image segmentation with genetic algorithm in a raisin sorting system based on machine vision in variable conditions. Expert Systems with Applications, 38(4), 3671-3678.
Agricultural Statistics. (2017). Ministry of Agriculture, Deputy Director of Economic and Planning, ICT Center. Retrieved December 20, 2018, from https://usda.library.cornell.edu
Bai, X., Cao, Z., Wang, Y., Yu, Z., Zhang, X. & Li, C. (2013). Crop segmentation from images by morphology modeling in the CIE L* a* b* color space. Computers and Electronics in Agriculture, 99, 21–34.
Cabras, P., Angioni, A., Garau, V. L., Minelli, E. V., Cabitza, F. & Cubeddu M. (1997). Residues of some pesticides in fresh and dried apricots. Journal of Agricultural and Food Chemistry, 45, 3221-3222
Chowdhury, S., Verma, B. & Stockwell, D. (2015). A novel texture feature based multiple classifier technique for roadside vegetation classification. Expert Systems with Applications, 42 (12), 5047–5055.
Ebrahimi, E., Mollazade, K. & Babaei, S. (2014). Toward an automatic wheat purity measuring device: A machine vision-based neural networks-assisted imperialist competitive algorithm approach. Measurement, 55, 196-205.
Esmaiilia, M., Sotudeh-Gharebaghb, R., Croninc, K., Mousavia, M.A.E. & Rezazadehd, G. (2007). Grape Drying: A Review. Food Reviews International, 23 (3), 257–280.
Gardé-Cerdán, T., Arias-Gil, M., Marsellés-Fontanet, A.R., Ancín-Azpilicueta, C. & Martín-Belloso, O. (2007). Effects of thermal and non-thermal processing treatments on fatty acids and free amino acids of grape juice. Food Control, 18, 473–479.
Ghrairi F., Lahouar L., Amira A., Brahmi F., Ferchichi A., Achour L. & Said S. (2013). Physicochemical composition of different varieties of raisins (Vitis vinifera L.) from Tunisia. Industrial Crops and Products, 43, 73-77.
Guanjun, B., Mimi, J., Yi, X., Shibo, C., & Qinghua, Y. (2019). Cracked egg recognition based on machine vision. Computers and Electronics in Agriculture, 158, 159-166.
Guevara-Hernandez, F. & Gomez-Gil, J. (2011). A machine vision system for classification of wheat and barley grain kernels. Spanish Journal of Agricultural Research, 9 (3), 672–680.
Gurak, P.D., Cabral, L.M.C., Rocha-Leão, M.H.M., Matta, V.M. & Freitas, S.P. (2010). Quality evaluation of grape juice concentrated by reverse osmosis. Journal of Food Engineering, 96, 421–426.
Haralick, R. M., Shanmugam, K. & Dinstein, I. H. (1973). Textural features for image classification. Systems, Man and Cybernetics. IEEE Transactions on, 6, 610–621.
Jairaj, K.S., Singh, S.P. & Srikant, K.A. (2009). Review of solar dryers developed for grape drying. Solar Energy, 83, 1698–1712.
Jiang, G., Wang, X., Wang, Z. & Liu, H. (2016). Wheat rows detection at the early growth stage based on Hough transform and vanishing point. Computers and Electronics in Agriculture, 123, 211–223.
Karimi, N., Kondrood, R. R. & Alizadeh, T. (2017). An intelligent system for quality measurement of Golden Bleached raisins using two comparative machine learning algorithms. Measurement, 107, 68-76.
Liming, X. & Yanchao, Z. (2010). Automated strawberry grading system based on image processing. Computers and Electronics in Agriculture, 71, S32–S39.
Mokhtar, U., El Bendary, N., Hassenian, A.E., Emary, E., Mahmoud, M.A., Hefny, H. & Tolba, M.F. (2015). SVM-based detection of tomato leaves diseases. In: Intelligent Systems' 2014. Springer, Cham, pp. 641–652.
Okamura, N. K., Delwiche, M. J. & Thompson, J. F. (1993). Raisin grading by machine vision. Transactions of the ASAE (USA).
Olgun, M., Onarcan, A.O., Özkan, K., Işik, Ş., Sezer, O., Özgişi, K. & Koyuncu, O. (2016). Wheat grain classification by using dense SIFT features with SVM classifier. Computers and Electronics in Agriculture, 122, 185–190.
Pangavhane, D.R. & Sawhney, R.L. (2002). Review of research and development work on solar dryers for grape drying. Energy Conversion and Management, 43, 45–61.
Pham, V.H. & Lee, B.R. (2015). An image segmentation approach for fruit defect detection using k-means clustering and graph-based algorithm. Vietnam Journal of Computer Science, 2 (1), 25–33.
Rehman, T. U., Zaman, Q. U., Chang, Y. K., Schumann, A. W., & Corscadden, K. W. (2019). Development and field evaluation of a machine vision based in-season weed detection system for wild blueberry. Computers and Electronics in Agriculture, 162, 1-13.
Renzetti, F. R. & Zortea, L. (2011). Use of a gray level co-occurrence matrix to characterize duplex stainless steel phases microstructure. Frattura ed Integrità Strutturale, 16(1), 43-51.
Su, Q., Kondo, N., Li, M., Sun, H., Al Riza, D. F., & Habaragamuwa, H. (2018). Potato quality grading based on machine vision and 3D shape analysis. Computers and electronics in agriculture, 152, 261-268.
Sun, Y., Gu, X., Sun, K., Hu, H., Xu, M., Wang, Z. & Pan, L. (2017). Hyperspectral reflectance imaging combined with chemometrics and successive projections algorithm for chilling injury classification in peaches. LWT- Food Science and Technology, 75, 557–564.
Unay, D., Gosselin, B., Kleynen, O., Leemans, V., Destain, M. & Debeir, O. (2011). Automatic grading of bi-colored apples by multispectral machine vision. Computers and Electronics in Agriculture, 75 (1), 204–212
Veernagouda Ganganagowder, N. & Kamath, P. (2017). Intelligent classification models for food products basis on morphological, colour and texture features. Acta Agronómica, 66 (4).
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.
Yimyam, P. & Clark, A.F. (2016). 3D reconstruction and feature extraction for agricultural produce grading. In: Paper Presented at the Knowledge and Smart Technology (KST), 2016 8th International Conference on, pp. 136–141.
Yu, X., Liu, K., Wu, D., & He, Y. (2012). Raisin quality classification using least squares support vector machine (LSSVM) based on combined color and texture features. Food and Bioprocess Technology, 5(5), 1552-1563.