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.

The efficient pre-incubation selection of duck eggs is essential to ensuring stable hatchability, but most existing optoelectronic and machine vision systems have been calibrated for chicken eggs and cannot be directly used for duck eggs because of their larger size, stronger reflectivity and wider morphological variability. This study proposes an optoelectronic method specifically adapted to Adigel duck eggs that combines load cell weighing, infrared distance sensing and dual-projection image processing in a single stationary setup. A total of 300 eggs were measured manually and automatically, and the results were statistically compared. The developed algorithm uses adaptive Gaussian thresholding (51 × 51, C = 2) and a median 5 × 5 filter to stabilize contour extraction on glossy and spotted shells, followed by ellipsoid-based volume estimation with a breed-specific correction factor (K_v = 0.637). The automatic system showed high agreement with manual measurements (r > 0.95 for mass and linear dimensions) and a mean relative error below 2%. Density, the shape index (If) and the shape coefficient (K₁) were computed to classify eggs into “suitable”, “borderline” and “unsuitable” categories. A preliminary incubation trial (n = 60) of eggs classified as “suitable” resulted in 92% hatchability, thus confirming the predictive value of the proposed criteria. Unlike chicken-oriented systems, the presented solution provides breed-specific calibration and can be implemented in small and medium hatcheries for the reproducible, non-destructive sorting of hatching duck eggs.

Sorting apples by product category is a key stage of harvest preparation, especially for small and medium-sized farms, where traditional manual sorting leads to high labor intensity. This article presents the development and experimental study of a compact apple-sorting machine based on computer vision, color assessment, and indirect fruit-weight estimation. The designed machine was adapted for Kazakhstani apple varieties and integrates low-cost parts: a fruit feeding and positioning module, a computer vision system with an image processing unit, PLC-based control, and a sorting actuator. The decision-rule procedure uses color and visual geometric parameters of images, and classification by regression analysis. Statistical analysis identified projected fruit area as the primary predictor for indirect weight estimation using the proposed regression model. The model robustness and classification accuracy were assessed by confusion matrices and main validation metrics. The influence of conveyor speed on the stability of sorting was observed. The experimental results indicated that the optimal operating mode for the machine is an apple transport speed of 0.16 m/s, where sorting throughput is approximately 400 kg/hour, with an average accuracy of 92%. The proposed machine provides sufficient real-time sorting accuracy with a simple, cost-effective machine design and can be used as a useful sorting solution in small and medium-sized farms.

This article presents the development and experimental study of a compact, automated apple sorting machine based on computer vision, indirect fruit weight estimation, and color grading. It is designed for use in small and medium-sized farms, where the use of industrial lines is limited by high cost and complex maintenance. The proposed machine enables non-destructive assessing and sorting of apples in a continuous flow mode without the use of mechanical weighing, includes a fruit feeding and positioning module, a computer vision system, an image processing unit, and a sorting actuator synchronized with the conveyor movement. Fruit weight and color assessment is based on visual geometric parameters (diameter, fruit height, projected area, and the proportion of surface color), extracted from digital images, followed by classification by product category using regression models. As part of the experimental study, a correlation analysis was conducted between the actual weight of apples and their geometric parameters for five varieties typical of Kazakhstan. It was shown that the projected fruit area exhibits the most stable correlation with weight, justifying its use as the primary predictor in constructing a regression model for indirect weight estimation. An assessment of the accuracy of apple classification by product categories was carried out and the influence of conveyor speed on the stability and correctness of sorting was observed. The experimental results with a total 1250 apples of five varieties - Aport Alexander, Sinap Almaty, Kazakhski Yubileinyi, Ainur, and Nursat indicated that the optimal operating mode for the machine is an apple transport speed of 0.16 m/s. In this mode, sorting throughput is approximately 400 kg/hour, with an average accuracy of 92% for the automatic classification in accordance with GOST requirements. These results confirm that the proposed approach provides sufficient real-time sorting accuracy with a simple machine design. The machine can be used as a standalone sorting solution, as well as a base platform for further expansion of functionality by integrating surface defect assessment and grade identification modules.

This article presents methods and hardware for the multi-criteria non-destructive determination of chicken egg quality parameters, implemented using a multifunctional rotary system. Unlike traditional single-criteria sorting, which relies primarily on weight, the proposed approach utilizes a combination of physical and geometric parameters, including weight, linear dimensions, cross-sectional area and perimeter, volume, density, and shape. The experimental framework for the study was formed by measuring the parameters of 750 chicken eggs, covering the entire range of product categories and morphological variations. Geometric parameters were determined using machine vision methods, weight was determined using a strain gauge, and derived parameters were calculated using formalized models. A multi-criteria evaluation algorithm based on fuzzy set theory was used to make the classification decision, accounting for overlapping feature ranges and regulatory differences between EU and EAEU standards. The results of statistical and correlation analysis showed that egg density is identified as a relatively independent diagnostic parameter, weakly correlated with weight and geometric characteristics, justifying its inclusion in the quality model. A comparison of manual and automatic classification revealed differences in boundary categories during single-criteria sorting and indicated the potential of a multi-criteria approach. The obtained results support the feasibility of the developed methods and hardware under the conditions of the present study.

This article presents methods and hardware for multi-criteria non-destructive determination of chicken egg quality parameters, implemented using a multifunctional rotary system. Unlike traditional single-criteria sorting, which relies primarily on weight, the proposed approach utilizes a combination of physical and geometric parameters, including weight, linear dimensions, cross-sectional area and perimeter, volume, density, and shape. The experimental framework for the study was formed by measuring the parameters of 750 chicken eggs, covering the entire range of product categories and morphological variations. Geometric parameters were determined using machine vision methods, weight was determined using a strain gauge, and derived parameters were calculated using formalized models. A multi-criteria evaluation algorithm based on fuzzy set theory was used to make the classification decision, accounting for overlapping feature ranges and regulatory differences between EU and EAEU standards. The results of statistical and correlation analysis showed that egg density is an independent diagnostic parameter, weakly correlated with weight and geometric characteristics, justifying its inclusion in the quality model. A comparison of manual and automatic classification revealed systematic biases in boundary categories during single-criteria sorting and demonstrated the advantages of a multi-criteria approach. The obtained results confirm the effectiveness of the developed methods and hardware for automated egg quality control in industrial poultry farming.