Codling moth (&lt;i&gt;Cydia pomonella&lt;/i&gt;) mating disruption outcomes in apple orchards

Walker, James T.; Lo, P. L.; Horner, Rachael; Park, N.M.; Hughes, Jacqueline; Fraser, T.M.

doi:10.30843/nzpp.2013.66.5642

Cited by 7 publications

(4 citation statements)

References 9 publications

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“…If untreated, CM can result in up to a 50% loss in pre- and post-harvest apples [ 3 ]. Furthermore, production will only tolerate 1% of affected fruit [ 4 ], where if any apple infestation is found in some of the US’ top importing countries, the whole shipment is rejected [ 5 ]. Detection of infestation, therefore, is very critical but the current manual random methods are inefficient.…”

Section: Introductionmentioning

confidence: 99%

Nondestructive Detection of Codling Moth Infestation in Apples Using Pixel-Based NIR Hyperspectral Imaging with Machine Learning and Feature Selection

Ekramirad

Khaled

Doyle

et al. 2021

Foods

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Codling moth (CM) (Cydia pomonella L.), a devastating pest, creates a serious issue for apple production and marketing in apple-producing countries. Therefore, effective nondestructive early detection of external and internal defects in CM-infested apples could remarkably prevent postharvest losses and improve the quality of the final product. In this study, near-infrared (NIR) hyperspectral reflectance imaging in the wavelength range of 900–1700 nm was applied to detect CM infestation at the pixel level for three organic apple cultivars, namely Gala, Fuji and Granny Smith. An effective region of interest (ROI) acquisition procedure along with different machine learning and data processing methods were used to build robust and high accuracy classification models. Optimal wavelength selection was implemented using sequential stepwise selection methods to build multispectral imaging models for fast and effective classification purposes. The results showed that the infested and healthy samples were classified at pixel level with up to 97.4% total accuracy for validation dataset using a gradient tree boosting (GTB) ensemble classifier, among others. The feature selection algorithm obtained a maximum accuracy of 91.6% with only 22 selected wavelengths. These findings indicate the high potential of NIR hyperspectral imaging (HSI) in detecting and classifying latent CM infestation in apples of different cultivars.

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Section: Introductionmentioning

confidence: 99%

Nondestructive Detection of Codling Moth Infestation in Apples Using Pixel-Based NIR Hyperspectral Imaging with Machine Learning and Feature Selection

Ekramirad

Khaled

Doyle

et al. 2021

Foods

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“…The codling moth also causes 40%-60% crop losses in apple orchards (Hepdurgun et al, 2001). Infested apples are unmarketable, so pesticide applications are used as the main control method in many countries (Walker et al, 2013). However, broad-spectrum insecticides have caused insect resistance problems in field populations (Reyes and Ochando, 2004).…”

Section: Introductionmentioning

confidence: 99%

Genetic diversity of codling moth Cydia pomonella L. (Lepidoptera: Tortricidae) populations in Turkey

Kuyulu¹,

Genç²

2020

Turk J Zool

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The codling moth (Cydia pomonella L., Lepidoptera: Tortricidae) is a well-known pest worldwide and was first reported in 1952 in Turkey. It is traditionally controlled with several pesticide applications during each apple production season. The genetic diversity of codling moths was investigated in this study, based on the partial mitochondrial cytochrome oxidase subunite I gene. Twenty-two haplotypes from 9 populations were studied. The results showed an average haplotype and nucleotide diversity of 0.835 and 0.571, respectively. Phylogenetic analyses were indicated for 2 major clades using the maximum likelihood method. Median Joining Network analysis revealed that the 2 major clades are different from each other by at least 4 mutation steps. The 1st clade consisted of specimens from Western Turkey (Çanakkale and Balıkesir), and the 2nd clade was found in all tested populations, although a mixture of insects and improperly classified specimens were also observed. Fst value estimates ranged from 0 and 0.179, suggesting genetic differentiation among some populations. Possible situations for the genetic differentiation between the 2 major clades are also discussed.

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“…Growers adopted this programme rapidly and by 2001 the use of broad-spectrum pesticides had virtually ceased. The programme was improved further between 2007 and 2010 by increasing use of pheromone mating disruption and biological pesticides targeting lepidopteran pests Walker et al 2013). This has enabled production of fruit with ultra-low pesticide residues, which helps to maintain New Zealand's position as an exporter of premium apples to over 65 markets .…”

Section: Introductionmentioning

confidence: 99%

Risks to pest management in New Zealands pipfruit Integrated Fruit Production programme

Walker²,

Rogers³

2015

NZPP

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Pest management in New Zealand's pipfruit Integrated Fruit Production (IFP) programme relies on selective pesticides, biological control and pheromone mating disruption. The current situation is potentially precarious and one concern, the impact of less selective pesticides, was tested. Apple trees received synthetic pyrethroid (deltamethrin) sprays during the first half of two growing seasons. Beneficial and pest species were monitored monthly from November to April. Treated trees had fewer predatory bugs (91-100% reduction), lacewings (64-100%), earwigs (80-100%), predatory mites (67-100%), spiders (20-64%) and Hymenoptera (16-49%) than untreated trees. Ladybird numbers varied depending on the assessment method. Outbreaks of phytophagous mites and woolly apple aphid occurred on treated trees, but there were fewer apple leafcurling midge, San José scale and Froggatt's apple leafhopper on untreated trees. Factors that make pest management under IFP vulnerable in the future, include a loss of biological control and the consequences of new pests establishing.

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Codling moth (<i>Cydia pomonella</i>) mating disruption outcomes in apple orchards

Cited by 7 publications

References 9 publications

Nondestructive Detection of Codling Moth Infestation in Apples Using Pixel-Based NIR Hyperspectral Imaging with Machine Learning and Feature Selection

Nondestructive Detection of Codling Moth Infestation in Apples Using Pixel-Based NIR Hyperspectral Imaging with Machine Learning and Feature Selection

Genetic diversity of codling moth Cydia pomonella L. (Lepidoptera: Tortricidae) populations in Turkey

Risks to pest management in New Zealands pipfruit Integrated Fruit Production programme

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