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ASPHC Baner

Vol. 25 No. 5 (2026):

Research paper

Comparative assessment of raspberry cultivars from organic and conventional cultivation systems using image analysis and traditional machine learning algorithms

DOI: https://doi.org/10.24326/asphc.2026.5732
Submitted: 29 June 2026
Published: 30.10.2026

Abstract

The objective of this study was to evaluate the usefulness of image analysis and traditional machine learning algorithms for the discrimination of raspberry cultivars originating from organic and conventional cultivation systems. Images of the whole fruit of four raspberry cultivars: Polana, Polesie, Poemat, and Poranek, were acquired using a digital camera. Texture descriptors were extracted from images. The mean values of selected image features were compared, and the artificial intelligence models were developed. The mean comparison revealed significant differences in image texture descriptors among raspberry cultivars within both cultivation systems and between organic and conventional samples of the same cultivar. For cultivar discrimination of Polana, Polesie, Poemat, and Poranek in organically grown raspberries, the highest accuracy of 89.2% was achieved using the Subspace Discriminant classifier, followed by Quadratic Support Vector Machine (88.1%) and Wide Neural Network (86.0%). In conventionally cultivated raspberries, the highest accuracy of distinguishing cultivars reached 91.7% for the Linear Discriminant model, followed by Subspace Discriminant (91.3%) and Medium Neural Network (88.9%). All models were characterized by high area under the Receiver Operating Characteristic (ROC) curve (AUC) values, indicating excellent discrimination performance. Complete separation of organic and conventional raspberries for all analyzed cultivars with an accuracy of 100% was obtained. The results demonstrate that image texture analysis combined with traditional machine learning algorithms provides a rapid, objective, and non-destructive approach for raspberry cultivar identification and cultivation-system authentication. The proposed methodology shows considerable potential for applications in quality control, traceability, and verification of production practices in the raspberry industry.

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