Piotr Markowski

Realistic digital representation of agricultural products is a prerequisite for accurate numerical simulations used in modern agri-food engineering, including Discrete Element Method (DEM), Computational Fluid Dynamics (CFD), and Finite Element Method (FEM) applications. However, irregular biological structures are often represented by simplified geometries that may inadequately reproduce their geometric and inertial characteristics. This study investigates the suitability of analytical shape approximations for representing onion bulbs (Allium cepa L.) using high-resolution three-dimensional (3D) scanning and mesh-based analysis. Sixty onion bulbs were digitized using,a NextEngine 2020i laser-based 3D scanner and watertight triangular meshes were reconstructed to obtain reference values of volume, surface area, shape descriptors, and principal moments of inertia. Mesh-derived parameters were compared with analytical models based on a sphere, a rotational spheroid, and a triaxial ellipsoid defined from externally measurable dimensions. The results demonstrated substantial variability in bulb geometry while maintaining high average sphericity,(0.956 ± 0.034). The triaxial ellipsoid provided the most accurate representation of geometric properties, particularly surface area, whereas the spheroidal model achieved comparable accuracy for volume estimation. The average occupancy coefficient of the bounding ellipsoidal volume was 0.76 ± 0.07, indicating systematic overestimation associated with dimension-based analytical representations. Inertia tensor analysis revealed that geometric anisotropy significantly affects rotational characteristics and model accuracy. The proposed methodology provides quantitative information for selecting appropriate particle representations in DEM simulations and for improving geometric fidelity in CFD and FEM models involving agricultural products.

Improving spray deposition within crop canopies is important for sustainable pesticide application, particularly when target surfaces are shielded or located in difficult-to-reach canopy zones. Air-assisted spraying may improve droplet transport and canopy penetration, but its effect is likely to depend on crop structure and target-zone position. This study evaluated the usefulness of air-assisted spraying in pea and parsley using a prototype sprayer operated with and without airflow support. Spray deposition was assessed with water-sensitive papers positioned at crop-relevant heights in horizontal and vertical orientations. In pea, air assistance significantly increased deposition on vertically exposed surfaces; for example, on the approach side at 50 cm above ground, mean coverage increased from 0.0622 to 0.4491% (+621.9%). Its effect on upper horizontal surfaces was limited. In parsley, the response was more selective: deposition increased on vertical surfaces at 20 cm, but decreased on horizontal ones, including a reduction at ground level from 0.0928 to 0.0393% (−57.7%). These results indicate that the performance of air-assisted spraying depends on crop architecture, target-zone accessibility, and surface orientation. Within the limits of this two-crop study, air assistance appears most justified where improved canopy penetration or reduced ground losses are particularly relevant. The observed deposition differences should not be interpreted as direct evidence of biological efficacy.

This study presents a hybrid diagnostic approach combining the Continuous Wavelet Transform (CWT) and Convolutional Neural Networks (CNN) for assessing screw wear in a single-screw extruder operating under controlled conditions. Electrical current signals from the drive motor were analyzed to identify changes associated with the degradation of working components. CWT scalograms were used as time–frequency inputs for a CNN classifier, achieving a classification accuracy of 92.3% in distinguishing between new and worn screw states. Principal Component Analysis (PCA) confirmed clear separability of operating conditions, with the first two components explaining over 99% of the total variance. The results indicate that electrical signals contain diagnostically relevant information and that their combined analysis using CWT and CNN enables automated, non-invasive condition assessment with potential applicability in predictive maintenance systems without additional sensors.

Efficient pesticide application remains a critical resource-management challenge in modern agriculture, where limited spray penetration reduces treatment efficacy, wastes chemical inputs, and increases environmental losses. This study quantified the effect of air-assisted spraying (AAS) on droplet deposition in two contrasting field crops, oilseed rape and wheat. Field trials were conducted using a sprayer equipped with an adjustable airflow module, and spray coverage was measured with water-sensitive papers at multiple canopy heights and orientations. In oilseed rape, AAS improved deposition on front-facing and top surfaces in the lower canopy, for example, increasing top-surface coverage at 90 cm from 53.4% to 65.5% at 6 km∙h⁻¹, indicating more uniform distribution and enhanced penetration. In wheat, which typically exhibits a more open canopy structure compared to oilseed rape, AAS effects were smaller and less consistent, with the greatest gain on front-facing lower surfaces (from 13.3% to 21.9% at 7 km∙h⁻¹). Although drift was not measured in this experiment, previous studies using the same sprayer prototype demonstrated measurable reductions, supporting the environmental relevance of improved deposition. These results highlight the role of canopy architecture in determining AAS performance and underscore the technology’s potential to reduce pesticide inputs, minimize off-target losses, and improve the resource efficiency of crop protection in line with EU Farm to Fork objectives.

In recent years, increasing efforts have been directed towards reducing greenhouse gas emissions from agriculturally managed soils to mitigate their negative environmental impacts. The total emissions released are influenced by the chosen farming practices, including soil surface treatment methods. While numerous studies have focused on arable cropping systems, research in permanent crops, such as vineyards, remains limited. For this reason, our study aimed to assess the effects of different soil surface management strategies in vineyard inter-rows on CO₂ emissions. Five treatments were examined: cultivation to a depth of 70 mm (C70); cultivation to a depth of 150 mm (C150); compost application (50 t·ha⁻¹) incorporated into the soil at 150 mm depth (C+C150); mulching with plant residues left on the soil surface (M) and an untreated control (Co). Results from two-year measurements indicated the highest CO₂ emissions in the C+C150 treatment (42–76% higher) and C150 (34–53% higher) compared to the control (Co). The impact of soil surface treatment on CO₂ emissions is further substantiated by cumulative values recorded over 120 days, ranging from 11–24 g C-CO₂·m⁻²·h⁻¹, corresponding to 9.64–21.03 Mg C-CO₂·ha⁻¹·y⁻¹.

Reducing pesticide drift is essential for minimizing environmental pollution and so implementing sustainable agricultural practices. Various methods can be employed to counteract this phenomenon, one of which is air-assisted spraying. This study evaluates the impact of air assistance on spray deposition using a field sprayer designed for cereal crops. Spray quality was assessed using water-sensitive paper, allowing drift levels to be estimated not only by assessing coverage but also by measuring the average number of droplets per square centimeter on the paper surface. This method provided a more reliable evaluation of drift reduction. The results indicate that air assistance reduced drift by 40.74% in coverage and 37.55% in droplet density per square centimeter. Notably, the greatest drift reduction occurred at the point of highest coverage—1 m from the spraying area—with an average reduction of 50%. Furthermore, at this distance, an 80% drift reduction was achieved at the recommended sprayer speed of 6 km/h. These findings highlight the potential of air-assisted spraying to enhance efficiency while reducing environmental impact. This research aligns with the objectives of the European Green Deal, supporting the transition toward more sustainable pesticide application techniques and contributing to environmentally responsible crop protection practices.

The aim of this study was to determine the influence of boom height above a crop stand and the spacing between nozzles and diffusers in an over-row sprayer on the uniformity of the horizontal spray distribution and the uniformity of the air velocity distribution. The experimental setup involved a prototype over-row sprayer equipped with a boom with a working width of 8 m and ten air diffusers with spray nozzles. Air diffusers were connected to one or two nozzles each, and they were installed on the boom at intervals of 60, 80, and 90 cm. Terminal airflow velocity at a canopy is determined by the height of a sprayer boom and the diffuser spacing, ranging from around 2 m s^–1 to around 27 m s^–1. The sprayer boom should be positioned at a height of 50 cm above a crop stand due to the difference between the minimum and maximum airflow velocities. The horizontal spray distribution was more uniform when the sprayer was equipped with hollow-cone nozzles instead of flat-fan nozzles; hollow-cone nozzles should be applied if the distance between nozzles needs to be adjusted to the row width and row spacing. The analyzed coefficients did not exceed 10% when the boom was positioned 50 cm above the crop stand and when the nozzles were spaced 80 cm apart, which suggests that, in this configuration, sprayers equipped with hollow-cone nozzles can also be applied to close-grown crops.

Designing machines and equipment for post-harvest operations of agricultural products requires information about their physical properties. The aim of the work was to evaluate the possibility of introducing a new approach to predict the moisture content in bean and corn seeds based on measuring their dimensions using image analysis using artificial neural networks (ANN). Experimental tests were carried out at three levels of wet basis moisture content of seeds: 9, 13 and 17%. The analysis of the results showed a direct relationship between the wet basis moisture content and the main dimensions of the seeds. Based on the statistical analysis of the seed material, it was shown that the characteristics examined have a normal or close to normal distribution, and the seed material used in the investigation is representative. Furthermore, the use of artificial neural networks to predict the wet basis moisture content of seeds based on changes in their dimensions has an efficiency of 82%. The results obtained from the method used in this work are very promising for predicting the moisture content.

The study evaluated the seeding quality of radish seeds cv. Saxa Polana (percentage of single seeds, double seeds and missed seeds (skips) sown with a Max Pneumatic S 156 seed drill fitted with a pneumatic seed dosing unit set at different forward speeds of a metering unit and different sowing...