Hyperspectral imaging for detection of codling moth infestation in GoldRush apples

Rady, Ahmed; Ekramirad, Nader; Adedeji, Akinbode A.; Li, M.; Alimardani, Reza

doi:10.1016/j.postharvbio.2017.03.007

Cited by 57 publications

(35 citation statements)

References 21 publications

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“…Early-stage CM-infested apple detection was not a problem in our study because five-day larvae infestation was used. Another study using hyperspectral imaging and multivariate analysis achieved 86% classification (Rady et al, 2017). However, for both x-ray and hyperspectral imaging, practical application would be difficult because of the implementation costs and sensitivity of the methods due to the large amount of image data.…”

Section: Resultsmentioning

confidence: 99%

“…This is coupled with the fact that not all of the apples are inspected. Nondestructive methods have been described for detecting defects and quality attributes in fruits and vegetables, such as x-ray imaging for apple defect detection (Schatzki et al, 1997), hyperspectral imaging for CM infestation detection (Rady et al, 2017), and magnetic resonance imaging for prediction of tomato quality attributes and mechanical damage (Milczarek et al, 2009;Zhang and McCarthy, 2012). Each of these methods has limitations, such as the inability to detect changes beyond the surface reflectance of a thick object (such as an apple) with hyperspectral imaging, and the response time of thermal imaging, which may not be practical for online applications.…”

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confidence: 99%

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Application of Acoustic Emission and Machine Learning to Detect Codling Moth Infested Apples

Mengxing¹,

Ekramirad²,

Rady³

et al. 2018

Transactions of the ASABE

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Abstract. Incidence of codling moth (CM) ( L.) infestation in apples has been a major concern in North America for decades. CM larvae bore deep into the fruit, making it unmarketable. An effective noninvasive method to detect larvae-infested apples is necessary to ensure that apples are CM-free in post-harvest processing. In this study, a novel approach using an acoustic emission (AE) system and subsequent machine learning methods was applied to classify larvae-infested apples from intact apples. ‘GoldRush’ apples were infested with CM neonates and stored at the same conditions as intact apples. The AE system was used to collect the data emitted by 80 larvae-infested and intact apples in total. Eleven AE features that changed with signaling time were obtained with the AE system. For each feature, the area under the curve along the signaling time was calculated and used as an independent input variable for the machine learning algorithms, which included linear discriminant analysis (LDA) and ensemble method adaptive boosting. With signaling times ranging from 0.5 to 120 s, classification rates for infested versus intact apples ranged from 91% to 100% for the training set and from 83% to 100% for the test set. The quick signal collection and high classification accuracy obtained in this study show the potential of AE for detecting and classifying CM-infested apples. Keywords: Acoustic emission, Apple, Codling moth, Machine learning, Pest infestation.

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Section: Resultsmentioning

confidence: 99%

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confidence: 99%

Application of Acoustic Emission and Machine Learning to Detect Codling Moth Infested Apples

Mengxing¹,

Ekramirad²,

Rady³

et al. 2018

Transactions of the ASABE

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“…Codling moth infestation of apples under storage conditions of different temperatures (4, 10, 17, and 27°C) was effectively classified based on decision trees at 5 most influential wavelengths (434, 438, 538, 583, and 915 nm) that were determined by the sequential forward selection (SFS) method. The highest classification accuracy of 82% was obtained for insect‐infested apples (Rady and others ). The multispectral imaging technique has also been widely investigated to detect various types of defects (such as insect damage, bruising, decay, cold injury, black heart, puncture injury, and cracks) on various plant foods (such as peach, radish, sunflower seed, citrus, and jujube) (Ma and others ; Zhang and others ; Folch‐Fortuny and others ; Li and others , ; Liu and others ; Pan and others ; Song and others ; Wu and others ).…”

Section: Determination Of Quality Parameters Of Plant Foodsmentioning

confidence: 98%

Multispectral Imaging for Plant Food Quality Analysis and Visualization

Sun

2018

Comp Rev Food Sci Food Safe

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The multispectral imaging technique is considered a reformation of hyperspectral imaging. It can be employed to noninvasively and rapidly evaluate food quality. Even though several imaging or sensor‐based techniques have been conducted for the quality assessment of various food products, the rise of multispectral imaging has been more promising. This paper presents a comprehensive review of the use of the multispectral sensor in the quality assessment of plant foods (such as cereals, legumes, tubers, fruits, and vegetables). Different quality parameters (such as physicochemical and microbiological aspects) of plant‐based foods that were determined and visualized by the combination of modeling methods and feature wavelength selection approaches are summarized. Based on the literature, the most frequently used wavelength selection methods are the successive projection algorithm (SPA) and the regression coefficient (RC). The most effective models developed for analyzing plant food products are the partial least squares regression (PLSR), least square support vector machine (LS‐SVM), support vector machine (SVM), partial least squares discriminant analysis (PLSDA), and multiple linear regression (MLR). This article concludes with a discussion of challenges, potential uses, and future trends of this flourishing technique that is now also being applied to plant foods.

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“…The larval phase is its most devastating phase when it feeds on the flesh and pulp of fruits it was laid on. When the point of entry is the calyx, the damage is difficult to detect with the subjective method of assessment common in most apple processing plants and this is why non-destructive detection becomes important [9,10]. Early detection when eggs are laid on the surface of the produce is also very important.…”

Section: Introductionmentioning

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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Hyperspectral imaging for detection of codling moth infestation in GoldRush apples

Cited by 57 publications

References 21 publications

Application of Acoustic Emission and Machine Learning to Detect Codling Moth Infested Apples

Application of Acoustic Emission and Machine Learning to Detect Codling Moth Infested Apples

Multispectral Imaging for Plant Food Quality Analysis and Visualization

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

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