An IoT Smart Rodent Bait Station System Utilizing Computer Vision

Ross, Robert; Parsons, Lyle; Thai, Ba; Hall, Richard; Kaushik, Meha

doi:10.3390/s20174670

Cited by 13 publications

(5 citation statements)

References 19 publications

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“…Considering the rodent monitoring application, there is limited rodent control research on using IoT technology in recent years. One of the most common traditional approaches is placing bait stations [47,48], which may contain poisons that could harm pets and even people. In 2019, a low-power remote monitoring system was proposed to transmit data on bait and battery [47].…”

Section: Related Applications With Lora Technologymentioning

confidence: 99%

“…In 2019, a low-power remote monitoring system was proposed to transmit data on bait and battery [47]. In 2020, [48] presented a low-power bait station monitoring system Bait combined with computer vision, which can wirelessly report data from bait stations. To ensure the sustainability of the Earth, rodent infestation prevention should be implemented using non-toxic approaches that allow for no delay.…”

Section: Related Applications With Lora Technologymentioning

confidence: 99%

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A Remote Monitoring System for Rodent Infestation Based on LoRaWAN

Lai

Wang

Liu

et al. 2023

Sensors

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Rodent infestations are a common problem that can result in several issues, including diseases, damage to property, and crop loss. Conventional methods of controlling rodent infestations often involve using mousetraps and applying rodenticides manually, leading to high manpower expenses and environmental pollution. To address this issue, we introduce a system for remotely monitoring rodent infestations using Internet of Things (IoT) nodes equipped with Long Range (LoRa) modules. The sensing nodes wirelessly transmit data related to rodent activity to a cloud server, enabling the server to provide real-time information. Additionally, this approach involves using images to auxiliary detect rodent activity in various buildings. By capturing images of rodents and analyzing their behavior, we can gain insight into their movement patterns and activity levels. By visualizing the recorded information from multiple nodes, rodent control personnel can analyze and address infestations more efficiently. Through the digital and quantitative sensing technology proposed at this stage, it can serve as a new objective indicator before and after the implementation of medication or other prevention and control methods. The hardware cost for the proposed system is approximately USD 43 for one sensor module and USD 17 for one data collection gateway (DCG). We also evaluated the power consumption of the sensor module and found that the 3.7 V 18,650 Li-ion batteries in series can provide a battery life of two weeks. The proposed system can be combined with rodent control strategies and applied in real-world scenarios such as restaurants and factories to evaluate its performance.

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Section: Related Applications With Lora Technologymentioning

confidence: 99%

Section: Related Applications With Lora Technologymentioning

confidence: 99%

A Remote Monitoring System for Rodent Infestation Based on LoRaWAN

Lai

Wang

Liu

et al. 2023

Sensors

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show abstract

“…With the technological advances in computer science and engineering, designing automated toxin delivery devices specific to target species is now achievable (Meek et al 2020;Moseby et al 2020;Ross et al 2020;Corva et al 2022). We aimed to provide a reliable means of automatically replenishing baits while minimising undesirable bait loss (e.g.…”

Section: Introductionmentioning

confidence: 99%

The Sentinel Bait Station: an automated, intelligent design pest animal baiting system

et al. 2023

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Context. Ground baiting is a strategic method for reducing vertebrate pest populations. Best practice involves maximising bait availability to the target species, although sustaining this availability is resource intensive because baits need to be replaced each time they are taken. This study focused on improving pest population management through the novel baiting technique outlined in this manuscript, although there is potential use across other species and applications (e.g. disease management). Aims. To develop and test an automated, intelligent, and semipermanent, multi-bait dispenser that detects target species before distributing baits and provides another bait when a target species revisits the site. Methods. We designed and field tested the Sentinel Bait Station, which comprises a camera trap with in-built species-recognition capacity, wireless communication and a dispenser with the capacity for five baits. A proof-of-concept prototype was developed and validated via laboratory simulation with images collected by the camera. The prototype was then evaluated in the field under real-world conditions with wild-living canids, using non-toxic baits. Key results. Field testing achieved 19 automatically offered baits with seven bait removals by canids. The underlying image recognition algorithm yielded an accuracy of 90%, precision of 83%, sensitivity of 68% and a specificity of 96% throughout field testing. The response time of the system, from the point of motion detection (within 6-10 m and the field-of-view of the camera) to a bait being offered to a target species, was 9.81 ± 2.63 s. Conclusion.The Sentinel Bait Station was able to distinguish target species from non-target species. Consequently, baits were successfully deployed to target species and withheld from non-target species. Therefore, this proof-of-concept device is able to successfully provide baits to successive targets from secure on-board storage, thereby overcoming the need for daily bait replacement. Implications. The proof-of-concept Sentinel Bait Station design, together with the findings and observations from field trials, confirmed the system can deliver multiple baits and increase the specificity in which baits are presented to the target species using artificial intelligence. With further refinement and operational field trials, this device will provide another tool for practitioners to utilise in pest management programs.

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“…An Internet of Things (IoT)-based rodent infestation monitoring system is reported in [ 10 ], where the authors utilized sensor-based and vision-based algorithms for rodent monitoring. Ross et al reported the development of a smart rodent monitoring system called

that uses computer vision and IoT for inspection within a rodent bait station [ 11 , 12 ]. Sowmika et al discuss a method of rodent detection in farmlands, where a PIR sensor is used to detect the motion of rodents within a range of ten meters, and sensor data collected are pushed to the cloud for enabling remote monitoring [ 13 ].…”

Section: Introductionmentioning

confidence: 99%

Towards an Optimal Footprint Based Area Coverage Strategy for a False-Ceiling Inspection Robot

Pathmakumar

Sivanantham

Padmanabha

et al. 2021

Sensors

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False-ceiling inspection is a critical factor in pest-control management within a built infrastructure. Conventionally, the false-ceiling inspection is done manually, which is time-consuming and unsafe. A lightweight robot is considered a good solution for automated false-ceiling inspection. However, due to the constraints imposed by less load carrying capacity and brittleness of false ceilings, the inspection robots cannot rely upon heavy batteries, sensors, and computation payloads for enhancing task performance. Hence, the strategy for inspection has to ensure efficiency and best performance. This work presents an optimal functional footprint approach for the robot to maximize the efficiency of an inspection task. With a conventional footprint approach in path planning, complete coverage inspection may become inefficient. In this work, the camera installation parameters are considered as the footprint defining parameters for the false ceiling inspection. An evolutionary algorithm-based multi-objective optimization framework is utilized to derive the optimal robot footprint by minimizing the area missed and path-length taken for the inspection task. The effectiveness of the proposed approach is analyzed using numerical simulations. The results are validated on an in-house developed false-ceiling inspection robot—Raptor—by experiment trials on a false-ceiling test-bed.

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An IoT Smart Rodent Bait Station System Utilizing Computer Vision

Cited by 13 publications

References 19 publications

A Remote Monitoring System for Rodent Infestation Based on LoRaWAN

A Remote Monitoring System for Rodent Infestation Based on LoRaWAN

The Sentinel Bait Station: an automated, intelligent design pest animal baiting system

Towards an Optimal Footprint Based Area Coverage Strategy for a False-Ceiling Inspection Robot

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