Research Article
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Year 2023, Volume: 29 Issue: 2, 427 - 442, 31.03.2023
https://doi.org/10.15832/ankutbd.1019586

Abstract

References

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  • Demir B, Sayıncı B, Çetin N, Yaman M, Çömlek R, Aydın Y & Sütyemez M (2018). Elliptic Fourier based analysis and multivariate approaches for size and shape distinctions of walnut (Juglans regia L.) cultivars. Grasas y Aceites 69(4): 1-12 doi.org/10.3989/gya.0104181
  • Demir B, Sayinci B, Çetin N, Yaman M & Çömlek R (2019). Shape discrimination of almond cultivars by elliptic fourier descriptors. Erwerbs-Obstbau 61(3): 245-256. doi.org/10.1007/s10341-019-00423-7
  • Demir B, Eski İ, Gürbüz F, Kuş Z A, Sesli Y & Ercişli S (2020). Prediction of walnut mass based on physical attributes by Artificial Neural Network (ANN). Erwerbs-Obstbau 62(1): 47-56. doi.org/10.1007/s10341-019-00468-8
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  • Germšek B, Rozman Č & Unuk T (2017). Forecasting apple fruit color intensity with machine learning methods. Erwerbs-Obstbau 59(2): 109-118. doi.org/10.1007/s10341-016-0305-7
  • George C, McGruder R & Torgerson K (2007). Determination of optimal surface area to volume ratio for thin-layer drying of breadfruit (Artocarpus altilis). International Journal for Service Learning in Engineering 2(2): 76-88. doi.org/10.24908/ijsle.v2i2.2093
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Evaluation of Image Processing Technique on Quality Properties of Chickpea Seeds (Cicer arietinum L.) Using Machine Learning Algorithms

Year 2023, Volume: 29 Issue: 2, 427 - 442, 31.03.2023
https://doi.org/10.15832/ankutbd.1019586

Abstract

Chickpea is an important edible legume consumed worldwide because of rich nutrient composition. The physical parameters of chickpea are crucial attributes for design of processing and classification systems. In this study, effects of seven different irrigation treatments (I1-rainfed, I2-pre-flowering single irrigation, I3-beginning of flowering single irrigation, I4-50% pod set single irrigation, I5-irrigation at 50% flowering and 50% pod fill, I6-irrigation before flowering and at 50% pod set, I7-full irrigation) on size, shape, mass, and color properties of chickpea seeds were investigated, and machine learning algorithms were used to estimate mass and color attributes of chickpea seeds. In terms of physical attributes, the best results were obtained in I1 and I5 irrigation treatments. According to the findings, among the irrigation treatments, I5 had the greatest mass, volume, geometric mean diameter, projected area with the values of 0.50 g, 394.86 cm3, 9.10 mm and 65.03 mm2, respectively. In addition, I1 had the highest shape index and elongation as 1.33 and 1.34, respectively. The results showed that multilayer perceptron (MLP) had the greatest correlation coefficients for mass (0.9997), chroma (0.9998) hue angle (0.9998) and color index (0.9992). The MLP yielded better outcomes than random forest for both mass and color estimation. Additionally, single or couple irrigation treatment at different physiological stages instead of full irrigation treatment might be sufficient to improve the physical attributes of chickpea.

References

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  • de Camargo A C, Favero B T, Morzelle M C, Franchin M, Alvarez-Parrilla E, de la Rosa L A, Geraldi M V, Maróstica Júnior M R, Shahidi F & Schwember A R (2019). Is chickpea a potential substitute for soybean? Phenolic bioactives and potential health benefits. International Journal of Molecular Sciences 20(11): 2644 doi.org/10.3390/ijms20112644
  • Demir B (2018). Application of data mining and adaptive neuro-fuzzy structure to predict color parameters of walnuts (Juglans regia L.). Turkish Journal of Agriculture and Forestry 42(3): 216-225. doi.org/10.3906/tar-1801-78
  • Demir B, Sayıncı B, Çetin N, Yaman M, Çömlek R, Aydın Y & Sütyemez M (2018). Elliptic Fourier based analysis and multivariate approaches for size and shape distinctions of walnut (Juglans regia L.) cultivars. Grasas y Aceites 69(4): 1-12 doi.org/10.3989/gya.0104181
  • Demir B, Sayinci B, Çetin N, Yaman M & Çömlek R (2019). Shape discrimination of almond cultivars by elliptic fourier descriptors. Erwerbs-Obstbau 61(3): 245-256. doi.org/10.1007/s10341-019-00423-7
  • Demir B, Eski İ, Gürbüz F, Kuş Z A, Sesli Y & Ercişli S (2020). Prediction of walnut mass based on physical attributes by Artificial Neural Network (ANN). Erwerbs-Obstbau 62(1): 47-56. doi.org/10.1007/s10341-019-00468-8
  • Eissa A H A, Mohamed M A, Moustafa H & Alghannam A R O (2010). Moisture dependent physical and mechanical properties of chickpea seeds. International Journal of Agricultural and Biological Engineering 3(4): 80-93.
  • Ercisli S, Sayinci B, Kara M, Yildiz C & Ozturk I (2012). Determination of size and shape features of walnut (Juglans regia L.) cultivars using image processing. Scientia Horticulturae 133: 47-55. doi.org/10.1016/j.scienta.2011.10.014
  • FAOSTAT (2019) Source: http://faostat.fao.org/ (accessed on 14 May, 2021).
  • Fıratlıgil-Durmus E, Sárka E, Bubník Z, Schejbal M & Kadlec P (2010). Size properties of legume seeds of different varieties using image analysis. Journal of Food Engineering 99(4): 445-451. doi.org/10.1016/j.jfoodeng.2009.08.005
  • Gaur P M, Samineni S, Sajja S & Chibbar R N (2015). Achievements and challenges in improving nutritional quality of chickpea. Legume Perspectives 09: 31-33.
  • Germšek B, Rozman Č & Unuk T (2017). Forecasting apple fruit color intensity with machine learning methods. Erwerbs-Obstbau 59(2): 109-118. doi.org/10.1007/s10341-016-0305-7
  • George C, McGruder R & Torgerson K (2007). Determination of optimal surface area to volume ratio for thin-layer drying of breadfruit (Artocarpus altilis). International Journal for Service Learning in Engineering 2(2): 76-88. doi.org/10.24908/ijsle.v2i2.2093
  • Gurbuz F, Demi̇r B, Eski İ, Kuş Z A, Yılmaz K U, Ilikçioğlu E & Ercişli S (2018). Estimation of the weights of almond nuts based on physical properties through data mining. Notulae Botanicae Horti Agrobotanici Cluj-Napoca 46(2): 579-584. doi.org/10.15835/nbha46210631
  • Hall M, Frank E, Holmes G, Pfahringer B, Reutemann P & Witten I H (2009). The WEKA data mining Software: An update. SIGKDD Explorations. Available online: http://www.cs.waikato.ac.nz. (Accessed 10 May 2020)
  • Hammer Ø, Harper D A T & Ryan P D (2001). PAST: Paleontological statistics software package for education and data analysis. Palaeontologia Electronica 4(1): 9.
  • Hawkes C (2006). Uneven dietary development: linking the policies and processes of globalization with the nutrition transition, obesity and diet-related chronic diseases. Global Health 2(1): 1-18. doi.org/10.1186/1744-8603-2-4
  • Huang M, Wang Q, Zhang M & Zhu Q (2014). Prediction of color and moisture content for vegetable soybean during drying using hyperspectral imaging technology. Journal of Food Engineering 128: 24-30. doi.org/10.1016/j.jfoodeng.2013.12.008
  • IBM SPSS (2010). Statistical software. SSS Inc., IBM Company©, Version 20.0.
  • Iqbal A, Ateeq N, Khalil I A, Perveen S & Saleemullah S (2006). Physicochemical characteristics and amino acid profile of chickpea cultivars grown in Pakistan. Journal of Foodservice 17(2): 94-101. doi.org/10.1111/j.1745-4506.2006.00024.x
  • Jimenez-Cuesta M, Cuquerella J & Martinez-Javega J M (1982). Determination of a color index for fruit degreening. Proceedings of the International Society of Citriculture, November, 9-12, Tokyo, Japan.
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There are 69 citations in total.

Details

Primary Language English
Subjects Engineering
Journal Section Research Article
Authors

İhsan Serkan Varol 0000-0001-8474-4514

Necati Çetin 0000-0001-8524-8272

Halil Kırnak 0000-0002-6922-5457

Publication Date March 31, 2023
Submission Date November 5, 2021
Acceptance Date August 18, 2022
Published in Issue Year 2023 Volume: 29 Issue: 2

Cite

APA Varol, İ. S., Çetin, N., & Kırnak, H. (2023). Evaluation of Image Processing Technique on Quality Properties of Chickpea Seeds (Cicer arietinum L.) Using Machine Learning Algorithms. Journal of Agricultural Sciences, 29(2), 427-442. https://doi.org/10.15832/ankutbd.1019586

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