Cihat Gedik

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.

Wheat (Triticum aestivum L.) is a staple food for billions worldwide and plays a critical role in global food security. However, fungal pathogens cause yield losses and significant economic damage, posing a serious threat to wheat production. Therefore, early and accurate detection is strategically important to mitigate these losses. In this study, a dataset comprising 5,325 images of seven major wheat fungal diseases Blast, Brown Rust, Stripe Rust, Fusarium Head Blight, Loose Smut, Powdery Mildew, and Septoria along with healthy samples was used. The images were split into training (70%), validation (15%), and test (15%) subsets, and four pre-trained convolutional neural network (CNN) models, MobileNet, NASNetMobile, DenseNet121 and DenseNet169 were fine-tuned using transfer learning. Results showed that DenseNet169 achieved the highest classification accuracy (86.87%), followed by DenseNet121 (84.16%) and MobileNet (81.07%). NASNetMobile, however, demonstrated the lowest performance during validation. The findings highlight the strong potential of DenseNet169 in achieving high accuracy for wheat disease classification and its applicability in developing early detection systems that support sustainable agricultural production.

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.

This study was carried out to determine the biomass potential, energy value, bio-composting material and composting possibilities produced from plant and animal residues in Samsun. Samsun is a province of Türkiye in the Black Sea region. Samsun province has 17 districts. Residue amounts of agricultural products grown in Samsun were calculated using the Turkish Statistical Institute (TUIK) 2021 and year product production data, Türkiye Biomass Energy Potential Atlas (BEPA) 2021 biomass data. The total amount of agricultural waste is approximately 877.812 tons wheat 254.154 tons, paddy 132,891 tons, maize 53.861 tons and oat 47.797 tons in cereals in fruits, hazelnut is 66.363 tons and peach is 125.065 tons. Total heating value was found as 5.439.003 GJ. Hazelnut was the highest contributor to this value with 27% as fruit for cereals, it was maize with 17.14%. The energy equivalents of the biomass amounts are respectively; 37.34% hazelnut, 24% paddy, 10% wheat, 17.4% maize and 11.26% other plants were found.