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Dergiler / Tarım Bilimleri Dergisi / 2026 / Volume: 32 Issue: 3

Hazelnut (Corylus avellana L.) Mass Prediction Using Machine Learning Models Based on Physical Attributes

Khaled Adil Dawood Idress(ONDOKUZ MAYIS UNIVERSITY)0000-0002-1631-6232
Omsalma Alsadig Adam Gadalla(ONDOKUZ MAYIS UNIVERSITY)0000-0001-6132-4672
Geofrey Baitu(ONDOKUZ MAYIS UNIVERSITY)0000-0002-3243-3252
Mohamedeltayib Omer Salih Eissa(ONDOKUZ MAYIS UNIVERSITY)0000-0003-0186-1112
Y. Benal Öztekin(ONDOKUZ MAYIS UNIVERSITY)0000-0003-2387-2322
Sayfa
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Yayın Tarihi
2026-07-28
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⚡ TL;DR(1 Cümlede Hızlı Çıkarım)

These findings highlight the potential of ML techniques in non destructive mass estimation of hazelnuts, with prospects for enhancement through the integration of chemical attributes for improved cultivar discrimination and mass prediction accuracy.

📌 3 Maddede Bu Makale

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Amaç & Hipotez:Türkiye is the dominant global producer of hazelnuts, supplying approximately 70% of the world’s total output.
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Temel Bulgular & Veri:A total of 1,050 hazelnuts from seven cultivars (Çakıldak, Kalınkara, Palaz, Sivri, Tombul, Yerli, and Yomra), collected from orchards in Türkiye's Black Sea region, were analyzed.
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Sonuç & Katkı:These findings highlight the potential of ML techniques in non destructive mass estimation of hazelnuts, with prospects for enhancement through the integration of chemical attributes for improved cultivar discrimination and mass prediction accuracy.
🔬Tür:Modelleme & Sayısal Simülasyon
📍Kapsam:Genişletilmiş Akademik Örneklem
⚙️Değişkenler:Geometric Descriptors · Predictive Modelling · Mass Prediction

Abstract

Türkiye is the dominant global producer of hazelnuts, supplying approximately 70% of the world’s total output. The physical characteristics of hazelnuts, particularly mass, play a critical role in determining quality and shelf life. To improve efficiency in hazelnut handling, this study explores non-destructive, rapid prediction of hazelnut mass using machine learning (ML) models based on physical attributes such as size and shape. A total of 1,050 hazelnuts from seven cultivars (Çakıldak, Kalınkara, Palaz, Sivri, Tombul, Yerli, and Yomra), collected from orchards in Türkiye's Black Sea region, were analyzed. Each sample was measured for length, width, and thickness using digital calipers, and weighed with ±0.001 g precision. Derived attributes—including volume, geometric mean diameter, projected area, surface area, sphericity, shape index, aspect ratio, and elongation—were computed mathematically. Three ML models were evaluated: Linear Regression, Random Forest, and Artificial Neural Networks (ANN). Among these, the Artificial Neural Networks and Linear Regression models achieved the highest prediction performance (R² = 0.87, 0.871 for kernel, and 0.802, 0.804 for kernels, respectively). These findings highlight the potential of ML techniques in non destructive mass estimation of hazelnuts, with prospects for enhancement through the integration of chemical attributes for improved cultivar discrimination and mass prediction accuracy.

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Bu Makaleye Atıf Yap

Idress, K. A. D., Gadalla, O. A. A., Baitu, G., Eissa, M. O. S., Öztekin, Y. B. (2026). Hazelnut (Corylus avellana L.) Mass Prediction Using Machine Learning Models Based on Physical Attributes. Tarım Bilimleri Dergisi, Volume: 32 Issue: 3. https://doi.org/10.15832/ankutbd.1716318

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