CONCEPT SOLUTION OF AUTONOMOUS IOT SMART HIVE AND OPTIMIZATION OF ENERGY CONSUMPTION USING ARTIFICIAL INTELLIGENCE
Abstract
In this paper the authors present a conceptual solution for an autonomous smart beehive with a focus on energy efficiency; the hive's existence is based on artificial intelligence. The hive is equipped with an advanced system for monitoring the entry and exit of bees, as well as for collecting data on the weather inside and around the hive. Using an array of sensors controlled by Espressif ESP32 and Arduino Mega microcontroller boards, the hive continuously optimizes the operation of the ventilation system and other components, monitoring energy consumption and adapting to changing conditions. Special accents in the work are dedicated to the monitoring of the solar panel and, consequently, the capacity of the battery for independent power supply of the system, as well as the application of artificial intelligence to predict meteorological changes and optimize energy efficiency. This paper provides a comprehensive overview of the solutions and technologies that enable the autonomous and energy-efficient functioning of the Smart Hive.
References
Al-Rajhi, M. (2022). Warming Beehives with Solar Energy Stored in Water. Türkiye Tarımsal Araştırmalar Dergisi, 9(3), 286-294.
Andrijević, N., Urošević, V. (2021). Concept of Adaptive Smart IoT Technology for Application in Smart Agriculture. Scientific and Professional Conference with International Participation Politehnika, pp. 749.
Andrijević, N., Radivojević, V., Radaković, D., Milovanović, D., Suljović, S. (2023). Conceptual model of a system for optimizing the temperature and humidity of honeybee hives using artificial intelligence. Scientific and Professional Conference with International Participation Politehnika, Belgrade, pp. 1266-1269.
Andrijević, N., Urošević, V. (2022). Development of software for control of beehive ecosystem with alarm. YU-INFO conference 2022, pp. 173–175.
Andrijević, N., Urošević, V., Arsic, B., Herceg, D., Savić, B. (2022). IoT Monitoring and Prediction Modeling of Honeybee Activity with Alarm. Electronics, 11(5), 783.
Andrijević, N., Urošević, V. (2021). Development of a System for Continuous Monitoring of the State of the Bee Society. YU-INFO conference 2021, pp. 123–126.
El-Sayed, A. S., Al-Rajhi, M. A. I., & Sharobeem, Y. F. (2024). Development of an automatic solar powered honey bee feeding system in a movable multi shelves apiary. Journal of Apicultural Research, 63(1), 18-31.
He, W., Zhang, S., Hu, Z., Zhang, J., Liu, X., Yu, C., & Yu, H. (2020). Field experimental study on a novel beehive integrated with solar thermal/photovoltaic system. Solar Energy, 201, 682-692.
Joshi, D. B. (2022). Design and Implementation of Sustainable Smart Beehive Monitoring. University of California, Riverside.
Kulyukin, V., & Reka, S. K. (2016). A computer vision algorithm for omnidirectional bee counting at langstroth beehive entrances. In Proceedings of the International Conference on Image Processing, Computer Vision, and Pattern Recognition (IPCV) (p. 229).
Ntawuzumunsi, E., Kumaran, S., & Sibomana, L. (2021). Self-Powered Smart Beehive Monitoring and Control System (SBMaCS). Sensors, 21(10), 3522.
Pane, S. B., Nuraini, C., & Syam, F. H. (2022). BeeHive Co-Working with Green Architecture Approach. In The International Conference on Education, Social Sciences and Technology (ICESST), 1(1), pp. 178-187.
Shah K. (2017). Power analysis of continuous data capture in beepi, a solarpowered multi-sensor electronic beehive monitoring system for langstroth beehives. Utah state university Logan, Utah.
Zacepins, A., Kviesis, A., Ahrendt, P., Richter, U., Tekin, S., & Durgun, M. (2016). Beekeeping in the future - Smart apiary management. In 2016 17th International Carpathian Control Conference (ICCC), pp. 808-812.
http://www.sustainablebusinesstoolkit.com/solar-beehive/ (28.02.2024.)
http://www.thermosolarhive.com/en/ (28.02.2024.)
http://www.pveurope.eu/installation/solar-beehive-protecting-bees-solar-energy (28.02.2024.)