Abstract:
In recent years, the agricultural industry has witnessed remarkable progresses driven by artificial intelligence (AI) technologies. These innovations have revolutionized traditional practices by enabling sophisticated monitoring and analysis techniques. In this context, we present a leading-edge application of AI in beekeeping, where a remote monitoring system equipped with sensors and algorithms provides unprecedented insights into hive conditions and bee performance. Colony Collapse Disorder (CCD) is a significant problem facing bee populations around the world, with serious implications for food production and the environment. Despite extensive research efforts, the precise cause of Colony Collapse Disorder (CCD) remains elusive and is believed to be the result of multiple contributing factors, including exposure to pesticides, presence of diseases, and inadequate nutrition. As a result, researchers have been exploring a variety of approaches to mitigate the impact of CCD and improve the health and productivity of bee colonies. This research aims to tackle the issue of Colony Collapse Disorder (CCD) in honey bee colonies by utilizing IoT sensors and Artificial Intelligence (AI) in bee hives. We have implemented a range of sensors including temperature, humidity, sound, weight, and IR sensors for bee counting. These sensors provide real-time data which is then analyzed by an AI model to detect any abnormalities or signs of CCD. Our system can also predict CCD at an early stage, allowing beekeepers to take timely preventive measures. Our methodology has great potential to help reduce the impact of CCD on honey bee colonies and improve the sustainability of beekeeping practices. By analyzing the data collected from various sensors, the AI model is able to identify any changes in the hive's environment that may indicate the early stages of CCD. This early detection and prediction can help beekeepers take timely preventive measures, such as adjusting the hive's conditions or treating the bees for any diseases, ultimately helping to mitigate the impact of CCD and maintain the health of honey bee colonies. Our methodology has the potential to significantly improve beekeeping practices and contribute to the sustainability of honey bee colonies. This research will inspire further research and development in this field and lead to the development of more advanced and effective methods to combat CCD.
Page(s):
81-81
DOI:
DOI not available
Published:
Journal: Abstract Book on International Conference on Food and Applied Sciences (ICFAS-23) 3-5 August 23, Volume: 0, Issue: 0, Year: 2023
Keywords:
IoT in Bee Keeping
,
AI for Beekeeping
,
Bee hive monitoring
,
CCD detection