2 December 25, 2025
Articles
1. Armen Kirakosyan, Zaven Khanamiryan, Patrik Yesayan, Mushegh Aslikyan, Ara Galstyan, Astghik Sukiasyan
Forecasting the Spatiotemporal Dynamics of Trace-Element Concentrations in Soil Based on Multi-Year Monitoring
Biogeosystem Technique. 2025. 12(2): 63-70.
Number of views: 7 Download in PDF
2. Vasiliy Yu. Rud’, Denis A. Egorov, Rafek R. Abdullin, Nataliya V. Krupenina, Vladimir E. Marley, Ivan V. Rud, Maxim V. Dyuldin, Zhenyue Yuan, Van Yuikun, Artem A. MarkaryanBiogeosystem Technique. 2025. 12(2): 63-70.
Abstract:
Mathematical forecasting methods were developed to evaluate the spatiotemporal dynamics of trace elements, including Fe, Zn, Cu, Mn, Cr, and V, in soils at the study sites. To detect trends and generate predictions, various models were employed, including linear and smoothing techniques. The trace-element composition in the studied soils shows moderate variability, mostly smooth and gradual, indicating the influence of long-term geochemical processes. Regional differences also emerged, highlighting the unequal impact of natural conditions and human activities on the trace-element background. These characteristics are crucial diagnostic tools for analyzing forecast results.
Mathematical forecasting methods were developed to evaluate the spatiotemporal dynamics of trace elements, including Fe, Zn, Cu, Mn, Cr, and V, in soils at the study sites. To detect trends and generate predictions, various models were employed, including linear and smoothing techniques. The trace-element composition in the studied soils shows moderate variability, mostly smooth and gradual, indicating the influence of long-term geochemical processes. Regional differences also emerged, highlighting the unequal impact of natural conditions and human activities on the trace-element background. These characteristics are crucial diagnostic tools for analyzing forecast results.
Number of views: 7 Download in PDF
Invasive Plant Monitoring in Hard-To-Reach Areas Using Swarms of Agricultural Drones
Biogeosystem Technique. 2025. 12(2): 71-78.
Number of views: 5 Download in PDF
3. Vasiliy Yu. Rud’, Denis A. Egorov, Nataliya V. Krupenina, Vladimir E. Marley, Ivan V. Rud, Eugeny O. Ol'khovik, Maxim V. Dyuldin, Rafek R. Abdullin, Zhenyue Yuan, Van YuikunBiogeosystem Technique. 2025. 12(2): 71-78.
Abstract:
The article is devoted to the issues of organization of monitoring and control of invasive plants growing in hard-to-reach places using a swarm of drones and a drone port. Sosnovsky's hogweed has spread widely in Russia and is actively seizing new areas, creating infestation steps that are difficult to control using traditional methods. Monitoring and elimination of such foci by traditional manual methods is time-consuming, ineffective and unsafe. A new technology that provides rapid monitoring of large areas and targeted chemical intervention only where necessary, reducing the risk of damage from invasions and the use of pesticides, is a technology based on the use of a drone swarm in conjunction with a drone port. It allows you to quickly explore large areas and get detailed images of growing vegetation from different angles. The resulting images can be recognized by means of artificial intelligence, analyzing the density of growth of invasive plants and their proximity to other crops. The data collected by agrodrones can be conditionally divided into digital and graphical. When receiving digital data from a swarm of drones, the information on the drone port is cleaned of noise and checked for consistency to ensure the reliability of the data, which improves the efficiency of system maintenance. For graphic data, first of all, color correction is used, restoring color details and increasing clarity, while restoring the natural image distorted at the time of digitization and subsequent processing. The key issue is the merging of the data collected by the agrodron swarm. Different specimens of agrodrons can receive different parameters and different images of the same habitat of invasive plants, and these data need to be linked to each other, eliminating contradictions. After building a consistent model of the area, the growing plants are recognized using artificial intelligence. The described technology allows automated analysis of the vegetation condition and provides conclusions and recommendations based on artificial intelligence.
The article is devoted to the issues of organization of monitoring and control of invasive plants growing in hard-to-reach places using a swarm of drones and a drone port. Sosnovsky's hogweed has spread widely in Russia and is actively seizing new areas, creating infestation steps that are difficult to control using traditional methods. Monitoring and elimination of such foci by traditional manual methods is time-consuming, ineffective and unsafe. A new technology that provides rapid monitoring of large areas and targeted chemical intervention only where necessary, reducing the risk of damage from invasions and the use of pesticides, is a technology based on the use of a drone swarm in conjunction with a drone port. It allows you to quickly explore large areas and get detailed images of growing vegetation from different angles. The resulting images can be recognized by means of artificial intelligence, analyzing the density of growth of invasive plants and their proximity to other crops. The data collected by agrodrones can be conditionally divided into digital and graphical. When receiving digital data from a swarm of drones, the information on the drone port is cleaned of noise and checked for consistency to ensure the reliability of the data, which improves the efficiency of system maintenance. For graphic data, first of all, color correction is used, restoring color details and increasing clarity, while restoring the natural image distorted at the time of digitization and subsequent processing. The key issue is the merging of the data collected by the agrodron swarm. Different specimens of agrodrons can receive different parameters and different images of the same habitat of invasive plants, and these data need to be linked to each other, eliminating contradictions. After building a consistent model of the area, the growing plants are recognized using artificial intelligence. The described technology allows automated analysis of the vegetation condition and provides conclusions and recommendations based on artificial intelligence.
Number of views: 5 Download in PDF
Monitoring Features of the Pipeline Systems Condition
Biogeosystem Technique. 2025. 12(2): 79-84.
Number of views: 4 Download in PDF
4. Nguyen Van Thinh, Do Phong Luu, Ngo Trung Dung, Nguyen Trong Hiep, Mai Quang Tuyen, Dinh Vu Anh Tu, Alla A. OkolelovaBiogeosystem Technique. 2025. 12(2): 79-84.
Abstract:
The article is devoted to the consideration of topical issues of organizing continuous monitoring of the pipeline system for pumping extracted natural gas and oil in hard-to-reach places based on the use of a drone port and a swarm of drones. A large amount of natural resources is extracted in the coastal shelf of the northern seas, where the water surface is covered with ice most of the time, and monitoring of the pipeline system by means of the auxiliary fleet is possible only during a short period of navigation, therefore automation of the monitoring process will allow for year-round monitoring and timely identification of emerging problems for their prompt elimination. Using a drone swarm with a droneport base station makes it possible to increase the efficiency of obtaining information by obtaining it more quickly from several alternative sources and then merging them.When information is received from the drone, the information is cleared of noise and checked for consistency to ensure a higher level of data reliability, which improves the efficiency of system maintenance. Data is cleared from noise by the drone port, while merging data from coherent sources and building a visual model of the pipeline system status is performed by a stationary computer after data is transmitted from the drone port via fiber-optic communication channels. The visual model, combined with parametric data obtained from sensors installed inside the pipeline, allows artificial intelligence systems to predict potential emergency conditions and plan routine repairs of the pipeline infrastructure until a real accident occurs with serious consequences. The introduction of an automated continuous monitoring system will allow the pipeline to be operated according to its actual technical condition, thereby reducing operating costs and ensuring the safety of its operation.
The article is devoted to the consideration of topical issues of organizing continuous monitoring of the pipeline system for pumping extracted natural gas and oil in hard-to-reach places based on the use of a drone port and a swarm of drones. A large amount of natural resources is extracted in the coastal shelf of the northern seas, where the water surface is covered with ice most of the time, and monitoring of the pipeline system by means of the auxiliary fleet is possible only during a short period of navigation, therefore automation of the monitoring process will allow for year-round monitoring and timely identification of emerging problems for their prompt elimination. Using a drone swarm with a droneport base station makes it possible to increase the efficiency of obtaining information by obtaining it more quickly from several alternative sources and then merging them.When information is received from the drone, the information is cleared of noise and checked for consistency to ensure a higher level of data reliability, which improves the efficiency of system maintenance. Data is cleared from noise by the drone port, while merging data from coherent sources and building a visual model of the pipeline system status is performed by a stationary computer after data is transmitted from the drone port via fiber-optic communication channels. The visual model, combined with parametric data obtained from sensors installed inside the pipeline, allows artificial intelligence systems to predict potential emergency conditions and plan routine repairs of the pipeline infrastructure until a real accident occurs with serious consequences. The introduction of an automated continuous monitoring system will allow the pipeline to be operated according to its actual technical condition, thereby reducing operating costs and ensuring the safety of its operation.
Number of views: 4 Download in PDF
Effects of Light and Temperature on Photosynthetic Capacity of Mangrove Species in the Southern Coastal Region of Vietnam
Biogeosystem Technique. 2025. 12(2): 85-97.
Number of views: 7 Download in PDF
5. Patrik YesayanBiogeosystem Technique. 2025. 12(2): 85-97.
Abstract:
Mangrove forests play a vital role in coastal protection and biodiversity maintenance, yet they are under severe pressure from climate change and human activities. This study evaluates the effects of light and temperature on the photosynthetic performance of five mangrove species (Sonneratia alba, Avicennia alba, Excoecaria agallocha, Ceriops zippeliana, and Bruguiera gymnorrhiza) in the southern coastal region of Vietnam, aiming to clarify their adaptability to climate change.
Mangrove forests play a vital role in coastal protection and biodiversity maintenance, yet they are under severe pressure from climate change and human activities. This study evaluates the effects of light and temperature on the photosynthetic performance of five mangrove species (Sonneratia alba, Avicennia alba, Excoecaria agallocha, Ceriops zippeliana, and Bruguiera gymnorrhiza) in the southern coastal region of Vietnam, aiming to clarify their adaptability to climate change.
Number of views: 7 Download in PDF
Short-Term Forecasting of Heavy Metal Concentrations in Soil: A Case Study of Some Regions of Armenia
Biogeosystem Technique. 2025. 12(2): 98-103.
Number of views: 6 Download in PDF
6. Biogeosystem Technique. 2025. 12(2): 98-103.
Abstract:
Abstract The article examines the potential of forecasting methods to evaluate changes in the concentration of lithophilic chemical elements in soil samples, with a focus on scenarios in which limited monitoring data are available. The analysis is based on averaged chemical element concentrations, which enables assessment of the overall direction of change without reference to individual sampling points. This approach facilitates comparisons among elements, thereby allowing the identification of discrepancies in their temporal dynamics. The forecasts indicate that the concentrations of certain elements (Rb, Zr) remain unchanged, whereas those of others (Ba, Sr) show directional change or increased variability. It is imperative to account for this when interpreting pollution dynamics in the absence of detailed spatial data.
Abstract The article examines the potential of forecasting methods to evaluate changes in the concentration of lithophilic chemical elements in soil samples, with a focus on scenarios in which limited monitoring data are available. The analysis is based on averaged chemical element concentrations, which enables assessment of the overall direction of change without reference to individual sampling points. This approach facilitates comparisons among elements, thereby allowing the identification of discrepancies in their temporal dynamics. The forecasts indicate that the concentrations of certain elements (Rb, Zr) remain unchanged, whereas those of others (Ba, Sr) show directional change or increased variability. It is imperative to account for this when interpreting pollution dynamics in the absence of detailed spatial data.
Number of views: 6 Download in PDF