This paper deals with the problem of predetermining the spray angle and uniformity of the flat fan sprayer with a semicircular impact surface for the intra-soil application of liquid mineral fertilizers. The jet impact on a round splash plate and radial atomization properties are investigated theoretically, the formation features of the spray with an obtuse angle are studied in a geometrical way, and the design search of the nozzle shape and optimization calculations are performed using computational fluid dynamics (CFD) simulations and then verified experimentally. It was revealed that the spray rate and spray angle can be adjusted by changing the parameter s, and when the spray angle is within s = 0–0.2 mm, it forms spray angles with range of 140°–175°. The spraying angle, in turn, shows the potential length of the tillage knife in accordance with the undersoil cavity dimensions. A spray uniformity of up to 74% was achieved, which is sufficient for applied studies and for intra-soil application operations. According to the investigations and field experiments, it can be concluded that the designed nozzle is applicable for the intra-soil application of liquid mineral fertilizers. The use of flat fan nozzles that form a spraying band under the soil cavity and along the entire length of the tillage knife ensures a highly efficient mixing process, the liquid mineral fertilizers with treated soil (particles) positively contributing to plant maturation.

This paper presents the validation of a digital image analysis method for the quantitative determination of physical quality parameters of apples by comparative analysis with a traditional measurement method. The method was used to determine the quality indicators of apples based on digital image analysis in accordance with standard requirements. Five popular apple varieties from Kazakhstan were selected for the study: Aport Alexander, Ainur, Sinap Almaty, Nursat and Kazakhskij Yubilejnyj. The parameters of the five apple varieties were measured both manually and digitally, which revealed close agreement between the obtained values. The analysis of the results of measuring the geometric parameters of the apples and the percentage of red color in the images was carried out. The maximum relative errors in determining the diameters (d, D) and height (h) were 2.99%, 3.03% and 4.12%, respectively. Regression models were developed to predict the weight and volume of apples. The best results in weight prediction were obtained for the Sinap Almatinsky variety using stepwise linear regression (R² = 0.96), and volume prediction showed the best results for the Nursat variety (R² = 0.92). This study lays the foundation for the development of automated systems for sorting apples by commercial varieties.

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.

Kazakhstan’s growing potato industry underscores the need to develop and apply digital solutions that boost grading efficiency. A comparison between two traditional deep neural network architectures used to classify color images of potatoes from Kazakhstan is discussed in the paper. Ten representative varieties of Kazakhstani potatoes were selected as objects of study: Alians, Alians mini, Astana, Astana mini, Edem, Edem mini, Nerli, Nerli mini, Zhanaisan, and Zhanaisan mini. Two convolutional neural network (CNN) models, SqueezeNet and GoogLeNet, were refined via transfer learning employing three optimization approaches. Then, they were used to classify the potato images. A comparison of the two neural networks’ classification performance was conducted using common evaluation criteria—accuracy, precision, F1 score, and recall—alongside a confusion matrix to highlight misclassified samples. The comparative analysis demonstrated that both CNN architectures—SqueezeNet and GoogLeNet—achieve high classification accuracy for Kazakhstani potato varieties, with the best performance on Astana and Zhanaisan (>97%). The study confirms the applicability of lightweight CNNs for digital varietal identification and automated quality assessment of seed potatoes under controlled imaging conditions. The developed approach is the first comparative CNN-based varietal identification of Kazakhstani potato tubers using transfer learning and contributes to the digitalization of potato breeding, and provides a baseline for future real-time sorting systems using deep learning.

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.