Affiliation:
1. Istitute for Bio-Economy and Agri-Technology (iBO), Centre for Research and Technology—Hellas (CERTH), 10th km Charilaou-Thermi Road, Balkan Centre, 57001 Thermi, Greece
2. Department of Agricultural Development, Agri-Food and Natural Resources Management, National and Kapodistrian University of Athens (Evripos Campus), 34400 Psachna, Greece
Abstract
Apiculture has presented significant growth in the last decades in Europe and worldwide. According to the Food and Agriculture Organization (FAO), there were 25.1 million bee colonies in Europe in 2021, with most of them being located in the southeastern countries. Smart technologies have invaded almost every pillar of agriculture, including apiculture. Modern apiculture is rather more nomadic than sedentary. Nomadism in beekeeping requires monitoring the settlement of bee colonies, in more than one place per year, in order to select more honey and pollen and contribute to the overall growth of the bees. To this scope, it is efficient to monitor and have wide control of bees remotely, in parallel with other smart applications, in order to prevent crises that would affect bee survival and/or yield production. The objectives of this paper are to outline a series of automation systems in apiculture used as a means towards the optimization of bee apiary management processes. Four beekeepers’ case studies were used to demonstrate how sensors and communication means transfer multiple bee-related data from various bee apiary locations to a single control system. The methodology was based on input/output data evaluation, risk prioritization based on real data, and feedback to the beekeeper based on the potential risks. Based on the results, the most significant risks are related to bad weather conditions, varroa mites, and bee colony health. Furthermore, the beekeeper is able to optimize the whole management, operations, and strategic planning throughout the year. Last, it should be noted that the presented remote monitoring system will never substitute the necessity of traditional beekeeper visits, but it contributes to minimizing them based on the monitored daily data.
Reference26 articles.
1. An IoT based smart irrigation management system using Machine learning and open source technologies;Goap;Comput. Electron. Agric.,2018
2. Fountas, S., Mylonas, N., Malounas, I., Rodias, E., Santos, C.H., and Pekkeriet, E. (2020). Agricultural robotics for field operations. Sensors, 9.
3. Bohnenkamp, D., Behmann, J., and Mahlein, A.K. (2019). In-field detection of Yellow Rust in Wheat on the Ground Canopy and UAV Scale. Remote Sens., 21.
4. FAO (2024, June 14). FAO Statistics. Available online: https://www.fao.org/faostat/en/#data/QC.
5. Guruprasad, S.M., and Leiding, B. (2024). BeeOpen—An Open Data Sharing Ecosystem for Apiculture. Agriculture, 14.