Duke, J. A. (1981). Handbook of legumes of world economic importance. New York: Plenum Press.
 Wallace, T. C., Murray, R., Zelman, K. M. (2016). The Nutritional Value and Health Benefits of Chickpeas and Hummus. Nutrients, 8, 1-10.
 Hulse, J. H. (1991). Nature composition and utilization of gran legumes. In: Uses of Tropical Legumes: Proceeding of a consultants meeting, 27-30 March 1989, ICRISAT Centre. ICRISAT, Patancheru, A.P.502324, India. pp. 11-27.
 Malhotra, R. S., & Saxena, M. C. (2002). Strategies for Overcoming Drought Stress in Chickpea. Caravan, ICARDA, 17p.
 Kochaki, A., & Banayaneaval, M. (2009). Pulse Crops. Mashhad, I. R. Iran. Mashhad Academic Jahad Publishers. [In Persian].
 Kheiralipour, K., Ahmadi, H., Rajabipour, A., Rafiee, S. (2018). Thermal Imaging, Principles, Methods and Applications (1st ed). Ilam, I. R. Iran. Ilam University Publication. [In Persian].
 Kheiralipour, K. 2012. Implementation and construction of a system for detecting fungal infection of pistachio kernel based on thermal imaging (TI) and image processing technology. Ph.D. Dissertation, University of Tehran, Karaj, Iran. [In Persian].
 Chen, B., Tojo, S., & Watanabe, K. (2003). Machine vision for a micro weeding robot in a paddy field. Biosystems Engineering, 85(4), 393-404.
 Tong, J. H., J. B., Li, and H. Y. Jiang. 2013. Machine visiontechniques for the evaluation of seedling quality based on leaf area. Biosystems engineering, 115(3): 369-379.
, K., & Marzbani, F. (2016). Pomegranate quality sorting by image processing and artificial neural network. 10th Iranian National Congress on Agricultural Machinery Engineering (Biosystems) and Mechanizasion
. 30-31 August, Mashhad, Iran. [In Persian].
 Mohammadi, V., Kheiralipour, K., & Ghasemi-Varnamkhasti, M. (2015). Detecting maturity of persimmon fruit based on imageprocessing technique. Scientia Horticulturae, 184, 123-128.
 Khazaee, Y., Kheiralipour
., K. Hoseinpour., A. & Javadikia, H.
(2019). Development of an algorithm based on image processing technique and sport vector machine to distinct potato from clod and stone. Journal of Research in Mechanics of Agricultural Machinery
, 8(1): 1-11. [In Persian].
 Iraji, M. S. (2018). Comparison between soft computing methods for tomato quality grading using machine vision. Journal of Food Measurement and Characterization, 13, 1-15.
 Kheiralipour, K., & Pormah, A. (2017). Introducing new shape features for classification of cucumber fruit based on image processing technique and artificial neural networks. Journal of Food Process Engineering
 Concha-Meyer, A., Eifert, J., Wang, H., & Sanglay. G. (2018). Volume estimation of strawberries, mushrooms, and tomatoes with a machine vision system. International Journal of Food Properties, 21(1), 1867-1874.
 Mahdiani, M., and H. Sadrnia. 2010. Grade raisins using image processing: identification cap stem and color. 6th Iranian National Congress on Agricultural Machinery and Mechanization, 15-16 September, Karaj, Iran. [In Persian].
 Venora, G., Grillo, O., Shahin, M. A., Symons, S. J. (2007). Identification of Sicilian landraces and Canadian cultivars of lentil using an image analysis system. Food Research International, 40, 161-166.
 LeMasurier, L. S., Panozzo, J. F., & Walker, C. K. (2014). A digital image analysis method for assessment of lentil size traits. Journal of Food Engineering, 128, 72-78.
 Chen, J., Lian, Y., & Li, Y. (2020). Real-time grain impurity sensing for rice combine harvesters using image processing and decision-tree algorithm. Computers and Electronics in Agriculture, 175, 105591.
 Shen, Y., Yin, Y., Li,
B., Zhao, C.,
, G. (2021). Detection of impurities in wheat using terahertz spectral imaging and convolutional neural networks. Computers and Electronics in Agriculture
 Jahanbakhshi, A. & Kheiralipour, K. (2019). Carrot sorting based on shape using image processing, artificial neural network, and support vector machine. Journal of Agricultural Machinery, 9 (2), 295-307. [In Persian].
 Azadnia, R. & Kheiralipour, K. (2021). Recognition of leaves of different medicinal plant species using a robust image processing algorithm and artificial neural networks classifier. Journal of Applied Research on Medicinal and Aromatic Plants, 100327.
 Pourdarbani, R., Sabzi, S., García-Amicis V.M., García-Mateos, G., Molina-Martínez, J.M., & Ruiz-Canales, A. (2019). Automatic classification of chickpea varieties using computer vision techniques. Agronomy, 9, 672.
 Pourdarbani, R., Sabzi, S., Kalantari, D., Hernández-Hernández, J. L., & Ignacio Arribas, J. (2020). A Computer vision system based on majority-voting ensemble neural network for the automatic classification of three chickpea varieties. Foods, 9, 113.
 Gonzalez, R. C., & Woods, R. E. (2002). Digital Image Processing (2nd ed). Prentice Hall Inc.
 Mansourfar, K. (1995). Statistical methods (3rd ed). Tehran, I. R. Iran. University of Tehran Publication. [In Persian].
 Ehdaee, B. (1994). General Experimental Statistics (4th ed). Ahvaz, I. R. Iran. Shahid Chamran University publication. [In Persian].
 Menhaj, M. B. (2002). Principles of Artificial Neural Networks. (2nd ed). Tehran, I. R. Iran. Industrial University of Amirkabir (Tehran Polytechnic) Publication. [In Persian].
, Z-W., Liang
, F-N., & Liu
, Y-Z. )2018(. Artificial neural network modeling of biosorption process using agricultural wastes in a rotating packed bed. Applied Thermal Engineering
. 140, 95-101.