&amp;lt;i&amp;gt;Low Frequency Signal Patterns for Codling Moth Larvae Activity in Apples&amp;lt;/i&amp;gt;

Ekramirad, Nader; Parrish, Chadwick A.; Villanueva, Raul T.; Donohue, Kevin D.; Adedeji, Akinbode A.

doi:10.13031/aim.202001028

Cited by 4 publications

(3 citation statements)

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“…In a recent study, Li et al [17] reported that AE detected codling moth activities in infested apples and they obtained a very high classification rate (83%) utilizing 0.5 s of acoustic signal collection. Also, an attempt was made by Ekramirad et al [105] to authenticate the specific source and signature of acoustic emission in codling moth-infested apples through correlating visually observed larvae activities, such as chewing and locomotion, with patterns in the synchronized signals from the contact Lead zirconate titanate (PZT) sensors. For this objective, they video recorded the larval behavior while the acoustic signals were being collected at the same time.…”

Section: Acoustic Techniques For Insect Infestation Detectionmentioning

confidence: 99%

Non-Destructive Technologies for Detecting Insect Infestation in Fruits and Vegetables under Postharvest Conditions: A Critical Review

Adedeji

Ekramirad

Rady

et al. 2020

Foods

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In the last two decades, food scientists have attempted to develop new technologies that can improve the detection of insect infestation in fruits and vegetables under postharvest conditions using a multitude of non-destructive technologies. While consumers’ expectations for higher nutritive and sensorial value of fresh produce has increased over time, they have also become more critical on using insecticides or synthetic chemicals to preserve food quality from insects’ attacks or enhance the quality attributes of minimally processed fresh produce. In addition, the increasingly stringent quarantine measures by regulatory agencies for commercial import–export of fresh produce needs more reliable technologies for quickly detecting insect infestation in fruits and vegetables before their commercialization. For these reasons, the food industry investigates alternative and non-destructive means to improve food quality. Several studies have been conducted on the development of rapid, accurate, and reliable insect infestation monitoring systems to replace invasive and subjective methods that are often inefficient. There are still major limitations to the effective in-field, as well as postharvest on-line, monitoring applications. This review presents a general overview of current non-destructive techniques for the detection of insect damage in fruits and vegetables and discusses basic principles and applications. The paper also elaborates on the specific post-harvest fruit infestation detection methods, which include principles, protocols, specific application examples, merits, and limitations. The methods reviewed include those based on spectroscopy, imaging, acoustic sensing, and chemical interactions, with greater emphasis on the noninvasive methods. This review also discusses the current research gaps as well as the future research directions for non-destructive methods’ application in the detection and classification of insect infestation in fruits and vegetables.

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Section: Acoustic Techniques For Insect Infestation Detectionmentioning

confidence: 99%

Non-Destructive Technologies for Detecting Insect Infestation in Fruits and Vegetables under Postharvest Conditions: A Critical Review

Adedeji

Ekramirad

Rady

et al. 2020

Foods

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“…Using forms of nondestructive testing to assess certain qualities of apples is not new. Some of the nondestructive techniques used on apples include hyperspectral imaging (HSI) [ 9 , 10 ], vibro-acoustic signaling [ 11 , 12 ], ultrasonic acoustic detection [ 13 ], delta absorbance meter [ 14 ], machine vision [ 15 , 16 ], and spectroscopy [ 10 , 17 ]. Each technique has its advantages and disadvantages; however, the one that stands out is HSI.…”

Section: Introductionmentioning

confidence: 99%

“…While some of these techniques have been used to detect and classify infested apples, such as the work done with acoustic emissions [ 18 ] and vibro-acoustic signaling [ 12 ], HSI is ideal because it conveys additional useful information for nondestructive applications. HSI combines the capability of spectroscopy and machine vision techniques.…”

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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Acoustic Emission and Near-Infra Red Imaging Methods for Nondestructive Apple Quality Detection and Classification

Adedeji

Ekramirad

Khaled

et al. 2022

Nondestructive Quality Assessment Techniques for Fresh Fruits and Vegetables

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<i>Low Frequency Signal Patterns for Codling Moth Larvae Activity in Apples</i>

Cited by 4 publications

References 0 publications

Non-Destructive Technologies for Detecting Insect Infestation in Fruits and Vegetables under Postharvest Conditions: A Critical Review

Non-Destructive Technologies for Detecting Insect Infestation in Fruits and Vegetables under Postharvest Conditions: A Critical Review

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

Acoustic Emission and Near-Infra Red Imaging Methods for Nondestructive Apple Quality Detection and Classification

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