Filip Vasić

Dr. Filip Vasić


Research Focus & Metrics

Academic Career Length

5Years (2022-Present)

(Calculated once Scilit publication data is synced: current year − year of first publication + 1)

Representative Publications

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11 March 2022 in Applied Sciences
Verified
Journal Article
Open access
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ASAlexander Sedlar
Alexander Sedlar
SGSonja Gvozdenac
Sonja Gvozdenac
MPMomčilo Pejović
Momčilo Pejović
Vladimir VišackiVladimir Višacki
Vladimir Višacki
Jan Jan TuranJan Jan Turan
Jan Jan Turan
FVFilip Vasić
Filip Vasić
Protective fungicides are sensitive to environmental conditions such as rainfall and solar radiation. Therefore, it is important to prolong the biological activity and fungicide resistance to the above-mentioned factors that can be achieved by adding a wetting agent to the working solution. Additionally, the quality and efficiency of preventive contact fungicides significantly depend on the application technique. Thus, it is important to make the right choice of the nozzles and adjust the working parameters of the treatment. The aim of this work is to determine the influence of a wetting agent and type of nozzle on copper hydroxide (2 L ha−2) deposits on sugar beet leaves. Experiments are set up under laboratory and field conditions. A pinole-based wetting agent is applied at three rates (0.3, 0.6, and 1.0 L ha−1) and two types of nozzles are used (standard with flat jet and modern turbo-drop twin-jet). A brilliant blue tracer is added to a working solution to enable the measurement of copper hydroxide deposits. The deposit amount is recorded before and after the rain simulation (15 L m−2) with a spectrophotometer light beam. In order to ensure the timeliness of the application of fungicides, remote sensing of vegetative indices is used as an indicator of disease occurrence. The results indicated an increase in copper hydroxide deposits with the increase in wetting agent rates for both types of nozzles and in both laboratory and field experiments. Moreover, when applying the copper hydroxide mixtures with modern turbo drop nozzles, the increase in copper hydroxide deposit is significant, compared to the standard nozzles.

18 March 2025 in Preprints.org
Verified
Preprint
Open access
PDF file
Vladimir VišackiVladimir Višacki
Vladimir Višacki
ASAleksandar Dusan Sedlar
Aleksandar Dusan Sedlar
LTLazar Turšijan
Lazar Turšijan
FVFilip Vasić
Filip Vasić
PBPatrik Burg
Patrik Burg
Lazar PavlovićLazar Pavlović
Lazar Pavlović
Selecting appropriate spraying parameters is a prerequisite for profitable agricultural crop production. These parameters include spraying speed, application rate, working pressure and characteristics of the equipment used, such as nozzles, pumps, filters and tanks. The listed parameters are essential for optimizing the efficacy and cost-effectiveness of pesticide application and minimizing environmental impact. In addition, weather conditions, terrain, crop type and specific microclimatic and micro-relief conditions impose restrictions on these parameters. Pesticide deposition on the target area is influenced by multiple parameters, including adjusted working pressure, speed, nozzle altitude and boom air assistance. Drone sprayers generate strong air assistance at the nozzle jets, causing droplet drift. If altitude of the sprayer is lower, droplets spend less time in the air and reach the target area more quickly. In contrast, if the drone is flying at a higher altitude, droplets spend more time in the air and are more affected by drone fans. Drone engine blades create an effect similar to wind, potentially causing wind drift and leading to faster droplets evaporation. The deposition efficiency of a drone sprayer using three different nozzle types was assessed during wheat treatment. The tested nozzles included a flat fan nozzle, an air-injector single flat fan nozzle and an air-injector double flat fan nozzle. These nozzles were tested at three different altitudes with consistent speed and nozzle pressure. At the highest altitude, deposition was reduced by half due to various types of drift. Small droplets evaporated while flying through the air, further pushed by the wind generated by the drone engines blades. It resulted in reduced total deposition (approximately 12%), as only larger droplets reached the target. Also, an unsatisfactory vortex was recorded. Since air-injector nozzles produce larger droplets, all parameters improved, achieving optimal efficacy, with the total deposition reaching up to 80%. Maximum deposition was achieved at a flight altitude of 1.5 m. The average deposition was approximately 34% for a spray width of 6 m. The collected data were sorted as nested data and processed to develop a model describing changes in deposition at different drone spray altitudes. This model can be a valuable tool for farmers and agricultural engineers in selecting appropriate nozzles for drone spraying. The choice of spraying altitude influences pesticide deposition, affecting both application efficacy and potential impact on the environment. Furthermore, during drone-based pesticide application, it is necessary to use only pesticides registered for aerial application. It is also obligatory to obtain permission from the Ministry of Agriculture, and notify the local community about the date and time of spraying. In this study, pesticide application was simulated using Brilliant Blue tracers. Drones equipped with multispectral cameras were used in this study for monitoring crop health and detecting disease outbreaks. Multispectral imaging was conducted to analyze vegetation indices such as NDVI and NDRE, in combination with drift analysis, to determine the optimal timing for pesticide application. High-resolution multispectral images of 1×1 m wheat plots were captured at a 15 m flight altitude, ensuring a spatial resolution of less than 1 cm per pixel. This approach allowed for precise detection of crop stress while simultaneously evaluating pesticide drift and deposition efficiency. By integrating remote sensing for stress detection and drift analysis for application efficiency, this study provides a data-driven approach for optimizing pesticide use, improving precision and minimizing environmental impact.