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Machine learning based mass prediction and discrimination of chickpea (Cicer arietinum L.) cultivars

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Chickpea is an important edible legume that can be grown in rain fed conditions. Image analysis and machine learning could be used for rapid and non-destructive determination of seed physical attributes and such techniques yield objective, accurate and reliable results. In this study, size, shape, and area attributes of 26 different chickpea cultivars were determined by image processing method, and color properties were determined by chromametric method, and machine learning algorithms (Multilayer Perceptron-MLP, Random Forest-RF, Support Vector Regression-SVR, and k-Nearest Neighbor-kNN, were used for mass prediction of chickpea seeds. Ilgaz and Çakır cultivars had the highest size and shape values, while İzmir and Sezenbey cultivars had the highest color attributes. Compactness (in horizontal orientation) had a positive correlation with the equivalent diameter (in vertical orientation) and elongation (in vertical orientation) (r = 0.99 for both parameters). Besides, a* had a high correlation with b* (r = 0.97). According to Euclidean distances, Akça–İnci and Damla–Işık cultivars were identified as the closest cultivars in terms of physical attributes. In PCA analysis, PC1 and PC2 explained 73.17% of the total variation. The PC1 included length, geometric mean diameter, volume and surface area, and the PC2 included roundness (in horizontal orientation), thickness, elongation (in horizontal orientation) and sphericity. RF and ML had successful results with the values of 0.8054 and 0.8043 for train-test split, and 0.8231 and 0.8142 for k-fold cross validation, respectively. Present findings revealed that texture image processing and machine learning could be used as an effective and inexpensive discrimination tool for chickpea seeds.

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References

  • Abbaspour-Gilandeh Y, Azizi A (2018) Identification of cracks in eggs shell using computer vision and hough transform Yuzuncu Yıl Univ. J Agric Sci 28:375–383

    Google Scholar 

  • Abu-Salem FM, Abou-Arab EA (2011) Physico-chemical properties of tempeh produced from chickpea seeds. J Am Sci 7:107–118

    Google Scholar 

  • Bacci L, Rapi B, Novaro P (2002) Durum wheat quality evaluation software. In World Congress of Computers in Agriculture and Natural Resources, Proceedings of the 2002 Conference (p. 49). American Society of Agricultural and Biological Engineers.

  • Durum wheat quality evaluation software world congress of computers in agriculture and natural resources Proceedings of the 2002 Conference

  • Cetin N, Yaman M, Karaman K, Demir B (2020) Determination of some physicomechanical and biochemical parameters of hazelnut (Corylus avellana L) cultivars. Turkish J Agric Forest 44(3):1–12

    Google Scholar 

  • Chavan JK, Kadam SS, Salunkhe DK, Beuchat LR (1987) Biochemistry and technology of chickpea (Cicer arietinum L) seeds. Crit Rev Food Sci Nutr 25(2):107–158

    Article  Google Scholar 

  • Colton T (1974) Statistics in medicine. Little Brown and Company

    Google Scholar 

  • Çetin N, Karaman K, Beyzi E, SaÄŸlam C, Demirel B (2021) Comparative evaluation of some quality characteristics of sunfower oilseeds (Helianthus annuus L) through machine learning classifiers. Food Anal Methods 14:1666–1681

    Article  Google Scholar 

  • Çetin N (2022) Machine learning for varietal binary classification of soybean (Glycine max (l) merrill) seeds based on shape and size attributes. Food Anal Methods 15:2260–2273

    Article  Google Scholar 

  • de Camargo AC, Favero BT, Morzelle MC, Franchin M, Alvarez-Parrilla E, de la Rosa LA (2019) Is chickpea a potential substitute for soybean? Phenolic bioactives and potential health benefits. Int J Molec Sci 20:26–44

    Article  Google Scholar 

  • Demir B, Eski Ä°, KuÅŸ ZA, ErciÅŸli S (2017) Prediction of physical parameters of pumpkin seeds using neural network. Notulae Botan Horti Agrobot Cluj-Napoca 45(1):22–27

    Article  Google Scholar 

  • 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 Aceites 69(4):e271

    Article  Google Scholar 

  • Demir B, Eski Ä°, Gürbüz F, KuÅŸ ZA, Sesli Y, ErciÅŸli S (2020) Prediction of walnut mass based on physical attributes by Artificial Neural Network (ANN). Erwerbs-Obstbau 62(1):47–56

    Article  Google Scholar 

  • DüzgüneÅŸ O, Kesici T, Kavuncu O, Gürbüz F (1987) AraÅŸtırma ve Deneme Metodları (Ä°statistik Metodları II). Ankara Ãœniv Ziraat Fak Yayınları: 1021 Ders Kitabı, 295 Ankara (In Turkish)

  • Eissa AHA, Mohamed MA, Moustafa H, Alghannam ARO (2010) Moisture dependent physical and mechanical properties of chickpea seeds. Int J Agric Biol Eng 3:80–93

    Google Scholar 

  • Fathizadeh Z, Aboonajmi M, Hassan-Beygi SR (2021) Nondestructive methods for determining the firmness of apple fruit flesh. Inf Processing Agric 8:515–527

    Google Scholar 

  • Food and Agriculture Organization of the United Nations (FAO) (2019) http://wwwfaoorg/faostat/en/#data/QC Accessed date: 06012022

  • George C, McGruder R, Torgerson K (2007) Determination of optimal surface area to volume ratio for thin-layer drying of breadfruit (Artocarpus altilis). Int J Serv Learn Eng 2:76–88

    Google Scholar 

  • Gonzalez JPB, Ortiz F, A P Rodriguez CPP, (2017) Mass and volume estimation of passion fruit using digital images. IEEE Latin America Trans 15(2):275–282

    Article  Google Scholar 

  • Guidoti DT, Gonela A, Vidigal MCG, Conrado TV, Romani I (2018) Interrelationship between morphological agronomic and molecular characteristics in the analysis of common bean genetic diversity. Acta Scient Agron 40

  • Gürbüz F, Demi̇r B, Eski I, KuÅŸ ZA, Yılmaz KU, Ä°likçioÄŸlu E, ErciÅŸli S (2018) Estimation of the weights of almond nuts based on physical properties through data mining. Notulae BotanHorti Agrobot Cluj-Napoca 46(2):579–584

    Article  Google Scholar 

  • Güzel D, Sayar S (2012) Effect of cooking methods on selected physicochemical and nutritional properties of barlotto bean chickpea faba bean and white kidney bean. J Food Sci Tech 49:89–95

    Article  Google Scholar 

  • Iqbal A, Ateeq N, Khalil IA, Perveen S, Saleemullah S (2006) Physicochemical characteristics and amino acid profile of chickpea cultivars grown in Pakistan. J Foodserv 17:94–101

    Article  Google Scholar 

  • Jogihalli P, Singh L, Kumar K, Sharanagat VS (2017) Physico-functional and antioxidant properties of sand-roasted chickpea (Cicer arietinum). Food Chem 237:1124–1132

    Article  CAS  Google Scholar 

  • Kara M, Sayinci B, Elkoca E, Öztürk Ä°, Özmen TB (2013) Seed size and shape analysis of registered common bean (Phaseolus vulgaris L) cultivars in Turkey using digital photography. J Agric Sci 19:219–234

    Google Scholar 

  • Kibar H, Öztürk T, Temizel KE (2014) Effective engineering properties in the design of storage structures of postharvest dry bean grain. Acta Scient Agron 36:147–158

    Article  Google Scholar 

  • Kus ZA, Demir B, Eski I, Gurbuz F, Ercisli S (2017) Estimation of the colour properties of apples varieties using neural network. Erwerbs-Obstbau 59(4):291–299

    Article  Google Scholar 

  • Masoumi AA, Tabil L (2003) Physical properties of chickpea (C arietinum) cultivars. In 2003 ASAE Annual Meeting (p 1) American Soc Agric Biol Eng 1–14

  • McGuire RG (1992) Reporting of objective colour measurements. HortScience 27(12):1254–1255

    Article  Google Scholar 

  • Nikoobin M, Mirdavardoost F, Kashaninejad M, Soltani A (2009) Moisture-dependent physical properties of chickpea seeds. J Food Proc Eng 32:544–564

    Article  Google Scholar 

  • Omid M, Khojastehnazhand M, Tabatabaeefar A (2010) Estimating volume and mass of citrus fruits by image processing technique. J Food Eng 100(2):315–321

    Article  Google Scholar 

  • Ozturk I, Kara M (2009) Physico-chemical grain properties of new common bean cv‘Elkoca-05.’ Sci Res Essays 4:88–93

    Google Scholar 

  • Parker JR (2001) Rank and response combination from confusion matrix data. Inform Fus 2(2):113–120

    Article  Google Scholar 

  • Rad MRN, Fanaei HR, Rad MRP (2015) Application of Artificial Neural Networks to predict the final fruit weight and random forest to select important variables in native population of melon (Cucumis melo L). Sci Hortic 181:108–112

    Article  Google Scholar 

  • Moosavi AA, Sepaskhah A (2012) Artificial neural networks for predicting unsaturated soil hydraulic characteristics at different applied tensions. Arch Agron Soil Sci 58(2):125–153

    Article  Google Scholar 

  • Naroui Rad MR, Ghalandarzehi A, Koohpaygani JA (2017) Predicting eggplant individual fruit weight using an artificial neural network. Int J Veg Sci 23(4):331–339

    Article  Google Scholar 

  • Ozaktan H (2021) Technological characteristics of chickpea (Cicer arietinum L.) cultivars grown under natural conditions. Turkish J Field Crops 26(2):235–243

    Google Scholar 

  • Ropelewska E, Jankowski KJ (2019) Classification of the seeds of traditional and double-low cultivars of white mustard based on texture features. J Food Process Eng 42:13077

    Article  Google Scholar 

  • Ropelewska E (2020) The use of seed texture features for discriminating different cultivars of stored apples. J Stored Prod Res 88:101668

    Article  Google Scholar 

  • Ropelewska E, Sabanci K, Aslan MF, Azizi A (2022) A novel approach to the authentication of apricot seed cultivars using innovative models based on image texture parameters. Horticulturae 8:431

    Article  Google Scholar 

  • Sabanci K, Aslan MF, Ropelewska E, Unlersen MF (2021) A convolutional neural network-based comparative study for pepper seed classification: analysis of selected deep features with support vector machine. J Food Process Eng 25:13955

    Google Scholar 

  • Saglam C, Cetin N (2022) Prediction of pistachio (Pistacia vera L) mass based on shape and size attributes by using machine learning algorithms. Food AnalMethods 15(3):739–750

    Google Scholar 

  • Sastry DVSSR, Upadhyaya HD, Srinivas TR (2019) Variation for seed physical and hydration properties of chickpea (Cicer arietinum L) mini core collection and their relevance to conservation and utilization. Plant Gen Resour 17:311–324

    Article  CAS  Google Scholar 

  • Sayinci B, Ercisli S, Ozturk I, Eryilmaz Z, Demir B (2012) Determination of size and shape in the ‘Moro’ blood orange and ‘Valencia’ sweet orange cultivar and its mutants using image processing. Notulae Botan Horti Agrobot Cluj-Napoca 40:234–242

    Article  Google Scholar 

  • Sayıncı B, Kara M, ErciÅŸli S, Duyar Ö, Ertürk Y (2015) Elliptic Fourier analysis for shape distinction of Turkish hazelnut cultivars. Erwerbs-Obstbau 57:1–11

    Article  Google Scholar 

  • Shabani A, Ghaffary KA, Sepaskhah AR, Kamgar-Haghighi AA (2017) Using the artificial neural network to estimate leaf area. Sci Hortic 216:103–110

    Article  Google Scholar 

  • Simonyan KJ, Yiljep YD, Oyatoyan OB, Bawa GS (2009) Effects of moisture content on some physical properties of Lablab purpureus (L) sweet seeds. Agric Eng Int CIGR Journal 11:1–13

    Google Scholar 

  • Soares JDR, Pasqual M, Lacerda WS, Silva SO, Donato SLR (2013) Utilization of artificial neural networks in the prediction of the bunches’ weight in banana plants. Sci Hortic 155:24–29

    Article  Google Scholar 

  • Symons SJ, Van Schepdael L, Dexter JE (2003) Measurement of hard vitreous kernels in Durum wheat by machine vision. Cereal Chem 80:511–517

    Article  CAS  Google Scholar 

  • Varol IS, Yusuf Murat K, Irik HA, Kirnak H, Kaplan M (2020) Supplementary irrigations at different physiological growth stages of chickpea (Cicer arietinum L.) change grain nutritional composition. Food Chem 303:125402

    Article  CAS  Google Scholar 

  • Witten IH, Frank E (2005) Data mining: practical machine learning tools and techniques. Morgan Kaufmann Press, San Francisco

    Google Scholar 

Download references

Funding

This study was supported by Turkish Scientific Research Council (TUBITAK) with the project number of 119O226.

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Authors and Affiliations

  1. Department of Biosystems Engineering, Faculty of Agriculture, Erciyes University, Kayseri, Turkey

    Necati Çetin

  2. Department of Agricultural Machinery and Technologies Engineering, Faculty of Agriculture, Ankara University, Ankara, Turkey

    Necati Çetin

  3. Department of Field Crops, Faculty of Agriculture, Erciyes University, Kayseri, Turkey

    Hamdi Ozaktan & Satı Uzun

  4. Department of Soil Science, Faculty of Agriculture, Erciyes University, Kayseri, Turkey

    Oguzhan Uzun

  5. Department of Field Crops, Faculty of Agriculture, Ankara University, Ankara, Turkey

    Cemalettin Yasar Ciftci

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  1. Necati Çetin
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Çetin, N., Ozaktan, H., Uzun, S. et al. Machine learning based mass prediction and discrimination of chickpea (Cicer arietinum L.) cultivars. Euphytica 219, 20 (2023). https://doi.org/10.1007/s10681-022-03150-5

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