Automatic X-ray quarantine scanner and pest infestation detector for agricultural products

Chuang, Chen-Lian; Ouyang, Chaoho; Lin, Ta‐Te; Yang, Man‐Miao; Yang, En‐Cheng; Huang, Tze-Wei; Kuei, Chia-Feng; Luke, Angela; Jiang, Joe‐Air

doi:10.1016/j.compag.2011.03.007

Cited by 31 publications

(11 citation statements)

References 12 publications

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“…The layout for architecture of an X-ray system developed by Chuang et al [23] used for detecting insects in different agricultural produce is shown in Figure 4. The obtained values of 96.8%, 98.6%, 97.7%, and 98.7% were for sensitivity, specificity, accuracy, and precision, respectively.…”

Section: X-ray Imagingmentioning

confidence: 99%

“…The rapid advancement in electronic technology and data analytics with greater computing power, along with their increased application in the agricultural field, have introduced new methods for non-destructive quality assessment of fruits and vegetables. A range of techniques have been reported for non-destructive detection of insect infestation such as near-infrared (NIR) spectroscopy [ 13 , 14 , 15 , 16 ], acoustic methods—sound/noise/vibration [ 17 , 18 , 19 ], imaging—visible light sensing [ 20 ], hyperspectral imaging [ 3 , 21 ], nuclear magnetic resonance [ 22 ], X-ray [ 23 , 24 ], volatile emission, and others [ 25 , 26 , 27 , 28 ]. With these new applications of technology in agricultural processing as well as the multiplicity of investigations all over the world, up-to-date reviews are needed as an orientation over technological applications in agriculture and food science.…”

Section: Introductionmentioning

confidence: 99%

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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: X-ray Imagingmentioning

confidence: 99%

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

View full text Add to dashboard Cite

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“…Individual samples can be analyzed to assess product quality, to predict maturity state, and to minimize waste. To facilitate early detection of health risks, it is crucial to apply a procedure to inspect the capability to detect foreign object inclusions [ 1 , 2 , 3 ] and contaminations [ 4 , 5 ]. This task can be performed by a human expert for an individual sample.…”

Section: Introductionmentioning

confidence: 99%

Unsupervised Foreign Object Detection Based on Dual-Energy Absorptiometry in the Food Industry

et al. 2021

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X-ray imaging is a widely used technique for non-destructive inspection of agricultural food products. One application of X-ray imaging is the autonomous, in-line detection of foreign objects in food samples. Examples of such inclusions are bone fragments in meat products, plastic and metal debris in fish, and fruit infestations. This article presents a processing methodology for unsupervised foreign object detection based on dual-energy X-ray absorptiometry (DEXA). A novel thickness correction model is introduced as a pre-processing technique for DEXA data. The aim of the model is to homogenize regions in the image that belong to the food product and to enhance contrast where the foreign object is present. In this way, the segmentation of the foreign object is more robust to noise and lack of contrast. The proposed methodology was applied to a dataset of 488 samples of meat products acquired from a conveyor belt. Approximately 60% of the samples contain foreign objects of different types and sizes, while the rest of the samples are void of foreign objects. The results show that samples without foreign objects are correctly identified in 97% of cases and that the overall accuracy of foreign object detection reaches 95%.

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“…In order to detect morphological and structural differences in plants, various techniques have been attempted, such as optical coherence tomography (OCT) [ 8 , 9 , 10 , 11 ], X-ray tomography [ 12 , 13 , 14 ], positron emission tomography (PET) [ 15 , 16 , 17 ], magnetic resonance imaging (MRI) [ 18 , 19 , 20 ], and ultrasound [ 21 , 22 , 23 ]. However, the use of these techniques has been limited to research on the structural analysis of plants.…”

Section: Introductionmentioning

confidence: 99%

Raman Hyperspectral Imaging for Detection of Watermelon Seeds Infected with Acidovorax citrulli

Lee

Kim

Qin

et al. 2017

Sensors

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The bacterial infection of seeds is one of the most important quality factors affecting yield. Conventional detection methods for bacteria-infected seeds, such as biological, serological, and molecular tests, are not feasible since they require expensive equipment, and furthermore, the testing processes are also time-consuming. In this study, we use the Raman hyperspectral imaging technique to distinguish bacteria-infected seeds from healthy seeds as a rapid, accurate, and non-destructive detection tool. We utilize Raman hyperspectral imaging data in the spectral range of 400–1800 cm−1 to determine the optimal band-ratio for the discrimination of watermelon seeds infected by the bacteria Acidovorax citrulli using ANOVA. Two bands at 1076.8 cm−1 and 437 cm−1 are selected as the optimal Raman peaks for the detection of bacteria-infected seeds. The results demonstrate that the Raman hyperspectral imaging technique has a good potential for the detection of bacteria-infected watermelon seeds and that it could form a suitable alternative to conventional methods.

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Automatic X-ray quarantine scanner and pest infestation detector for agricultural products

Cited by 31 publications

References 12 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

Unsupervised Foreign Object Detection Based on Dual-Energy Absorptiometry in the Food Industry

Raman Hyperspectral Imaging for Detection of Watermelon Seeds Infected with Acidovorax citrulli

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