This paper presents whole-body vibration prediction in an agricultural tractor based on selected operational parameters using machine learning. Experiments were performed using a Landini Powerfarm 100 model tractor on farmlands and service roads located at the Osijek School of Agriculture and Veterinary Medicine. The methodology adhered to the HRN ISO 5008 protocols for establishing test surfaces, including a smooth 100 m track and a rugged 35 m track. Whole-body vibrational exposure assessments were carried out in alignment with the HRN ISO 2631-1 and HRN ISO 2631-4 guidelines, which outline procedures for evaluating mechanical oscillations in occupational settings. The obtained whole-body vibration data were divided into three datasets (one for each axis) and processed using linear regression as a baseline and compared against three machine learning models (gradient boosting regressor; support vector machine regressor; multi-layer perception). The most accurate machine learning model according to the R² metric was the gradient boosting regressor for the x-axis (R²: 0.98) and the y-axis (R²: 0.98), and for the z-axis (R²: 0.95), the most accurate machine learning model was the SVM regressor. The application of machine learning methods indicates that machine learning models can be used to predict whole-body vibrations more accurately than linear regression.

This paper presents a study regarding fuel consumption, distance traveled and aggregate operation time during agrotechnical harrowing operation with and without a navigation system in the company. The measurement was carried out on an area of 32.41 ha, which was divided into two equal parts (16.205 ha). The researched area was divided into two equal parts using SMS Advance Software. The tractor-machine aggregate consisted of a Claas Axion 830 tractor as the driving part and a harrow as the working part of it. Fuel consumption was monitored via the tractor's display and the volumetric method. A significant difference in fuel consumption was determined for the volumetric method with navigation (9.97%) and without navigation (6.45%). Higher fuel costs were determined when the unit was operated without a navigation system.

Modern agricultural production requires the use of agricultural machinery. Among all agricultural machines, the tractor is one of the most frequently used agricultural machines. In this paper, we perform a literature review and explore various safety hazards a tractor operator is exposed to. Next, we present the results of a work safety related survey we conducted in Osijek-Baranja and Vukovar-Srijem counties. We conclude the paper with a list of safety related observations we derived from our research.