Treffer: Deep Learning-Based Identification of Kazakhstan Apple Varieties Using Pre-Trained CNN Models.

This paper presents a digital approach for the identification of apple varieties bred in Kazakhstan using deep learning methods and transfer learning. The main objective of this study is to develop and evaluate an algorithm for automatic varietal classification of apples based on color images obtained under controlled conditions. Five representative cultivars were selected as research objects: Aport Alexander, Ainur, Sinap Almaty, Nursat, and Kazakhskij Yubilejnyj. The fruit samples were collected in the pomological garden of the Kazakh Research Institute of Fruit and Vegetable Growing, ensuring representativeness and taking into account the natural variability of the cultivars. Two convolutional neural network (CNN) architectures—GoogLeNet and SqueezeNet—were fine-tuned using transfer learning with different optimization settings. The data processing pipeline included preprocessing, training and validation set formation, and augmentation techniques to improve model generalization. Network performance was assessed using standard evaluation metrics such as accuracy, precision, and recall, complemented by confusion matrix analysis to reveal potential misclassifications. The results demonstrated high recognition efficiency: the classification accuracy exceeded 95% for most cultivars, while the Ainur variety achieved 100% recognition when tested with GoogLeNet. Interestingly, the Nursat variety achieved the best results with SqueezeNet, which highlights the importance of model selection for specific apple types. These findings confirm the applicability of CNN-based deep learning for varietal recognition of Kazakhstan apple cultivars. The novelty of this study lies in applying neural network models to local Kazakhstan apple varieties for the first time, which is of both scientific and practical importance. The practical contribution of the research is the potential integration of the developed method into industrial fruit-sorting systems, thereby increasing productivity, objectivity, and precision in post-harvest processing. The main limitation of this study is the relatively small dataset and the use of controlled laboratory image acquisition conditions. Future research will focus on expanding the dataset, testing the models under real production environments, and exploring more advanced deep learning architectures to further improve recognition performance. [ABSTRACT FROM AUTHOR]

Copyright of AgriEngineering is the property of MDPI and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)