Computer Systemusing Internet of Things for Monitoring of Bee Hives

Dineva, Kristina

doi:10.5593/sgem2017h/63/s25.022

Cited by 4 publications

(4 citation statements)

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“…The significance of constant monitoring [32][33][34][35][36][37][38] in terms of monitoring and measuring various parameters of influence within and immediately around the hive is shown in a large number of papers [39][40][41][42][43][44][45][46][47][48]. Most papers relied on the application of IoT technologies in designing these systems [19,[49][50][51][52][53][54][55][56][57][58].…”

Section: Related Workmentioning

confidence: 99%

IoT Monitoring and Prediction Modeling of Honeybee Activity with Alarm

et al. 2022

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A significant number of recent scientific papers have raised awareness of changes in the biological world of bees, problems with their extinction, and, as a consequence, their impact on humans and the environment. This work relies on precision beekeeping in apiculture and raises the scale of measurement and prediction results using the system we developed, which was designed to cover beehive ecosystem. It is equipped with an IoT modular base station that collects a wide range of parameters from sensors on the hive and a bee counter at the hive entrance. Data are sent to the cloud for storage, analysis, and alarm generation. A time-series forecasting model capable of estimating the volume of bee exits and entrances per hour, which simulates dependence between environmental conditions and bee activity, was devised. The applied mathematical models based on recurrent neural networks exhibited high accuracy. A web application for monitoring and prediction displays parameters, measured values, and predictive and analytical alarms in real time. The predictive component utilizes artificial intelligence by applying advanced analytical methods to find correlation between sensor data and the behavioral patterns of bees, and to raise alarms should it detect deviations. The analytical component raises an alarm when it detects measured values that lie outside of the predetermined safety limits. Comparisons of the experimental data with the model showed that our model represents the observed processes well.

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Section: Related Workmentioning

confidence: 99%

IoT Monitoring and Prediction Modeling of Honeybee Activity with Alarm

et al. 2022

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“…The LEDs are used to observe the foraging activity, whereas SHTII sensors test the environmental conditions. An IoT based system [36] combines all the advances of existing IoT based beehive monitoring systems and adds more functionalities on top of it. The system comprises intelligent sensors installed inside and outside the hive and driven by a solar energy panel, which gathers real-time data.…”

Section: Multiple Sensor-based Approachesmentioning

confidence: 99%

A CNN-Based Identification of Honeybees' Infection using Augmentation

Kaur

Ardekani

Sharifzadeh

et al. 2022

2022 International Conference on Electrical, Computer, Communications and Mechatronics Engineering (ICECCME)

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Artificial Intelligence (AI) solutions have been extensively explored and implemented in the apiculture industry. Designing an efficient system for continuous observation of bees for their health and activities has been a challenge and of great interest among researchers. Several studies manifest that AI-based solutions can accommodate the purpose of beehive monitoring. However, the systems constructed based on computer vision techniques are found to be incompetent with noisy and insufficient data. This research proposes a computer vision-based approach to classify bee images for infected or healthy bees, dealing with indistinct and inadequate image data. As a solution, image contrast enhancement and data augmentation methods are used to improve the quality and size of data, respectively. The foggy bee images are managed using the Contrast Limited Adaptive Histogram Equalization (CLAHE), enhancing the image contrast to make it more understandable. The small dataset is augmented using an advanced method, Deep Convolutional Generative Adversarial Networks (DCGAN), which is capable of generating new images resembling the real ones. We investigate the capability of DCGAN as an alternative to the conventional augmentation technique that uses the varied geometric transformation of the image. An optimized Convolutional Neural Network (CNN) model is used to classify the augmented bee image data. Further, one of the transfer learning techniques, VGG16, is applied to compare the classifiers' performance on the same data sets. The effectiveness of the preprocessing method CLAHE and augmentation method DCGAN is assessed against original images and conventional augmentation methods. The classifiers CNN and VGG16 are compared alongside for their performance on non-augmented and augmented data sets. The investigations conducted on the given bee data sets delivered promising results. The CLAHE pre-processing method not only improved the image sharpness by 10% but also enhanced the CNN classifier's performance as compared to the original images. The optimized CNN's performance enhanced with conventional geometrically transformed augmented data. However, the classifier outperformed with added DCGAN generated synthetic images in the original data set providing up to 99.9% accuracy. Further, fine-tuned transfer learning classification model provided comparable accuracy alongside CNN with added efficiency building and executing the model. The success of this preliminary investigation paves the way to explore different variations of Generative Adversarial Network (GAN) to augment the image data together with enhancing its quality.

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“…Para o envio via Telegram a média foi de 9 Em algumas notificac ¸ões estudadas a coleta não estaria em atraso devido ao intervalo de checagem de atraso de cada usuário ser diferente. 10 Apesar das variáveis estarem dentro dos padrões dos usuários, algumas notificac ¸ões foram cadastradas para enviar os dados independente das configurac ¸ões. Para identificar o atraso total no envio das notificac ¸ões é necessário um estudo mais amplo, incluindo o tempo de 11 http://www.totalvoice.com.br/ processamento do servidor Sm@rtbee, o tempo de envio para o servidor que de fato irá enviar a mensagem e o tempo em que o cliente recebeu a mensagem.…”

Section: Avaliac ¸ãO Do Sistemaunclassified

“…Idealmente, uma análise prévia automática das grandezas deve ser feita para posterior envio de informac ¸ões mais relevantes para o apicultor. Uma das maneiras de informar ao apicultor sobre as grandezas coletadas é por meio do envio de notificac ¸ões/alertas [9,10,11]. Essas notificac ¸ões podem ser extremamente úteis para indicar qual ou quais itens devem receber mais atenc ¸ão pelo apicultor durante os manejos (ou inspec ¸ões) preventivos ou corretivos [12].…”

Section: Introduc ¸ãOunclassified

BeeNotified! A Notification System of Physical Quantities for Beehives Remote Monitoring

Braga

Rabelo

Callado

et al. 2020

RITA

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One of the ways to reduce inappropriate management of hives and monitor bee health is to send notifications/alerts about the data collected through sensors. This study presents BeeNotified!, a solution for sending notifications through Telegram, e-mail, and SMS. The notifications warn about the level of temperature, humidity, sound, carbon dioxide, oxygen, hive weight and delay in data gathering. From this data, researchers and beekeepers can be informed and make changes in the locations of the hives, avoiding catastrophes and possible diseases. The results obtained with the processing time in the sending of messages showed that the messages sent via SMS and Telegram have a shorter processing time compared to the sending via e-mail. In regards to sending notifications according to user preferences, all notifications were sent correctly.

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Computer Systemusing Internet of Things for Monitoring of Bee Hives

Cited by 4 publications

References 0 publications

IoT Monitoring and Prediction Modeling of Honeybee Activity with Alarm

IoT Monitoring and Prediction Modeling of Honeybee Activity with Alarm

A CNN-Based Identification of Honeybees' Infection using Augmentation

BeeNotified! A Notification System of Physical Quantities for Beehives Remote Monitoring

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