The objective of this study was to develop a mathematical model for predicting the volumetric efficiency for fluted rolls metering different crop seeds. A special test stand was designed and manufactured in order to conduct experiments in order to find out the volumetric efficiency of the fluted rolls. In order to meet the above objective, alfalfa, barley, coriander, flax, oat, rye, safflower, sesame and wheat seeds were used. Experiments were conducted at different roll revolutions and roll lengths by considering the seed rate for each crop and the rolls were driven by a step motor as controlled by a software installed on a laptop computer. Five replications were achieved for each experiment and a total of 1660 flow rate data was obtained. Five different models for volumetric efficiency were developed. Analysis based on different goodness of fit criteria were achieved to compare models in to order to select the appropriate one. The study conducted not only resulted in developing volumetric efficiency models but also revealed an important finding based on low flow evenness (low CV, %) values obtained as compared to other studies in the literature.

The objective of this research endeavor is to present engineering data pertaining to the size and shape characteristics of strawberries, which have a wide range of applications in industry, and to obtain the data necessary for the development and design of product processing systems. In this study, standard strawberry varieties were utilized, and analyses were conducted by means of an image-processing method. The projection area (601.5–762.0 mm²), length (34.0 mm), width (28.6 mm) and surface area (28.6 cm²) of the strawberry samples were measured in the horizontal and vertical orientation, in order to ascertain their size characteristics. Furthermore, the sphericity (86.1%) and roundness (1.039–1.087) parameters were calculated for the shape characteristics, accordingly. The findings of the correlation analysis suggested that the size parameters of the fruits exerted no influence on fruit shape characteristics. In the elliptic Fourier analysis performed to reveal the shape differences in the fruit, the contour geometry of each fruit sample was extracted, the principal component (PC) scores describing the shape were obtained and the shape categories of the fruit were determined. Following the analysis of the PCs, it was determined that 90.77% of the total shape variance was explained by the first seven components. Consequently, the shape of the strawberry fruit was defined as a spherical cone. Following the implementation of a discriminant analysis in conjunction with a clustering process, which categorized the samples into seven distinct shape categories employing the k-means algorithm, an accuracy rate of 94.1% was achieved.

Although research on field-type fertilizer spreaders is prevalent, investigations into small-scale equipment are almost absent. In this study, the performance of the distribution of pelletized organic fertilizers was determined with a ground-driven fertilizer spreader. The experiments were conducted in a laboratory environment devoid of wind influence. Organic fertilizer, characterised by a density of 679 kg m-3, was utilised in the form of cylindrical pellets with an average diameter of 3.7 mm and a length of 5.61 mm. A collection area measuring 500 mm in width and 9 m in length was established with 32 collection trays, each sized 250×500 mm, arranged perpendicular to the machine's movement direction. The research investigations were conducted in triplicate at varying machine forward speeds (2, 4, and 6 km h-1) and different orifice areas (1200, 1600, and 2000 mm²). Coefficients of variation (CV%) employed to assess the homogeneity of transverse fertilizer distribution were computed for each replication. As a result, the lowest CV% value was calculated as 9.42% at 2.5 m working width, 1200 mm2 orifice area and 4 km h-1 forward speed. In 1600 and 2000 mm2 orifice areas, the lowest CV% values were obtained as 17.08% and 16.8%, respectively, at a forward speed of 4 km h-1 and a working width of 3 m. The CV% results derived from this study were assessed according to the acceptable upper limit of 20%.

Tillage machines play a critical role in agricultural production by improving soil structure through aeration, disintegration, and mixing, while typically operating under high energy demand. Among the available implements, vertical rotary tillers are widely preferred for seedbed preparation owing to their intensive soil–tool interaction and favorable energy efficiency compared with conventional tillage equipment. However, the torque, draft force, and power consumption of vertical rotary tillers are strongly influenced by operational parameters such as rotor speed and blade geometry, and accurately predicting these parameters remains a key challenge for optimizing machine design and energy use. This study investigates the effects of rotor speed (220, 270, and 310 rpm), horizontal blade angles (0°, 5°, 10°, and 15°), and vertical blade angles (0°, 10°, 20°, 30°, and 40°) on torque, draft force, and power consumption of a vertical rotary tiller under controlled laboratory soil bin conditions. Torque and draft force were measured using a custom-designed torque measurement system and a load cell, respectively, while soil penetration resistance and moisture content were maintained at 0.2–0.5 MPa and 18% ± 1%. The experimental dataset, obtained from a full factorial design, was analyzed using six trained machine learning models: Gradient Boosting (GB), Light Gradient Boosting Machine (LightGBM), Decision Tree (DT), K-Nearest Neighbors (KNN), Artificial Neural Networks (ANN), and Support Vector Machine (SVM). Model performance was evaluated using R², MAPE, MAE, and RMSE metrics, supported by 5-fold cross-validation to assess generalization capability. The results showed that Gradient Boosting achieved the highest predictive accuracy across all output parameters (R² up to 0.999), while Decision Tree and LightGBM models also produced consistently high accuracy with low error metrics, whereas ANN and SVM exhibited comparatively higher prediction errors, particularly for draft force and power consumption. These results confirm the applicability and reliability of machine learning models, particularly GB and DT, for predicting key performance parameters in vertical rotary tillage under varying operational conditions.