Benchmarking Machine Learning Approaches to Evaluate the Cultivar Differentiation of Plum ( Prunus domestica L.) Kernels

Ewa Ropelewska, Xiang Cai, Zhan Zhang, Kadir Sabanci and Muhammet Fatih Aslan
Additional contact information
Ewa Ropelewska: Fruit and Vegetable Storage and Processing Department, The National Institute of Horticultural Research, Konstytucji 3 Maja 1/3, 96-100 Skierniewice, Poland
Xiang Cai: Faculty of Biology, Lomonosov Moscow State University, Leninskie Gory 1, 119991 Moscow, Russia
Zhan Zhang: University Office, Shenzhen MSU-BIT University, Dayun Newtown 1, Shenzhen 518172, China
Kadir Sabanci: Department of Electrical and Electronics Engineering, Karamanoglu Mehmetbey University, Karaman 70100, Turkey
Muhammet Fatih Aslan: Department of Electrical and Electronics Engineering, Karamanoglu Mehmetbey University, Karaman 70100, Turkey

Agriculture, 2022, vol. 12, issue 2, 1-12

Abstract: Plum fruit and kernels offer bioactive material for industrial production. The promising procedure for distinguishing plum kernel cultivars used in this study comprised two stages: image analysis to compute the texture parameters of plum kernels belonging to three cultivars ‘Emper’, ‘Kalipso’, and ‘Polinka’, and discriminant analysis using machine learning algorithms to classify plum kernel cultivars based on selected textures with the highest discriminative power. The discriminative models built separately for sets of textures selected from all color channels L , a , b , R , G , B , U , V , S , X , Y , Z , color space Lab and color channel b using the KStar (Lazy), PART (Rules), and LMT (Trees) classifiers provided the highest average accuracies reaching 98% in the case of the color space Lab and the KStar classifier. In this case, individual cultivars were discriminated with the accuracies of 97% for ‘Emper’ and ‘Kalipso’ to 99% for ‘Polinka’. The values of other performance metrics were also satisfactory, higher than 0.95. The ROC curves were quite smooth and steady with the most satisfactory curve for the ‘Kalipso’ kernels. The present study sheds light on an objective, non-destructive, and inexpensive procedure for cultivar discrimination of plum kernels.

Keywords: plum kernel images; texture parameters; discrimination; algorithms; performance metrics (search for similar items in EconPapers)
JEL-codes: Q1 Q10 Q11 Q12 Q13 Q14 Q15 Q16 Q17 Q18 (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (4)

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