A Biotechnical System for Increasing the Effectiveness of the Pre-Sowing Pulsed Laser Irradiation of Seeds to Increase Sunflower Yield
Mamyrbayev O. Alimhan K. Oralbekova D. Nykyforova L.E. Pavlov S. Aitkazina A. Zhumazhan N.
December 2024Multidisciplinary Digital Publishing Institute (MDPI)
AgriEngineering
2024#6Issue 43952 - 3968 pp.
In this study, we investigated the use of the pre-sowing electrophysical stimulation of seeds, particularly focusing on optimizing technological regimes for enhancing seed quality. The aim of this study was to improve sunflower seed germination utilizing laser optical radiation. The methods explored involved the pre-sowing stimulation of oilseeds and analyzing the key mechanisms affecting germination. Through our experimental research, we sought to identify the most effective laser irradiation parameters, ensuring the maximum seed quality improvement with minimal energy use. Using seeds of the first reproduction, we employed artificial aging to simulate a reduced seed quality and determined optimal irradiation regimes. Standard methods were followed to assess seed quality before and after irradiation, with 6–7 days of further exposure. Seed germination was carried out under controlled light and temperature conditions using the “on paper” method with paper napkins. A full factorial experiment was performed and key parameters for laser irradiation were determined, confirming that the pre-sowing laser pulse treatment significantly improved seed quality. In this research, we developed a biotechnical system for processing seeds and propose a method to adjust irradiation parameters based on the initial seed quality. The system effectively enhanced germination and crop yield, offering a reliable solution for improving sunflower seed productivity through laser treatment.
biotechnology , laser radiation , pre-sowing treatment , sunflower
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Department of Artificial Intelligence, U. Joldasbekov Institute of Mechanics and Engineering, Almaty, 050010, Kazakhstan
Department of Automation and Robotic Systems, National University of Life and Environmental Sciences of Ukraine, Kyiv, 03041, Ukraine
Department of Biomedical Engineering and Optic-Electronic Systems, Vinnytsia National Technical University, Vinnytsia, 21021, Ukraine
Department of Artificial intelligence and Big Data, Al-Farabi Kazakh National University, Almaty, 050040, Kazakhstan
Department of Artificial Intelligence
Department of Automation and Robotic Systems
Department of Biomedical Engineering and Optic-Electronic Systems
Department of Artificial intelligence and Big Data
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