The genus Rubus encompasses over 1000 species, including raspberries and blackberries, known for their rich nutritional and health-promoting properties. This review aims to provide a comprehensive overview of the nutritional values, health benefits, and potential medical and pharmaceutical applications of Rubus species. The fruits, roots, shoots, and leaves of these plants are distinguished by their high content of polyphenols, vitamins, and minerals, which contribute to their potent anti-oxidant, anti-inflammatory, anti-neurodegenerative, and anti-cancer effects. The diverse phytochemical profiles of the Rubus species support their use in the prevention and treatment of chronic diseases such as cardiovascular diseases, diabetes, and certain cancers. Additionally, the Rubus species are valuable as pharmaceutical raw materials due to their bioactive compounds. Despite the focus on a few widely cultivated species, numerous wild and lesser-known Rubus species offer significant untapped potential as functional foods, nutraceuticals, and pharmaceuticals. Future research should explore the detailed mechanisms of their bioactivities, develop effective extraction and formulation techniques, and integrate these findings into public health strategies. The genus Rubus represents a promising resource for enhancing human health and nutrition, as well as for pharmaceutical and medical applications, justifying increased cultivation and utilization of species from this genus.

High-quality nursery stock ensures the dynamic development of orchard crops. The most desirable by orchardists is a tree with a strongly developed crown, which makes it possible to shorten the investment period of newly planted orchards. The quality of the perianth and the degree of its branching depend on the growth strength of the rootstock, the ability of a given variety to form lateral shoots, the type and effectiveness of the treatments used to stimulate branching, soil and climatic conditions related to the location of the nursery and the course of the weather during the period of initiation and growth of young shoots. The study was conducted in 2017–2019 at a private nursery farm near Lublin. The aim of the experiment was to study the effect of mixtures based on the compounds benzyladenine (BA) and gibberellins (GA₃) and (GA₄₊₇) for chemical stimulation of lateral shoot outgrowth in apple cv. ‘Alwa’ and ‘Najdared’. In the course of the research, significant effects of the type of growth regulations used and the variety on the height of the perianths, the number and length of lateral shoots and the degree of branching were demonstrated.

The article examines the influence of six varieties of primocane raspberries, Rubus idaeus L., on the growth parameters and energy properties of the biomass. The unique aim of this study was to show the impact of primocane raspberry varieties, grown for a single harvest, on the growth parameters of the bushes and the energy and emission characteristics of the biomass obtained from the pruned canes. To the best of our knowledge, there is no such analysis connected to varieties of raspberries available in the literature. The following primocane raspberries were assessed: Polana, Polesie, Delniwa, Poemat, Polonez, and Poranek. Among the studied raspberry varieties, Polana was characterized by the highest number of side shoots and the greatest sum of the shoot lengths, while the Polonez variety was characterized by the highest average shoot length and shoot thickness. In the tested raspberry varieties, the weight of the shoots per hectare varied significantly, ranging from 6.06 t in the Poranek variety to 9.05 t in the Delniwa variety. It was shown that the raspberry variety had a significant impact on the higher heating value (HHV) and the lower heating value (LHV). The lowest energy value was found in the Delniwa (HHV—17.32 MJ·kg⁻¹; LHV—16.07 MJ·kg⁻¹) and Polana (HHV—17.33 MJ·kg⁻¹, LHV—16.19 MJ·kg⁻¹) varieties, and the significantly highest value was observed in the Poranek variety (HHV—17.63 MJ·kg⁻¹, LHV—16.39 MJ·kg⁻¹). The assessment of the total volume of exhaust gases showed the highest value of this parameter for the Polesie and Delniwa varieties (6.89 m³·kg⁻¹), with the lowest for the Polana variety (6.69 m³·kg⁻¹).

The aim of the study was to assess the impact of N Pro and Seactiv Complex technologies based on sea algae extracts on the yield and quality of Golden Delicious apple trees compared to the standard fertilization program. Research conducted in the years 2008–2017 showed that the tree fertilization technology and the year of research had a significant impact on the quality and yield parameters. The abundance of seaweed extracts had a positive effect on the greater number of fruits per tree (pcs.), their weight (t × ha⁻¹; kg tree⁻¹), the percentage of fruits exceeding 7.5 cm in size (%) and the average weight of fruits (g) compared to the control combination. In most of the analyzed study years, total yield exceeded the 90 t × threshold regardless of the fertilization program. The use of technology based on sea algae extracts during 10 years of research had a positive effect on the quality and weight compared to the standard fertilization technology commonly used in cultivation.

Nursery material intended for establishing intensive apple orchards should be characterised by a dominant and straight leader with an appropriate number of shoots that develop at the right height and are regularly spaced along the leader. The use of well-branched trees can lead to fruiting in the first year after planting. However, many apple varieties have difficulty forming lateral shoots due to strong apical dominance. The aim of the study was to assess the effectiveness of treatments stimulating the branching of maiden apple trees of the ‘Gloster’ variety. The research was carried out in 2017–2019 at a private nursery farm located in eastern Poland. The studied trees were subjected to a mechanical branching stimulation treatment, which consisted of pinching off 4–5 of the youngest leaves located below the growth cone, and chemical branching stimulation treatments, which consisted of applying growth regulator mixtures in the form of an aqueous solution, i.e., BA+GA₃ and BA+GA₄₊₇. The conducted studies showed that the type of branching-stimulating treatment had a significant effect on the height and trunk diameter of the maiden trees, the number of lateral shoots, the average length of one shoot and the sum of the lengths of all sylleptic shoots. The maiden trees treated with BA+GA₃ were characterised by the best quality among the analysed combinations. Maiden apple trees treated with BA+GA₃ were the tallest (2017—167.7 cm; 2018—175.3 cm; 2019—164.4 cm), produced the largest number of shoots (2017—6.5 pcs; 2018—6.8 pcs; 2019—6.3 pcs) and had the largest sum of lateral shoot lengths (2017—148.0 cm; 2018—155.4 cm; 2019—140.6 cm) among the evaluated combinations. The number of treatments and the concentration of applied growth regulators had a significant effect on the structure of the crown of the maiden apple trees of the ‘Gloster’ cultivar.

This study presents a comprehensive evaluation of recent YOLO architectures, YOLOv8s, YOLOv9s, YOLOv10s, YOLO11s, and YOLO12s, for the detection of red, yellow, and purple raspberry fruits under field conditions. Images were collected using an smartphone camera under varying illumination, weather, and occlusion conditions. Each model was trained and evaluated using standard object detection metrics (Precision, Recall, mAP₅₀, mAP_50:95, F₁-score), while inference performance was benchmarked on both high-performance (NVIDIA RTX 5080) and embedded (NVIDIA Jetson Orin NX) platforms. All models achieved high and consistent detection accuracy across fruits of different colors, confirming the robustness of the YOLO algorithm design. Compact variants provided the best trade-off between accuracy and computational cost, whereas deeper architectures yielded marginal improvements at higher Latency. TensorRT optimization on the Jetson device further enhanced real-time inference, particularly for embedded deployment. The results indicate that modern YOLO architectures have reached a level of architectural maturity, where advances are driven by optimization and specialization rather than structural redesign. These findings underline the strong potential of YOLO-based detectors as core components of intelligent, edge-deployable systems for precision agriculture and automated fruit detection.