Field studies were conducted in the northeastern part of the Republic of Croatia to determine the influence of the critical period of weed interference on sunflower (Helianthus annuus) yield, yield components, and oil content. For this purpose, different durations of competition were established, allowing weeds to infest the crop for increasing periods of time after planting or maintaining plots weed-free for increasing periods of time after planting. The beginning and the end of the critical period of weed control (CPWC), based on a 5% and 10% loss of sunflower yield, were determined by fitting the four-parameter log-logistic equations to the relative seed yield. The total weed biomass increased progressively in relation to the increase in the competition. The beginning of the CPWC period, based on a 5% acceptable yield loss, ranged from 141 to 234 growing degree days (GDD), which corresponded to the two-to-four true leaf development stage (the V2–V4 growth stages) across both sites and years. The crop had to be kept weed-free until a period when sunflower inflorescence began to open and flower (the R4–R5 growth stage) or from 1365 to 1932 GDD. The sunflower yield and yield components varied between the years and among locations. An increasing duration of weed interference negatively affected crop height, head diameter, and 1000-kernel weight, but not seed oil content.

The paper depicts agricultural robots that can perform complex tasks. Fast development and application of agricultural robotics is a result of increased development of agricultural machinery. Robots are complex and intelligent systems with a significant role in agriculture that are becoming an integral part of both the technological and scientific progress. The paper presents some important roles of robots and robotic systems in various agricultural areas and explains the deployment of new technologies supported by the examples of their application in arable farming, horticulture, and forestry. Robotics application decreases the deployment of human resources, enables significant production cost savings, and increases production capacity. The application of robotic systems facilitates high precision levels and repetition speed regarding time and space, which cannot be replicated by farmers

This systematic review focuses on intelligent navigation as a core enabler of autonomy in smart warehouses, where mobile robots must dynamically perceive, reason, and act in complex, human-shared environments. By synthesizing advancements in AI-driven decision-making, SLAM, and multi-sensor fusion, the study highlights how intelligent navigation architectures reduce operational uncertainty and enhance task efficiency in logistics automation. Smart warehouses, powered by mobile robots and AGVs and integrated with AI and algorithms, are enabling more efficient storage with less human labour. This systematic review followed PRISMA 2020 guidelines to systematically identify, screen, and synthesize evidence from 106 peer-reviewed scientific articles (including pri-mary studies, technical papers, and reviews) published between 2020–2025, sourced from Web of Science. Thematic synthesis was conducted across 8 domains: AI, SLAM, sensor fusion, safety, network, path planning, implementation, and design. The transition to smart warehouses requires modern technologies to automate tasks and optimize resources. This article examines how intelligent systems can be integrated with mathematical models to improve navigation accuracy, reduce costs and prioritize human safety. Real-time data management with precise information for AMRs and AGVs is crucial for low-risk operation. This article studies AI, the IoT, LiDAR, machine learning (ML), SLAM and other new technologies for the successful implementation of mobile robots in smart warehouses. Modern technologies such as reinforcement learning optimize the routes and tasks of mobile robots. Data and sensor fusion methods integrate information from various sources to provide a more precise understanding of the indoor environment and inventory. Semantic mapping enables mobile robots to navigate and interact with complex warehouse environments with high accuracy in real time. The article also analyses how virtual reality (VR) can improve the spatial orientation of mobile robots by developing sophisticated navigation solutions that reduce time and financial costs.

Intensive agricultural practices affect soil compaction, and their indirect and direct effects on crop growth and development are an increasingly important focus of scientific research. The objective of this study was to determine the influence of different tillage systems on soil compaction and to observe the influence of tractor tire pressure on penetrometer resistance during sowing. The three-year study was conducted on the heavy pseudogley soil of Brod-Posavina County in the Republic of Croatia. During the research, crops were observed in the following cropping sequence: soybean (Glycine max L.) in the first year, maize (Zea mays L.) in the second year and winter wheat (Triticum aestivum L.) in the third year. The tillage systems as the main study factor were conventional tillage (CT) plowing to a depth of 35 cm, disc tillage (DH) to a depth of 15 cm, loosening (CH) to a depth of 30 cm, and undermining (SS) to a depth of 50 cm. The following pressures were used as a subfactor of this study, namely the pressure of the front and rear tires of the tractor during sowing: p₁ (front 1.0 bar/rear 0.8 bar), p₂ (front 2.0 bar/rear 1.6 bar), and p₃ (front 3.0 bar/rear 2.4 bar). The tillage systems applied resulted in different soil compaction, thus the deepest tillage SS had the lowest resistance and the DH tillage had the highest resistance in all three experimental years. Penetrometer measurements showed the influence of tire pressure p₁ on reducing compaction as early as the first year in 2017, while in the last year of research in 2019, tractor tire pressure p₃ during sowing contributed to a significant increase in soil compaction.

In this paper, new types of autonomous systems used in agriculture were analysed. The paper shows new self-guiding systems such as AGVs with full autonomy in degrees operation. It explains internal transport and systems of autonomous vehicles in outdoor agriculture. New autonomous systems used outside such as appliance of special navigation systems and their purpose in agriculture are present in this work. Navigation systems with GPS signal and RTK technology, vehicle guidance camera and AI machine vision for manipulation are described. Light and laser technologies for fully autonomous robotic technologies such as LiDAR system in vehicle for detection of the presence of pests and diseases are presented in this paper. The paper emphasized advantages of using AGVs as result of their autonomy, clean power sources without harmful impact on the environment. Navigation in indoor spaces that uses LTE Direct protocol is explained, whereby the Wi-Fi ceiling antenna and wireless APP for horizontal movement of AGVs is shown. The ways of using UAVs for warehouse inventory through web applications with an advanced navigation system guided by AI are given in this work.

Airborne pollen monitoring in the city of Vukovar (Northeastern Croatia) was carried out using a Hirst-type spore trap, which was placed on the roof of a building at a height of 15 m above ground level. Over 5 consecutive years of airborne pollen monitoring, 76 pollen types from 45 plant families were identified. Of these, 29 pollen types belonged to arboreal species and 47 to non-arboreal species. Sixty-two percent of the total pollen was non-arboreal pollen. The main pollen types present in the airborne spectrum were Ambrosia artemisiifolia, Betula, Urtica, and Poaceae. Among the arboreal types, the genus Betula is the only taxon producing a high percentage in the pollen spectrum. Regarding allergenicity, A. artemisifolia, Betula, and Poaceae pose the highest allergy risk, while Urtica has a low risk of pollen allergy. Large differences in annual pollen sum and the seasonal dynamics of these dominant taxa during the study period were verified. The presence of the pollen in the air was influenced by weather conditions, showing in particular a positive correlation with the minimum air temperature for Betula and mean air temperature for Urtica, Poaceae, and A. artemisiifolia. However, the pollen concentration in the air was negatively correlated with precipitation for Betula, Urtica, and Poaceae and with humidity for A. artemisiifolia.