Acoustic Methods for the Automated Detection and Identification of Insects

Chesmore, E.D.; Nellenbach, C.

doi:10.17660/actahortic.2001.562.26

Cited by 18 publications

(13 citation statements)

References 0 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…For example, visual inspection showed that 4% of wheat samples from 79 U.S. grain elevators were infested with insects, while incubation of the same wheat samples over 3 to 6 weeks showed 16% insect infestation (Storey et al, 1982). Other detection techniques include: (a) selective fluorescent stains (Milner et al, 1950a), (b) x-ray inspection (Milner et al, 1950b;Schatzki and Fine, 1988;Keagy and Schatzki, 1993;Throne, 1994;AACC, 2001;Haff, 2001), (c) cracking and flotation (AACC, 2001), (d) acid hydrolysis test (Trauba et al, 1981;AACC, 2001), (e) immunological technique (Kitto, 1991;Quinn et al, 1992;Schatzki et al, 1993), (f) machine vision (Zayas and Flinn, 1998;Ridgway et al, 2001), (g) acoustic and acousto-fluidic detection methods (Shuman et al, 1993;Mankin et al, 1997;Chesmore and Nellenbach, 2001;Drzewiecki and Shuman, 2001), and (h) near-infrared (NIR) spectroscopy (Chambers and Ridgway, 1996;Chambers, 1996, 1998;Ghaedian and Wehling, 1997;Dowell et al, 1998Dowell et al, , 1999Baker et al, 1998;Ridgway et al, 1999;Cheewapramong and Wehling, 2001;Ridgway et al, 2001). Pedersen (1992) and Brader et al (2002) reviewed some of these screening methods for insect contamination in wheat.…”

mentioning

confidence: 99%

Automated Detection of Single Wheat Kernels Containing Live or Dead Insects Using Nearinfrared Reflectance Spectroscopy

Maghirang¹,

Dowell²,

Baker³

et al. 2003

Transactions of the ASAE

View full text Add to dashboard Cite

An automated near-infrared (NIR) reflectance system was used over a two-month storage period to detect single wheat kernels that contained live or dead internal rice weevils at various stages of growth. Correct classification of sound kernels plus kernels containing live pupae, large larvae, medium-sized larvae, and small larvae averaged 94%, 93%, 84%, and 63%, respectively. Pupae + large larvae calibrations were developed for live (day 1) and dead (days 7, 14, 28, 42, and 56) internal insects. Validation results showed that the live pupae +live large larvae calibration correctly classified 86% to 96% of dead pupae + dead large larvae validation samples. The dead pupae + dead large larvae calibration correctly detected the presence of live pupae + live large larvae with an accuracy of 92% to 93%. Thus, wheat kernels containing either live or dead insects can be used to develop calibrations for detecting both live and dead insects in wheat. These findings will impact how calibration sample sets can be handled. Results indicated that immediate sample processing for creating calibrations may no longer be necessary; internal insects can be killed and calibrations created at a later time without sacrificing accuracy. Additionally, laboratories can share these same calibration samples to save time and resources.

show abstract

mentioning

confidence: 99%

Automated Detection of Single Wheat Kernels Containing Live or Dead Insects Using Nearinfrared Reflectance Spectroscopy

Maghirang¹,

Dowell²,

Baker³

et al. 2003

Transactions of the ASAE

View full text Add to dashboard Cite

show abstract

“…Katsinis et al (1984) carried out the earliest ATI on marine zooplankton using image processing, and as recent as September of last year David et al (2010) correctly identified 94.5% of benthic invertebrate images by using the BugID ATI system from 9 larval stonefly taxa, even though small or damaged specimens were included in testing. Farr & Chesmore (2005) did the same for Coleoptera; and it has been done for Orthoptera (Chesmore & Nellenbach, 2001;Dietrich et al, 2003;Ohya & Chesmore 2003), cicadas (Ohya 2004) and mosquitoes (Campbell et al, 1996). Bacteria have also been used in ATI research (Walker & Kumagai, 2000;Foreroa et al, 2004).…”

Section: Use Of Automated Taxon Identification (Ati)mentioning

confidence: 99%

Taxonomy In India In The 21st Century: Call For A Digital Revival

Kumar

2012

TAPROBANICA

View full text Add to dashboard Cite

The Biodiversity of the planet is under threat due to various reasons. For the conservation and management of the bio-resources we need to precisely identify a species, so that the species can be accessed and managed accordingly. This is the very first basic step every taxonomist has to take. Though Traditional taxonomy has been used in systematics for over 250 years with the identification of more than 1.75 million species, in many countries as in India, traditional taxonomy is yet to adopt the latest molecular techniques available for a better and accurate identification of taxa. Enhanced taxonomic infrastructure and research tools can certainly allow Indian taxonomists to speed up the process of species exploration, description and classification which will enable the World scientific community to get access to India's flora and fauna dynamically without excuses of reduced funding or dearth of taxonomists for not mapping the biological diversity of India. This article overviews the latest digital advancements to revolutionize taxonomical research in India in the current century.

show abstract

“…One technique involves acoustic methods in which insects such as grasshoppers and crickets are classified using signal analysis of the sounds they make (Chesmore and Nellenbach, 2001). Another technique involves using an optical sensor to measure the wingbeat waveforms of mosquitoes (Moore, 1991) and aphids (Moore and Miller, 2002).…”

Section: Previous Work On Automated Classification Of Insectsmentioning

confidence: 99%

An Aquatic Insect Imaging System to Automate Insect Classification

Sarpola¹,

Paasch²,

Mortensen³

et al. 2008

Transactions of the ASABE

View full text Add to dashboard Cite

show abstract

Acoustic Methods for the Automated Detection and Identification of Insects

Cited by 18 publications

References 0 publications

Automated Detection of Single Wheat Kernels Containing Live or Dead Insects Using Nearinfrared Reflectance Spectroscopy

Automated Detection of Single Wheat Kernels Containing Live or Dead Insects Using Nearinfrared Reflectance Spectroscopy

Taxonomy In India In The 21st Century: Call For A Digital Revival

An Aquatic Insect Imaging System to Automate Insect Classification

Contact Info

Product

Resources

About

Acoustic Methods for the Automated Detection and Identification of Insects

Cited by 18 publications

References 0 publications

Automated Detection of Single Wheat Kernels Containing Live or Dead Insects Using Nearinfrared Reflectance Spectroscopy

Automated Detection of Single Wheat Kernels Containing Live or Dead Insects Using Nearinfrared Reflectance Spectroscopy

Taxonomy In India In The 21st Century: Call For A Digital Revival

An Aquatic Insect Imaging System to Automate Insect Classification

Contact Info

Product

Resources

About

Automated Detection of Single Wheat Kernels Containing Live or Dead Insects Using Nearinfrared Reflectance Spectroscopy

Automated Detection of Single Wheat Kernels Containing Live or Dead Insects Using Nearinfrared Reflectance Spectroscopy