Application of fuzzy logic for honey bee colony state detection based on temperature data

Kviesis, Armands; Komašilovs, Vitālijs; Komasilova, Olvija; Zacepins, Aleksejs

doi:10.1016/j.biosystemseng.2020.02.010

Cited by 27 publications

(13 citation statements)

References 11 publications

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“…In [64], various system architectures implemented with different methods and approaches to monitor the beehive temperature were discussed. In [65], a solution for honey bee colony state identification using temperature data and fuzzy logic was proposed. The system is based on a fuzzy inference system that starting from the temperature data defines three possible states for the hive health status, i.e., normal, death, and extreme.…”

Section: Humidity and Temperature Measurementmentioning

confidence: 99%

A Smart Sensor-Based Measurement System for Advanced Bee Hive Monitoring

Cecchi

Spinsante

Terenzi

et al. 2020

Sensors

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The widespread decline of honey bee (Apis mellifera L.) colonies registered in recent years has raised great attention to the need of gathering deeper knowledge about this phenomenon, by observing the colonies’ activity to identify possible causes, and design corresponding countermeasures. In fact, honey bees have well-known positive effects on both the environment and human life, and their preservation becomes critical not only for ecological reasons, but also for the social and economic development of rural communities. Smart sensor systems are being developed for real-time and long-term measurement of relevant parameters related to beehive conditions, such as the hive weight, sounds emitted by the bees, temperature, humidity, and CO 2 inside the beehive, as well as weather conditions outside. This paper presents a multisensor platform designed to measure the aforementioned parameters from beehives deployed in the field, and shows how the fusion of different sensor measurements may provide insights on the status of the colony, its interaction with the surrounding environment, and the influence of climatic conditions.

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Section: Humidity and Temperature Measurementmentioning

confidence: 99%

A Smart Sensor-Based Measurement System for Advanced Bee Hive Monitoring

Cecchi

Spinsante

Terenzi

et al. 2020

Sensors

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“…In case of wireless sensor networks (Meikle & Holst, 2015;Ampatzidis et al, 2016;Kviesis, Komasilovs, & Komasilova, 2020) sensor nodes are likely to be exposed to the same external noise that can bias measurements of these sensors; therefore, it is important to establish correct data correlation. This is especially important for large industrial grade apiaries.…”

Section: Data Fusion Challengesmentioning

confidence: 99%

Data fusion challenges in precision beekeeping: a review

Bumanis

2020

Research for Rural Development

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The objective of precision beekeeping is to minimize resource consumption and maximize productivity of bees. This is achieved by detecting and predicting beehive states by monitoring apiary and beehive related parameters like temperature, weight, humidity, noise, vibrations, air pollution, wind, precipitation, etc. These parameters are collected as a raw input data by use of multiple different sensory devices, and are often imperfect and require creation of correlation between time data series. Currently, most researches focus on monitoring and processing each parameter separately, whereas combination of multiple parameters produces information that is more sophisticated. Raw input data sets that complement one another could be pre-processed by applying data fusion methods to achieve understanding about global research subject. There are multiple data fusion methods and classification models, distinguished by raw input data type or device usage, whereas sensor related data fusion is called sensor fusion. This paper analyses existing data fusion methods and process in order to identify data fusion challenges and correlate them with precision beekeeping objectives. The research was conducted over a period of 5 months, starting from October, 2019 and was based on analysis and synthesis of scientific literature. The conclusion was made that requirement of data fusion appliance in precision beekeeping is determined by a global research objective, whereas input data introduces main challenges of data and sensor fusion, as its attributes correlate with potential result.

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“…Modern precision beekeeping incorporates the use of multisensory system and decision support systems [3], [14] to provide real-time information and expert grade support for beekeepers. There are various studies [15], [16] depicting the basic precision beekeepingmonitoring one or multiple particular physical variable, and use mathematical models to determine the current colony state.…”

Section: Data Organization In Precision Beekeepingmentioning

confidence: 99%

“…Introducing fuzzy logic helps in prediction of colony states in short term. Kviesis et al [14] implemented fuzzy logic principles in early identification of honeybee colony states such as colony death and swarming using temperature inside hive, ambient temperature and a season. Komasilova et al [25] performed the study aimed to help beekeepers to select the optimal place for apiary location.…”

Section: Data Organization In Precision Beekeepingmentioning

confidence: 99%

Application of Data Layering in Precision Beekeeping: The Concept

Bumanis

Komasilova

Komašilovs

et al. 2020

2020 IEEE 14th International Conference on Application of Information and Communication Technologies (AICT)

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The monitoring and predictions of various multilevel states of honeybee colonies are performed using emerging Internet of Things technologies and data processing methods. It is become common to use multiple sensors and devices providing multi-modal data to monitor a single activity. Modern data analysis and data processing procedures include a step of data fusion in order to provide more accurate input data. This, however, requires implementation of machine learning and large data sets, whereas gathering large data sets of real time and observation data is a common problem for small to medium size apiaries. This why there are no real implementation of data fusion method in precision beekeeping field. The aim of this paper was to introduce the concept of data layering, which aims to solve the global precision beekeeping problems without implementation of machine learning. The concept was demonstrated within the scope of foraging optimization problem using three data sets: flowering calendar data, rainfall precipitation data and bee activity data.

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Application of fuzzy logic for honey bee colony state detection based on temperature data

Cited by 27 publications

References 11 publications

A Smart Sensor-Based Measurement System for Advanced Bee Hive Monitoring

A Smart Sensor-Based Measurement System for Advanced Bee Hive Monitoring

Data fusion challenges in precision beekeeping: a review

Application of Data Layering in Precision Beekeeping: The Concept

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