Research Article
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Multivariate Machine Learning Approach for Size and Shape Prediction of Sunflower Seeds

Year 2022, Volume: 12 Issue: 4, 2034 - 2044, 01.12.2022
https://doi.org/10.21597/jist.1115119

Abstract

Sunflower constitutes an important source of protein, mineral, vitamin, fatty acid, and offer a balanced source of amino acids. Machine learning is mostly performed for the prediction of descriptive attributes in the quality evaluation of foods. In this study physical attributes of two different sunflower varieties (Metinbey and İnegöl Alası) were determined and algorithms were applied for size and shape prediction of these varieties. In addition, five different machine learning predictors were used as Multilayer Perceptron (MLP), Gaussian Processes (GP), Random Forest (RF), k-Nearest Neighbors (kNN), and Support Vector Regression (SVR). The prediction of surface area, volume, geometric mean diameter, aspect ratio, elongation, and shape index were based on the main physical attributes. İnegöl Alası variety had the greatest physical attributes. The seed length, width and thickness were obtained from İnegöl Alası variety as 23.89, 8.80 and 4.15 mm and from Metinbey as 17.88, 6.20 and 4.01 mm. All varieties were determined as significant in terms of the selected attributes as reported by Pillai Trace and Wilks’ Lambda (p<0.01). In the Wilks’ Lambda statistics, unexplained of the similarities or differences among the groups was 12.30%. Present findings revealed that MLP and SVR algorithms had the greatest correlation coefficients for all predicted attributes. In the study, the best predicted attributes were geometric mean diameter with an R value of 0.9989 (SVR), followed by volume and elongation with an R value of 0.9988 (MLP). Present findings revealed that MLP and SVR algorithms could potentially be used for size and shape prediction of sunflower varieties.

References

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  • Badouin H, Gouzy J, Grassa CJ, Murat F, Staton SE, Cottret L, Legrand L, 2017. The Sunflower Genome Provides Insights into Oil Metabolism, Flowering and Asterid Evolution. Nature 546:148-152.
  • Berhane T, Lane C, Wu Q, Autrey B, Anenkhonov O, Chepinoga V, Liu H, 2018. Decision-Tree, Rule-Based, and Random Forest Classification of High-Resolution Multispectral Imagery for Wetland Mapping and Inventory. Remote Sensing 10:580.
  • Breiman L, 2001. Random Forests. Machine Learning 45(1):5-32.
  • Bwade KE, Aliyu B, 2012. Investigations on the Effect of Moisture Content and Variety Factors on Some Physical Properties of Pumpkin Seed (Cucurbitaceae spp). International Journal of Engineering, Business and Enterprise Applications (IJEBEA) 3(1):20-24.
  • Cetin N, Yaman M, Karaman K, Demir B, 2020. Determination of Some Physicomechanical and Biochemical Parameters of Hazelnut (Corylus avellana L.) Cultivars. Turkish Journal of Agriculture and Forestry 44(5): 439-450.
  • Colton T, 1974. Statistics in Medicine. Little Brown and Co, New York.
  • 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.
  • Çetin N, Karaman K, Beyzi E, Sağlam C, Demirel B, 2021. Comparative Evaluation of Some Quality Characteristics of Sunflower Oilseeds (Helianthus annuus L.) Through Machine Learning Classifiers. Food Analytical Methods 14(8):1666-1681.
  • Çetin N, Sağlam C, 2022. Rapid Detection of Total Phenolics, Antioxidant Activity and Ascorbic Acid of Dried Apples by Chemometric Algorithms. Food Bioscience 47: 101670.
  • Çetin N, 2022. Machine Learning for Varietal Binary Classification of Soybean (Glycine max (L.) Merrill) Seeds Based on Shape and Size Attributes. Food Analytical Methods 15: 2260-2273.
  • Demir B, Eski I, Kus ZA, Ercisli S, 2017. Prediction of Physical Parameters of Pumpkin Seeds using Neural Network. Notulae Botanicae Horti Agrobotanici Cluj-Napoca 45(1):22-27.
  • 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): e271.
  • Ergen Y, Sağlam C, 2005. Bazı Çerezlik Ayçiçeği (Helianthus annuus L.) Çeşitlerinin Tekirdağ Koşullarinda Verim ve Verim Unsurlari. Tekirdağ Ziraat Fakültesi Dergisi 2(3):221-227.
  • Eski İ, Demir B, Gürbüz F, Kuş ZA, Uğurtan Yilmaz K, Uzun M, Ercişli S, 2018. Design of Neural Network Predictor for the Physical Properties of Almond Nuts. Erwerbs-Obstbau 60(2): 153-160.
  • FAOSTAT, 2018. Food and Agriculture Organization of the United Nations (FAO), http://www.fao.org/site, (accessed date: 22.03.2022).
  • Hammer Ø, Harper DAT, Ryan PD, 2001. PAST: Paleontological Statistics Software Package for Education and Data Analysis. Palaeontologia Electronica 4(1): 1-9.
  • IBM SPSS®, 2010. Statistical Software. SSS Inc., IBM Company©, Version 20.0.
  • Jafari S, Khazaei J, Arabhosseini A, Massah J, Khoshtaghaza MH, 2011. Study on Mechanical Properties of Sunflower Seeds. Food Science and Technology 14(1):1-12.
  • Kays SJ, 1999. Preharvest Factors Affecting Appearance. Postharvest Biology and Techonolgy 15: 233–247.
  • Khodabakhshian R, Emadi B, Fard MA, 2010. Some Engineering Properties of Sunflower Seed and its Kernel. Nong Ye Ke Xue Yu Ji Shu, 4(4):37-46.
  • MacKay DJC, 1998. Introduction to Gaussian processes. NATO ASI Series F Computer and Systems Sciences 168, 133-166.
  • Malik MA, Saini CS, 2016. Engineering Properties of Sunflower Seed: Effect of Dehulling and Moisture Content. Cogent Food & Agriculture 2(1):1-11.
  • Maxwell AE, Warner TA, Fang F, 2018. Implementation of Machine-learning Classification in Remote Sensing: An Applied Review. International Journal of Remote Sensing 39:2784-2817.
  • Mollazade K, Omid M, Arefi A, 2012. Comparing Data Mining Classifiers for Grading Raisins based on Visual Features. Computers and Electronics in Agriculture 84:124-131.
  • Nettleton DF, Orriols-Puig A, Fornells A, 2010. A Study of the Effect of Different Types of Noise on the Precision of Supervised Learning Techniques. Artificial Intelligence Review 33:275-306.
  • Omid M, Khojastehnazhand M, Tabatabaeefar A, 2010. Estimating Volume and Mass of Citrus Fruits by Image Processing Technique. Journal of Food Engineering 100(2):315-321.
  • Ortiz-Hernandez AA, Araiza-Esquivel M, Delgadillo-Ruiz L, Ortega-Sigala JJ, Durán-Muñoz HA, Mendez-Garcia VH, Vega-Carrillo HR, 2020. Physical Characterization of Sunflower Seeds Dehydrated by Using Electromagnetic Induction and Low-Pressure System. Innovative Food Science & Emerging Technologies 60:102285.
  • Rasmussen CE, Williams CK, 2006. Gaussian Process for Machine Learning. MIT Press, Cambridge, Massachusetts.
  • Parker JR, 2001. Rank and Response Combination from Confusion Matrix Data. Information Fusion 2(2), 113-120.
  • Ponce JM, Aquino A, Millán B, Andújar JM, 2018. Olive-Fruit Mass and Size Estimation using Image Analysis and Feature Modeling. Sensors 18(9):2930.
  • Romero JR, Roncallo PF, Akkiraju PC, Ponzoni I, Echenique VC, Carballido JA, 2013. Using classification algorithms for predicting durum wheat yield in the province of Buenos Aires. Computers and Electronics in Agriculture 96:173-179.
  • Saha KK, Uddin MZ, Rahman MM, Moniruzzaman M, Ali MA, Oliver MMH, 2021. Estimation of Cardamom Capsule Size and Surface Area Using Digital Image Processing Technique. Journal of the Bangladesh Agricultural University 19(3):398-405.
  • Sahay KM, Singh KK, 2004. Unit Operations of Agricultural Processing. Vikas Publishing House PVT LTD.
  • Sağlam C, Çetin N, 2022a. Machine Learning Algorithms to Estimate Drying Characteristics of Apples Slices Dried with Different Methods. Journal of Food Processing and Preservation e16496.
  • Saglam C, Cetin N, 2022b. Prediction of Pistachio (Pistacia vera L.) Mass Based on Shape and Size Attributes by using Machine Learning Algorithms. Food Analytical Methods 15(3): 739-750.
  • Santalla EM, Mascheroni RH, 2003. Equilibrium Moisture Characteristics of High Oleic Sunflower Seeds and Kernels. Drying Technology 21(1):147-163.
  • Seiler GJ, 2007. Wild Annual Helianthus Anomalus and H. Deserticola for Improving Oil Content and Quality in Sunflower. Industrial Crops and Products 25:95–100.
  • Singh SK, Vidyarthi SK, Tiwari R, 2020. Machine Learnt Image Processing to Predict Weight and Size of Rice Kernels. Journal of Food Engineering 274:109828.
  • Stegmayer G, Milone DH, Garran S, Burdyn L, 2013. Automatic Recognition of Quarantine Citrus Diseases. Expert Systems with Applications 40(9):3512-3517.
  • Tabatabaeefar A, Rajabipour A, 2005. Modeling the Mass of Apples by Geometrical Attributes. Scientia Horticulturae 105(3):373-382.
  • Vapnik VN, 2000. Methods of Pattern Recognition. The Nature of Statistical Learning Theory. Springer, New York.
  • Xu G, Shen C, Liu M, Zhang F, Shen W, 2017. A User Behavior Prediction Model Based on Parallel Neural Network and k-Nearest Neighbor Algorithms. Cluster Computing 20(2): 1703-1715.
  • Zhang, R., Ma, J. (2009). Feature Selection for Hyperspectral Data Based on Recursive Support Vector Machines. International Journal of Remote Sensing 30: 3669-3677.
  • Zhang H, Song T, Wang K, Wang G, Hu H, Zeng F, 2012. Prediction of Crude Protein Content in Rice Grain with Canopy Spectral Reflectance. Plant, Soil and Environment 58:514-520.
Year 2022, Volume: 12 Issue: 4, 2034 - 2044, 01.12.2022
https://doi.org/10.21597/jist.1115119

Abstract

References

  • Ataş M, Yardimci Y, Temizel A, 2012. A New Approach to Aflatoxin Detection in Chili Pepper by Machine Vision. Computers and Electronics in Agriculture 87:129-141.
  • Badouin H, Gouzy J, Grassa CJ, Murat F, Staton SE, Cottret L, Legrand L, 2017. The Sunflower Genome Provides Insights into Oil Metabolism, Flowering and Asterid Evolution. Nature 546:148-152.
  • Berhane T, Lane C, Wu Q, Autrey B, Anenkhonov O, Chepinoga V, Liu H, 2018. Decision-Tree, Rule-Based, and Random Forest Classification of High-Resolution Multispectral Imagery for Wetland Mapping and Inventory. Remote Sensing 10:580.
  • Breiman L, 2001. Random Forests. Machine Learning 45(1):5-32.
  • Bwade KE, Aliyu B, 2012. Investigations on the Effect of Moisture Content and Variety Factors on Some Physical Properties of Pumpkin Seed (Cucurbitaceae spp). International Journal of Engineering, Business and Enterprise Applications (IJEBEA) 3(1):20-24.
  • Cetin N, Yaman M, Karaman K, Demir B, 2020. Determination of Some Physicomechanical and Biochemical Parameters of Hazelnut (Corylus avellana L.) Cultivars. Turkish Journal of Agriculture and Forestry 44(5): 439-450.
  • Colton T, 1974. Statistics in Medicine. Little Brown and Co, New York.
  • 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.
  • Çetin N, Karaman K, Beyzi E, Sağlam C, Demirel B, 2021. Comparative Evaluation of Some Quality Characteristics of Sunflower Oilseeds (Helianthus annuus L.) Through Machine Learning Classifiers. Food Analytical Methods 14(8):1666-1681.
  • Çetin N, Sağlam C, 2022. Rapid Detection of Total Phenolics, Antioxidant Activity and Ascorbic Acid of Dried Apples by Chemometric Algorithms. Food Bioscience 47: 101670.
  • Çetin N, 2022. Machine Learning for Varietal Binary Classification of Soybean (Glycine max (L.) Merrill) Seeds Based on Shape and Size Attributes. Food Analytical Methods 15: 2260-2273.
  • Demir B, Eski I, Kus ZA, Ercisli S, 2017. Prediction of Physical Parameters of Pumpkin Seeds using Neural Network. Notulae Botanicae Horti Agrobotanici Cluj-Napoca 45(1):22-27.
  • 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): e271.
  • Ergen Y, Sağlam C, 2005. Bazı Çerezlik Ayçiçeği (Helianthus annuus L.) Çeşitlerinin Tekirdağ Koşullarinda Verim ve Verim Unsurlari. Tekirdağ Ziraat Fakültesi Dergisi 2(3):221-227.
  • Eski İ, Demir B, Gürbüz F, Kuş ZA, Uğurtan Yilmaz K, Uzun M, Ercişli S, 2018. Design of Neural Network Predictor for the Physical Properties of Almond Nuts. Erwerbs-Obstbau 60(2): 153-160.
  • FAOSTAT, 2018. Food and Agriculture Organization of the United Nations (FAO), http://www.fao.org/site, (accessed date: 22.03.2022).
  • Hammer Ø, Harper DAT, Ryan PD, 2001. PAST: Paleontological Statistics Software Package for Education and Data Analysis. Palaeontologia Electronica 4(1): 1-9.
  • IBM SPSS®, 2010. Statistical Software. SSS Inc., IBM Company©, Version 20.0.
  • Jafari S, Khazaei J, Arabhosseini A, Massah J, Khoshtaghaza MH, 2011. Study on Mechanical Properties of Sunflower Seeds. Food Science and Technology 14(1):1-12.
  • Kays SJ, 1999. Preharvest Factors Affecting Appearance. Postharvest Biology and Techonolgy 15: 233–247.
  • Khodabakhshian R, Emadi B, Fard MA, 2010. Some Engineering Properties of Sunflower Seed and its Kernel. Nong Ye Ke Xue Yu Ji Shu, 4(4):37-46.
  • MacKay DJC, 1998. Introduction to Gaussian processes. NATO ASI Series F Computer and Systems Sciences 168, 133-166.
  • Malik MA, Saini CS, 2016. Engineering Properties of Sunflower Seed: Effect of Dehulling and Moisture Content. Cogent Food & Agriculture 2(1):1-11.
  • Maxwell AE, Warner TA, Fang F, 2018. Implementation of Machine-learning Classification in Remote Sensing: An Applied Review. International Journal of Remote Sensing 39:2784-2817.
  • Mollazade K, Omid M, Arefi A, 2012. Comparing Data Mining Classifiers for Grading Raisins based on Visual Features. Computers and Electronics in Agriculture 84:124-131.
  • Nettleton DF, Orriols-Puig A, Fornells A, 2010. A Study of the Effect of Different Types of Noise on the Precision of Supervised Learning Techniques. Artificial Intelligence Review 33:275-306.
  • Omid M, Khojastehnazhand M, Tabatabaeefar A, 2010. Estimating Volume and Mass of Citrus Fruits by Image Processing Technique. Journal of Food Engineering 100(2):315-321.
  • Ortiz-Hernandez AA, Araiza-Esquivel M, Delgadillo-Ruiz L, Ortega-Sigala JJ, Durán-Muñoz HA, Mendez-Garcia VH, Vega-Carrillo HR, 2020. Physical Characterization of Sunflower Seeds Dehydrated by Using Electromagnetic Induction and Low-Pressure System. Innovative Food Science & Emerging Technologies 60:102285.
  • Rasmussen CE, Williams CK, 2006. Gaussian Process for Machine Learning. MIT Press, Cambridge, Massachusetts.
  • Parker JR, 2001. Rank and Response Combination from Confusion Matrix Data. Information Fusion 2(2), 113-120.
  • Ponce JM, Aquino A, Millán B, Andújar JM, 2018. Olive-Fruit Mass and Size Estimation using Image Analysis and Feature Modeling. Sensors 18(9):2930.
  • Romero JR, Roncallo PF, Akkiraju PC, Ponzoni I, Echenique VC, Carballido JA, 2013. Using classification algorithms for predicting durum wheat yield in the province of Buenos Aires. Computers and Electronics in Agriculture 96:173-179.
  • Saha KK, Uddin MZ, Rahman MM, Moniruzzaman M, Ali MA, Oliver MMH, 2021. Estimation of Cardamom Capsule Size and Surface Area Using Digital Image Processing Technique. Journal of the Bangladesh Agricultural University 19(3):398-405.
  • Sahay KM, Singh KK, 2004. Unit Operations of Agricultural Processing. Vikas Publishing House PVT LTD.
  • Sağlam C, Çetin N, 2022a. Machine Learning Algorithms to Estimate Drying Characteristics of Apples Slices Dried with Different Methods. Journal of Food Processing and Preservation e16496.
  • Saglam C, Cetin N, 2022b. Prediction of Pistachio (Pistacia vera L.) Mass Based on Shape and Size Attributes by using Machine Learning Algorithms. Food Analytical Methods 15(3): 739-750.
  • Santalla EM, Mascheroni RH, 2003. Equilibrium Moisture Characteristics of High Oleic Sunflower Seeds and Kernels. Drying Technology 21(1):147-163.
  • Seiler GJ, 2007. Wild Annual Helianthus Anomalus and H. Deserticola for Improving Oil Content and Quality in Sunflower. Industrial Crops and Products 25:95–100.
  • Singh SK, Vidyarthi SK, Tiwari R, 2020. Machine Learnt Image Processing to Predict Weight and Size of Rice Kernels. Journal of Food Engineering 274:109828.
  • Stegmayer G, Milone DH, Garran S, Burdyn L, 2013. Automatic Recognition of Quarantine Citrus Diseases. Expert Systems with Applications 40(9):3512-3517.
  • Tabatabaeefar A, Rajabipour A, 2005. Modeling the Mass of Apples by Geometrical Attributes. Scientia Horticulturae 105(3):373-382.
  • Vapnik VN, 2000. Methods of Pattern Recognition. The Nature of Statistical Learning Theory. Springer, New York.
  • Xu G, Shen C, Liu M, Zhang F, Shen W, 2017. A User Behavior Prediction Model Based on Parallel Neural Network and k-Nearest Neighbor Algorithms. Cluster Computing 20(2): 1703-1715.
  • Zhang, R., Ma, J. (2009). Feature Selection for Hyperspectral Data Based on Recursive Support Vector Machines. International Journal of Remote Sensing 30: 3669-3677.
  • Zhang H, Song T, Wang K, Wang G, Hu H, Zeng F, 2012. Prediction of Crude Protein Content in Rice Grain with Canopy Spectral Reflectance. Plant, Soil and Environment 58:514-520.
There are 45 citations in total.

Details

Primary Language English
Subjects Agronomy
Journal Section Biyosistem Mühendisliği / Biosystem Engineering
Authors

Necati Çetin 0000-0001-8524-8272

Early Pub Date November 25, 2022
Publication Date December 1, 2022
Submission Date May 10, 2022
Acceptance Date August 26, 2022
Published in Issue Year 2022 Volume: 12 Issue: 4

Cite

APA Çetin, N. (2022). Multivariate Machine Learning Approach for Size and Shape Prediction of Sunflower Seeds. Journal of the Institute of Science and Technology, 12(4), 2034-2044. https://doi.org/10.21597/jist.1115119
AMA Çetin N. Multivariate Machine Learning Approach for Size and Shape Prediction of Sunflower Seeds. J. Inst. Sci. and Tech. December 2022;12(4):2034-2044. doi:10.21597/jist.1115119
Chicago Çetin, Necati. “Multivariate Machine Learning Approach for Size and Shape Prediction of Sunflower Seeds”. Journal of the Institute of Science and Technology 12, no. 4 (December 2022): 2034-44. https://doi.org/10.21597/jist.1115119.
EndNote Çetin N (December 1, 2022) Multivariate Machine Learning Approach for Size and Shape Prediction of Sunflower Seeds. Journal of the Institute of Science and Technology 12 4 2034–2044.
IEEE N. Çetin, “Multivariate Machine Learning Approach for Size and Shape Prediction of Sunflower Seeds”, J. Inst. Sci. and Tech., vol. 12, no. 4, pp. 2034–2044, 2022, doi: 10.21597/jist.1115119.
ISNAD Çetin, Necati. “Multivariate Machine Learning Approach for Size and Shape Prediction of Sunflower Seeds”. Journal of the Institute of Science and Technology 12/4 (December 2022), 2034-2044. https://doi.org/10.21597/jist.1115119.
JAMA Çetin N. Multivariate Machine Learning Approach for Size and Shape Prediction of Sunflower Seeds. J. Inst. Sci. and Tech. 2022;12:2034–2044.
MLA Çetin, Necati. “Multivariate Machine Learning Approach for Size and Shape Prediction of Sunflower Seeds”. Journal of the Institute of Science and Technology, vol. 12, no. 4, 2022, pp. 2034-4, doi:10.21597/jist.1115119.
Vancouver Çetin N. Multivariate Machine Learning Approach for Size and Shape Prediction of Sunflower Seeds. J. Inst. Sci. and Tech. 2022;12(4):2034-4.