蜂群多特征长期监测系统设计与试验研究

doi:10.12133/j.smartag.2020.2.2.202005-SA001

摘要/Abstract

摘要：

目前，针对蜂群发生崩溃式消失的现象还缺乏有效的观测和分析手段。本研究在分析蜂群行为与检测特征的基础上，设计了一种基于物联网技术的蜂群多特征长期监测系统。该系统采用太阳能供电，融合了多种传感器，能够检测蜂群的多个特征（蜂箱内部的温度、湿度、蜂群重量、声音和蜜蜂的进出量），并利用无线数据同步传输技术将这些数据上传到远程云服务器中。基于该系统，本研究还进行了针对意大利蜜蜂从2018年秋季到2020年春季为期235天的长期连续监测试验，记录了蜂群在秋衰期、越冬期和春繁期蜂箱内部温度、湿度、蜂群重量、声音和进出量的逐小时的细致变化。试验结果表明，在此期间，蜂箱内的平均温度呈现从25℃下降到-5℃再回升至15℃的抛物线变化，相应的进出巢次数也由大约8万次/天减少至0次/天再增加至5万次/天。在越冬期中，蜂群的重量呈现出大约25 g/天的线性下降趋势，同时蜂箱内也更为安静，声音的频率集中于0~64 Hz。由此表明，在不干扰蜂群的情况下，该监测系统获得的特征数据能够有效地揭示蜂群的日常活动和趋势变化，可用来研究蜂群的行为生物学、探索崩溃式的蜂群消失成因以及发展精确化蜜蜂养殖业。

关键词: 蜂群监测, 智能蜂箱, 多特征, 智慧农业, 物联网技术

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.

Key words: bee colony monitoring, smart hive, multiple features, smart agriculture, Internet of Things

中图分类号:

洪葳, 胥保华, 刘升平. 蜂群多特征长期监测系统设计与试验研究[J]. 智慧农业（中英文）, 2020, 2(2): 105-114.

HONG Wei, XU Baohua, LIU Shengping. Design and Experimental Research of Long-Term Monitoring System for Bee Colony Multiple Features[J]. Smart Agriculture, 2020, 2(2): 105-114.

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