Design of birds detector and repellent using frequency based arduino uno with android system

Siahaan, Yahot; Wardijono, Bheta Agus; Mukhlis, Yulisdin

doi:10.1109/icitisee.2017.8285503

Cited by 11 publications

(5 citation statements)

References 1 publication

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“…Aside from such indispensable consequences, the testing of the device was conducted on a trial and error basis. [29] designed a bird detector and repellent system using frequency based Arduino uno with android system Birds were detected and repelled. This device cannot differentiate between birds and human, therefore, if a human passes, the device turns on and wastes energy and causes noise.…”

Section: Literature Reviewmentioning

confidence: 99%

Design and Development of an Energy-efficient Audio-based Repellent System for Rice Fields

Arowolo¹,

Fayose²,

Omowaye³

et al. 2022

IJETAE

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Bird infestation is one of the major limiting factors affecting the production of indigenous rice in Nigeria. Conventional audio-based systems used in repelling birds deteriorate in effectiveness, cause noise pollution and consume excess power. This research therefore, developed an energyefficient audio-based repellent system for rice fields incorporated with a convolutional neural network (CNN) model for bird detection. A CNN algorithm was developed using inception V3, built on transfer learning technique in google colab. The CNN model was trained with bird dataset of 275 bird species obtained from Kaggle database. The core of the system was the Raspberry pi 4, a low-powered microcomputer adopted to run the CNN model with information from a camera. The mechanical framework for the system was designed to support its sustainability in rice fields. The estimated coverage area was 42m2. The system used maximum power of 4.10W. The system supported multiple production of various sounds to prevent bird adaptability to fixed sounds, which could sustain its effectiveness. The incorporated camera aided in the detection of birds with consequent reduction in noise pollution inherent in conventional audio devices. The developed system used 53.52% of the average power reported for conventional audio systems. The developed audio-based repellent system is better in terms of bird detection, power consumption and less noise pollution. Further development on this system can be carried out with other detection algorithms which may require less power for processing. Keywords— Bird repellent system, convolutional neural network, audio repellent, rice field, energy-efficient

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Section: Literature Reviewmentioning

confidence: 99%

Design and Development of an Energy-efficient Audio-based Repellent System for Rice Fields

Arowolo¹,

Fayose²,

Omowaye³

et al. 2022

IJETAE

View full text Add to dashboard Cite

show abstract

“…The literature, by extension, includes comprehensive articles addressing both the detection and the scaring of birds [ 30 ]. One of these sources is referenced in study [ 31 ], which characterizes the prototype of a device that comprises PIR sensors as the detector to track moving birds and uses LC oscillator type colpitts with a piezo ultrasonic sensor as the repeller. An ultrasonic repeller is described also in source [ 32 ], whose authors nevertheless follow a markedly more sophisticated path, exploiting machine learning algorithms.…”

Section: Previous Researchmentioning

confidence: 99%

A System Using Artificial Intelligence to Detect and Scare Bird Flocks in the Protection of Ripening Fruit

Marcoň

Janoušek

Pokorny

et al. 2021

Sensors

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Flocks of birds may cause major damage to fruit crops in the ripening phase. This problem is addressed by various methods for bird scaring; in many cases, however, the birds become accustomed to the distraction, and the applied scaring procedure loses its purpose. To help eliminate the difficulty, we present a system to detect flocks and to trigger an actuator that will scare the objects only when a flock passes through the monitored space. The actual detection is performed with artificial intelligence utilizing a convolutional neural network. Before teaching the network, we employed videocameras and a differential algorithm to detect all items moving in the vineyard. Such objects revealed in the images were labeled and then used in training, testing, and validating the network. The assessment of the detection algorithm required evaluating the parameters precision, recall, and F1 score. In terms of function, the algorithm is implemented in a module consisting of a microcomputer and a connected videocamera. When a flock is detected, the microcontroller will generate a signal to be wirelessly transmitted to the module, whose task is to trigger the scaring actuator.

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“…By virtue of advances in software technology and improvements in the performance and affordability of sensors and computers, bird detection methods have diversified, especially in terms of offering improved accuracy and cost-effective approaches. Recent bird-detection methods can be categorized into two major modalities based on survey articles: visual [5][6][7][8] and audio [9][10][11]. Numerous outstanding methods have been proposed from both modalities.…”

Section: Related Studiesmentioning

confidence: 99%

Mallard Detection Using Microphone Arrays Combined with Delay-and-Sum Beamforming for Smart and Remote Rice–Duck Farming

et al. 2021

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This paper presents an estimation method for a sound source of pre-recorded mallard calls from acoustic information using two microphone arrays combined with delay-and-sum beamforming. Rice farming using mallards saves labor because mallards work instead of farmers. Nevertheless, the number of mallards declines when they are preyed upon by natural enemies such as crows, kites, and weasels. We consider that efficient management can be achieved by locating and identifying the locations of mallards and their natural enemies using acoustic information that can be widely sensed in a paddy field. For this study, we developed a prototype system that comprises two sets of microphone arrays. We used 64 microphones in all installed on our originally designed and assembled sensor mounts. We obtained three acoustic datasets in an outdoor environment for our benchmark evaluation. The experimentally obtained results demonstrated that the proposed system provides adequate accuracy for application to rice–duck farming.

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Design of birds detector and repellent using frequency based arduino uno with android system

Cited by 11 publications

References 1 publication

Design and Development of an Energy-efficient Audio-based Repellent System for Rice Fields

Design and Development of an Energy-efficient Audio-based Repellent System for Rice Fields

A System Using Artificial Intelligence to Detect and Scare Bird Flocks in the Protection of Ripening Fruit

Mallard Detection Using Microphone Arrays Combined with Delay-and-Sum Beamforming for Smart and Remote Rice–Duck Farming

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