智慧农业 (Jun 2020)
Design and Experimental Research of Long-Term Monitoring System for Bee Colony Multiple Features
Abstract
The pollination during bees’ foraging is vital to continue species on the earth. However, bee colonies in some areas of America and Europe frequently appeared colony collapse disorder in the past decade due to many possible factors such as climate change and pesticide usage, which has not received enough attention and positive response from human beings. In this research, bee colony’s activities were investigated with seven detectable features (i.e., weight, temperature, humidity, gas concentration, vibration, sound and entrance counts), and the applicability of the features was evaluated by considering four factors (i.e. the relevance to bee colony’s activities, the richness of information, the cheapness of cost and the simplicity of engineering). Based on the investigation and evaluation, an Internet of Things(IoT) based system was presented for long-time monitoring of bee colonies, which could hourly detect the temperature and humidity inside of hive, bee combs’ weight, bee colony’s sounds and bees’ counts of passing through hive entrance. In this system, each hive has an individual detection device for the monitoring of bee colony, and the colony information could be automatically collected and transferred to a remote cloud server which took responsible for the information storing. Finally, the users could freely visit the server to browse the history data and manage their bee colonies. Moreover, a 235 days continuous monitoring for Apis mellifera ligustica was performed from August, 2019 to April, 2020 to demonstrate the system performance, and long-time and one-day monitoring results were both analyzed. The monitoring results indicated that the system could continuously operate without human intervention, and the data could reveal bee colony’s activity and growth, e.g., the temperature and humidity could reflect the micro climate of the bee hive, the weight could show the forging and stock of food, the sounds contained lots of information about bees’ behavior and the entrance count was strongly related to the activeness and scale of bee colony. Compared with the reported monitoring system, this system is superior in the diversity of detected features, the capability of power self-support and the wireless of data transmission that can benefit to the system’s deployment in the field and long-term operation without maintenance. In the visible future, this system will effectively promote the study related to the biology of bee’s behavior, the reason of colony collapse disorder and the development of precision beekeeping.
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