Artificial neural network trained to identify mosquitoes in flight

Moore, Aubrey

doi:10.1007/bf01048285

Cited by 31 publications

(26 citation statements)

References 10 publications

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“…However, when the classification accuracy obtained in this study is compared to the performance of other automated systems reported in literature, the results are promising. Moore (1991) classified 84% of two mosquito species (differentiating between species and gender) correctly. In the OFIDIS-system, a correct classification of 69% was obtained for differentiating between five different Aphid species (Moore and Miller, 2002).…”

Section: Classification Of Bumblebee Species Based On Optical Sensor mentioning

confidence: 98%

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Optical identification of bumblebee species: Effect of morphology on wingbeat frequency

Roy

Baerdemaeker

Saeys

et al. 2014

Computers and Electronics in Agriculture

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Section: Classification Of Bumblebee Species Based On Optical Sensor mentioning

confidence: 98%

“…Chadwick (1939) used a stroboscope to investigate the wingbeat frequency of insects and reported that the wingbeat frequency is different between insect species (Reed et al, 1942;Moore, 1991). This wingbeat frequency can be measured in different ways.…”

Section: Introductionmentioning

confidence: 98%

Optical identification of bumblebee species: Effect of morphology on wingbeat frequency

Roy

Baerdemaeker

Saeys

et al. 2014

Computers and Electronics in Agriculture

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“…Using this device, they performed an analysis of the wing-beat frequency of two species of the genus Aedes from both sexes. The automatic classification of species and sex was subsequently presented in [19].…”

Section: Related Workmentioning

confidence: 99%

Exploring Low Cost Laser Sensors to Identify Flying Insect Species

Silva

Souza

Ellis

et al. 2015

J Intell Robot Syst

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Insects have a close relationship with the humanity, in both positive and negative ways. Mosquito borne diseases kill millions of people and insect pests consume and destroy around US $40 billion worth of food each year. In contrast, insects pollinate at least two-thirds of all the food consumed in the world. In order to control populations of disease vectors and agricultural pests, researchers in entomology have developed numerous methods including chemical, biological and mechanical approaches. However, without the knowledge of the exact location of the insects, the use of these techniques becomes costly and inefficient. We are developing a novel sensor as a tool to control disease vectors and agricultural pests. This sensor, which is built from inexpensive commodity electronics, captures insect flight information using laser light and classifies the insects according to their species. The use of machine learning techniques allows the sensor to automatically identify the species without human intervention. Finally, the sensor can provide real-time estimates of insect species with virtually no time gap between the insect identification and the delivery of population estimates. In this paper, we present our solution to the most important challenge to make this sensor practical: the creation of an accurate classification system. We show that, with the correct combination of feature extraction and machine learning techniques, we can achieve an accuracy of almost 90 % in the task of identifying the correct insect species among nine species. Specifically, we show that we can achieve an accuracy of 95 % in the task of correctly recognizing if a given event was generated by a disease vector mosquito.

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“…Artificial neural network methods have been investigated in identification of several types of insects. Moore [19] investigated the identification of mosquitoes in flight with an artificial neural network, while Moore and Miller [20] used artificial neural networks to identify flying insects with a photosensor. Peacock et al [21] investigated the application of using an artificial neural network to predict insect presence and absence.…”

Section: Identification Of Insects With Neural Network Analysismentioning

confidence: 99%

RF/microwave technology application for identification of stored-grain insects under electric fields

Ding

Jones

Weckler

2009

Sens. & Instrumen. Food Qual.

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The application of microwave technology to identify the species and quantities of seven stored-grain insects was investigated with a new measuring method using a sensor and an electric field device in the frequency range of 0.3-1200 MHz. Three different constant voltages (0, 20, and 40 V) and three different frequencies of alternating electric fields (0.01 Hz, 0.5 MHz, and 5 MHz) were tested to address the possibility of improving the identification of insects. Two-band pairs were optimized in the frequency range to maximize the identification and detection recognition rate using neural network techniques. The new measuring method resulted in high recognition rates for identifying both species and quantity of insects. Recognition rates of 95.5, 83.3, and 90.9% were achieved at the band-pair (660.1 and 768.1 MHz) under 0, 20, and 40 V constant electric fields respectively. Similarly, recognition rates of 90.0, 87.1, and 87.1% were respectively achieved at the band-pair (174.3 and 432.2 MHz) under 0.01 Hz, 0.5 MHz, and 5 MHz alternating electric fields.

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Artificial neural network trained to identify mosquitoes in flight

Cited by 31 publications

References 10 publications

Optical identification of bumblebee species: Effect of morphology on wingbeat frequency

Optical identification of bumblebee species: Effect of morphology on wingbeat frequency

Exploring Low Cost Laser Sensors to Identify Flying Insect Species

RF/microwave technology application for identification of stored-grain insects under electric fields

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