Enhancing Precision Beekeeping by the Macro-Level Environmental Analysis of Crowdsourced Spatial Data
Precision beekeeping focuses on ICT approaches to collect data through various IoT solutions and systems, providing detailed information about individual bee colonies and apiaries at a local scale. Since the flight radius of honeybees is equal to several kilometers, it is essential to explore the sp...
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| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2025-01-01
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| Series: | ISPRS International Journal of Geo-Information |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2220-9964/14/2/47 |
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| Summary: | Precision beekeeping focuses on ICT approaches to collect data through various IoT solutions and systems, providing detailed information about individual bee colonies and apiaries at a local scale. Since the flight radius of honeybees is equal to several kilometers, it is essential to explore the specific conditions of the selected area. To address this, the aim of this study was to explore the potential of using crowdsourced data combined with geographic information system (GIS) solutions to support beekeepers’ decision-making on a larger scale. This study investigated possible methods for processing open geospatial data from the OpenStreetMap (OSM) database for the environmental analysis and assessment of the suitability of selected areas. The research included developing methods for obtaining, classifying, and analyzing OSM data. As a result, the structure of OSM data and data retrieval methods were studied. Subsequently, an experimental spatial data classifier was developed and applied to evaluate the suitability of territories for beekeeping. For demonstration purposes, an experimental prototype of a web-based GIS application was developed to showcase the results and illustrate the general concept of this solution. In conclusion, the main goals for further research development were identified, along with potential scenarios for applying this approach in real-world conditions. |
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| ISSN: | 2220-9964 |