IoT-Based Bee Swarm Activity Acoustic Classification Using Deep Neural Networks

Žgank, Andrej

doi:10.3390/s21030676

Cited by 45 publications

(29 citation statements)

References 31 publications

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“…In the future, one may solve the fractional form of the singular models with Neumann–Robin, Dirichlet, and Neumann boundary conditions using the proposed ANN-GA-SQPM. Additionally, the memetic computing paradigm of ANN-GA-SQPM can be a good alternative to be exploited for problems involving the study of sensors [ 47 , 48 , 49 , 50 , 51 ].…”

Section: Discussionmentioning

confidence: 99%

Artificial Neural Networks to Solve the Singular Model with Neumann–Robin, Dirichlet and Neumann Boundary Conditions

Nisar

Sabir

Raja

et al. 2021

Sensors

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The aim of this work is to solve the case study singular model involving the Neumann–Robin, Dirichlet, and Neumann boundary conditions using a novel computing framework that is based on the artificial neural network (ANN), global search genetic algorithm (GA), and local search sequential quadratic programming method (SQPM), i.e., ANN-GA-SQPM. The inspiration to present this numerical framework comes through the objective of introducing a reliable structure that associates the operative ANNs features using the optimization procedures of soft computing to deal with such stimulating systems. Four different problems that are based on the singular equations involving Neumann–Robin, Dirichlet, and Neumann boundary conditions have been occupied to scrutinize the robustness, stability, and proficiency of the designed ANN-GA-SQPM. The proposed results through ANN-GA-SQPM have been compared with the exact results to check the efficiency of the scheme through the statistical performances for taking fifty independent trials. Moreover, the study of the neuron analysis based on three and 15 neurons is also performed to check the authenticity of the proposed ANN-GA-SQPM.

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Section: Discussionmentioning

confidence: 99%

Artificial Neural Networks to Solve the Singular Model with Neumann–Robin, Dirichlet and Neumann Boundary Conditions

Nisar

Sabir

Raja

et al. 2021

Sensors

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“…Our AI approach differs from the mentioned one, because we try to solve the regression problem and to draw a conclusion about the status of the hive based on the activity of the bees, i.e., whether the conditions in the hive are healthy or unhealthy. Similar approach was presented in [59,60] where the deep neural networks were used to classify bee swarm activity from audio signals.…”

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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“…Intelligent agriculture, based on Internet of Things (IoT) technologies, allows farmers to reduce waste and increase productivity, from irrigation with greater precision to the amount of fertilizer used [2]. In addition, it can help prevent pollution [3], monitoring climatic conditions at low cost [4], and bring solutions by adding artificial intelligence methods [5,6].…”

Section: Introductionmentioning

confidence: 99%

IndoorPlant: A Model for Intelligent Services in Indoor Agriculture Based on Context Histories

Martini

Helfer

Barbosa

et al. 2021

Sensors

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The application of ubiquitous computing has increased in recent years, especially due to the development of technologies such as mobile computing, more accurate sensors, and specific protocols for the Internet of Things (IoT). One of the trends in this area of research is the use of context awareness. In agriculture, the context involves the environment, for example, the conditions found inside a greenhouse. Recently, a series of studies have proposed the use of sensors to monitor production and/or the use of cameras to obtain information about cultivation, providing data, reminders, and alerts to farmers. This article proposes a computational model for indoor agriculture called IndoorPlant. The model uses the analysis of context histories to provide intelligent generic services, such as predicting productivity, indicating problems that cultivation may suffer, and giving suggestions for improvements in greenhouse parameters. IndoorPlant was tested in three scenarios of the daily life of farmers with hydroponic production data that were obtained during seven months of cultivation of radicchio, lettuce, and arugula. Finally, the article presents the results obtained through intelligent services that use context histories. The scenarios used services to recommend improvements in cultivation, profiles and, finally, prediction of the cultivation time of radicchio, lettuce, and arugula using the partial least squares (PLS) regression technique. The prediction results were relevant since the following values were obtained: 0.96 (R2, coefficient of determination), 1.06 (RMSEC, square root of the mean square error of calibration), and 1.94 (RMSECV, square root of the mean square error of cross validation) for radicchio; 0.95 (R2), 1.37 (RMSEC), and 3.31 (RMSECV) for lettuce; 0.93 (R2), 1.10 (RMSEC), and 1.89 (RMSECV) for arugula. Eight farmers with different functions on the farm filled out a survey based on the technology acceptance model (TAM). The results showed 92% acceptance regarding utility and 98% acceptance for ease of use.

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IoT-Based Bee Swarm Activity Acoustic Classification Using Deep Neural Networks

Cited by 45 publications

References 31 publications

Artificial Neural Networks to Solve the Singular Model with Neumann–Robin, Dirichlet and Neumann Boundary Conditions

Artificial Neural Networks to Solve the Singular Model with Neumann–Robin, Dirichlet and Neumann Boundary Conditions

IoT Monitoring and Prediction Modeling of Honeybee Activity with Alarm

IndoorPlant: A Model for Intelligent Services in Indoor Agriculture Based on Context Histories

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