Electronic Traps for Automated Monitoring of Insect Populations

Holguín, Germán Andrés; Lehman, Brian L.; Hull, Larry A.; Jones, Vincent P.; Park, Johnny

doi:10.3182/20101206-3-jp-3009.00008

Cited by 20 publications

(18 citation statements)

References 8 publications

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“…2i, j) or for C. capitata (Goldshtein et al 2017). However, this electric signal-based approach in some cases could result in a very poor accuracy of the monitoring system, as shown by Holguin et al (2010) for the automated counting of C. pomonella and G. molesta by using electronic bucket traps (Fig. 2k, l).…”

Section: Insect Pest Identification and Countmentioning

confidence: 99%

Insect pest monitoring with camera-equipped traps: strengths and limitations

2020

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Integrated pest management relies on insect pest monitoring to support the decision of counteracting a given level of infestation and to select the adequate control method. The classic monitoring approach of insect pests is based on placing in single infested areas a series of traps that are checked by human operators on a temporal basis. This strategy requires high labor cost and provides poor spatial and temporal resolution achievable by single operators. The adoption of image sensors to monitor insect pests can result in several practical advantages. The purpose of this review is to summarize the progress made on automatic traps with a particular focus on camera-equipped traps. The use of software and image recognition algorithms can support automatic trap usage to identify and/or count insect species from pictures. Considering the high image resolution achievable and the opportunity to exploit data transfer systems through wireless technology, it is possible to have remote control of insect captures, limiting field visits. The availability of real-time and on-line pest monitoring systems from a distant location opens the opportunity for measuring insect population dynamics constantly and simultaneously in a large number of traps with a limited human labor requirement. The actual limitations are the high cost, the low power autonomy and the low picture quality of some prototypes together with the need for further improvements in fully automated pest detection. Limits and benefits resulting from several case studies are examined with a perspective for the future development of technology-driven insect pest monitoring and management.

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Section: Insect Pest Identification and Countmentioning

confidence: 99%

Insect pest monitoring with camera-equipped traps: strengths and limitations

2020

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“…The job of the microprocessor was to analyze the frequency content of the acquired recording and calculate the distance metric from the spectrum of the unknown incoming recording to the spectrum of pre-stored prototype spectra of the pest results in order to identify the insect. Other efforts are seen the in the work of Holguin et al (2010), when two electronic trap prototypes based on a microcontroller from Microchip Technology Inc. Model PIC18F8722A to automate the labour-intensive operations of monitoring insect populations and reduced the cost of integrated pest management programmes are compared. The trap in question was the bucket pheromone.…”

Section: Related Workmentioning

confidence: 99%

“…Many researchers modify the already existing traps by integrating single-board computers, microcontrollers and various sensors (Holguin et al, 2010;Facello and Cavallo, 2013). At this stage, we identified all the electronic components to be used to realize an automated energy independent pheromone trap and used Equation 2 to estimate the load for the Off-grid solar PV system:…”

Section: Determine Total Loadmentioning

confidence: 99%

See 1 more Smart Citation

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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“…The penetration of high-end technology in the automated identification of insects and possibly control of pests has occurred and is gaining solid ground [ 2 , 3 , 4 ], but is far from being considered established in view of cost-effective and practical applications adopted by cultivators. In [ 5 , 6 , 7 , 8 , 9 ] researchers tried to sense the presence of a targeted pest and report counts for various insects of interest. The vast majority of reported cases treat the insect as a falling body that causes a photo-interruption which leads to a binary decision and counting.…”

Section: Introductionmentioning

confidence: 99%

Automated Surveillance of Fruit Flies

Potamitis

Rigakis

Tatlas

2017

Sensors

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Insects of the Diptera order of the Tephritidae family cause costly, annual crop losses worldwide. Monitoring traps are important components of integrated pest management programs used against fruit flies. Here we report the modification of typical, low-cost plastic traps for fruit flies by adding the necessary optoelectronic sensors to monitor the entrance of the trap in order to detect, time-stamp, GPS tag, and identify the species of incoming insects from the optoacoustic spectrum analysis of their wingbeat. We propose that the incorporation of automated streaming of insect counts, environmental parameters and GPS coordinates into informative visualization of collective behavior will finally enable better decision making across spatial and temporal scales, as well as administrative levels. The device presented is at product level of maturity as it has solved many pending issues presented in a previously reported study.

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Electronic Traps for Automated Monitoring of Insect Populations

Cited by 20 publications

References 8 publications

Insect pest monitoring with camera-equipped traps: strengths and limitations

Insect pest monitoring with camera-equipped traps: strengths and limitations

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

Automated Surveillance of Fruit Flies

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