Development of a solar powered bird repeller system with effective bird scarer sounds

Muminov, Azamjon; Jeon, Yun Chan; Na, Daeyoung; Lee, Cheolwon; Jeon, Heung Seok

doi:10.1109/icisct.2017.8188587

Cited by 22 publications

(8 citation statements)

References 2 publications

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“…The farmers can monitor the field conditions from anywhere. IoT-based smart farming is highly efficient when compared with the conventional approach [1,2].…”

Section: Introductionmentioning

confidence: 99%

RETRACTED: Modern Virtual Fencing Application: Monitoring and Controlling Behavior of Goats Using GPS Collars and Warning Signals

Muminov

Lee

et al. 2019

Sensors

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This paper describes our virtual fence system for goats. The present invention is a method of controlling goats without visible physical fences and monitoring their condition. Control occurs through affecting goats, using one or more sound signals and electric shocks when they attempt to enter a restricted zone. One of the best Machine Learning (ML) classifications named Support Vector Machines (SVM) is used to observe the condition. A virtual fence boundary can be of any geometrical shape. A smart collar on goats’ necks can be detected by using a virtual fence application. Each smart collar consists of a global positioning system (GPS), an XBee communication module, an mp3 player, and an electrical shocker. Stimuli and classification results are presented from on-farm experiments with a goat equipped with smart collar. Using the proposed stimuli methods, we showed that the probability of a goat receiving an electrical stimulus following an audio cue (dog and emergency sounds) was low (20%) and declined over the testing period. Besides, the RBF kernel-based SVM classification model classified lying behavior with an extremely high classification accuracy (F-score of 1), whilst grazing, running, walking, and standing behaviors were also classified with a high accuracy (F-score of 0.95, 0.97, 0.81, and 0.8, respectively).

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“…The farmers can monitor the field conditions from anywhere. IoT-based smart farming is highly efficient when compared with the conventional approach [1,2].…”

Section: Introductionmentioning

confidence: 99%

RETRACTED: Modern Virtual Fencing Application: Monitoring and Controlling Behavior of Goats Using GPS Collars and Warning Signals

Muminov

Lee

et al. 2019

Sensors

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“…An electronic bird repeller is an electronic sound system that emits the sound of a particular frequency which irritates the birds (See Fig. 2a) (Suryawanshi 2014;Muminov et al 2017;Baral et al 2019). A more common way to frighten the birds can be helped by "scare crow" discouraging the birds from nesting and sitting on PV module as shown in Fig.…”

Section: Bird's Repelling Systemsmentioning

confidence: 99%

Performance Enhancement of Solar Photovoltaic (PV) Module Using a Novel Flat Plate (NFP) Glass Cover by Reducing the Effect of Bird Dropping (BD) Settlement

Ak¹,

Rk²

2021

Preprint

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A massive BDs deposition on the common rectangular flat plate (RFP) of photovoltaic (PV) module is a matter of great concern in Western Rajasthan (WR) that diminish the overall energy production capacity of the system remarkably. In this research article, a prototype NFP design of a front glass cover of PV module is proposed to prevent the impact of BD settlement by the restriction of bird’s sitting/movement on the front glass cover. In this regard, the performance analysis of PV module with common RFP and newly designed NFP glass covers has been assessed at the different inclination β° (0- 90). The BD accumulation onto the both glass cover explored by the optical transmittance profiles at the different tilt angles, i.e., explained by bird movement on each flat glass surfaces. Consequently, a significant amount of output electric energy has been gained in NFP design rather than RFP corresponding to particular tilt regions TR I (0° ≤ β ≤ 25°), TR II (25° ≤ β ≤ 60°), and TR III (60° ≤ β ≤ 90°). According to the results achieved, an excellent level of improvement in average power loss, ~ 97.85%, corresponding to optimal TR(III) has been detected by employing NFP glass collector.

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“…We see more work in Muminov et al (2017) when a solar powered audible intelligent bird repeller system is developed based on Arduino UNO microcontroller to deter domestic birds which are a major threat in the field of agriculture causing damage to economic field crops, storage houses and also dirtying human life area. The other system components included a solar panel (7W, 12V), an intelligent PWM solar charge controller, 12V battery, MP3 Player, amplifier (Stereo 20W Class D Audio Amplifier -MAX9744), two 20W speakers, three sonar sensor and PIR sensor.…”

Section: Related Workmentioning

confidence: 99%

Automated Fall Armyworm (Spodoptera frugiperda, J.E. Smith) Pheromone Trap Based on Machine Learning

Chiwamba¹,

Phiri²,

Nkunika³

et al. 2019

Journal of Computer Science

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Maize is the main food crop that meets the nutritional needs of both humans and livestock in the sub-Saharan African region. Maize crop has in the recent past been threatened by the fall armyworm (Spodoptera frugiperda, J.E Smith) which has caused considerable maize yield losses in the region. Controlling this pest requires knowledge on the time, location and extent of infestation. In addition, the insect pest's abundance and environmental conditions should be predicted as early as possible for integrated pest management to be effective. Consequently, a fall armyworm pheromone trap was deployed as a monitoring tool in the present study. The trap inspection is currently carried out manually every week. The purpose of this paper is to bring automation to the trap. We modify the trap and integrate Internet of Things technologies which include a Raspberry Pi 3 Model B+ microcomputer , Atmel 8-bit AVR microcontroller, 3G cellular modem and various sensors powered with an off-grid solar photovoltaic system to capture real-time fall armyworm moth images, environmental conditions and provide real-time indications of the pest occurrences. The environmental conditions include Geographical Positioning System coordinates, temperature, humidity, wind speed and direction. The captured images together with environmental conditions are uploaded to the cloud server where the image is classified instantly using Google's pre-trained InceptionV3 Machine Learning model. Intended users view captured data including prediction accuracy via a web application. Once this smart technology is adopted, the labour-intensive task of monitoring will reduce while stakeholders shall be provided with a near real-time insight into the FAW situation in the field therefore enabling pro-activeness in their management of such a devastating pest.

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Development of a solar powered bird repeller system with effective bird scarer sounds

Cited by 22 publications

References 2 publications

RETRACTED: Modern Virtual Fencing Application: Monitoring and Controlling Behavior of Goats Using GPS Collars and Warning Signals

RETRACTED: Modern Virtual Fencing Application: Monitoring and Controlling Behavior of Goats Using GPS Collars and Warning Signals

Performance Enhancement of Solar Photovoltaic (PV) Module Using a Novel Flat Plate (NFP) Glass Cover by Reducing the Effect of Bird Dropping (BD) Settlement

Automated Fall Armyworm (Spodoptera frugiperda, J.E. Smith) Pheromone Trap Based on Machine Learning

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