Techniques for insect detection in stored food grains: An overview

Banga, Km. Sheetal; Kotwaliwale, Nachiket; Mohapatra, Debabandya; Giri, Saroj Kumar

doi:10.1016/j.foodcont.2018.07.008

Cited by 68 publications

(29 citation statements)

References 54 publications

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“…However, the early detection of Sitophilus zeamais in grains is very difficult to be achieved in practice because the egg, larva and pupa development occur inside the grain, which is not perceived by the human eye. Different methods have been developed to identify signs of insect infestation during storage such as staining of grains, acoustic techniques, Berlese funnel, uric acid method, grain probes and insect traps [ 6 , 7 ]; however, these methods are time-consuming and require experienced technicians, and their accuracy depends on the insect development stage and infestation level. In fact, most conventional techniques are useful only for external detection [ 7 ].…”

Section: Introductionmentioning

confidence: 99%

Convolutional Neural Networks Using Enhanced Radiographs for Real-Time Detection of Sitophilus zeamais in Maize Grain

Silva

Barroso

et al. 2021

Foods

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The application of artificial intelligence (AI) such as deep learning in the quality control of grains has the potential to assist analysts in decision making and improving procedures. Advanced technologies based on X-ray imaging provide markedly easier ways to control insect infestation of stored products, regardless of whether the quality features are visible on the surface of the grains. Here, we applied contrast enhancement algorithms based on peripheral equalization and calcification emphasis on X-ray images to improve the detection of Sitophilus zeamais in maize grains. In addition, we proposed an approach based on convolutional neural networks (CNNs) to identity non-infested and infested classes using three different architectures; (i) Inception-ResNet-v2, (ii) Xception and (iii) MobileNetV2. In general, the prediction models developed based on the MobileNetV2 and Xception architectures achieved higher accuracy (≥0.88) in identifying non-infested grains and grains infested by maize weevil, with a correct classification from 0.78 to 1.00 for validation and test sets. Hence, the proposed approach using enhanced radiographs has the potential to provide precise control of Sitophilus zeamais for safe human consumption of maize grains. The proposed method can automatically recognize food contaminated with hidden storage pests without manual features, which makes it more reliable for grain inspection.

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Section: Introductionmentioning

confidence: 99%

Convolutional Neural Networks Using Enhanced Radiographs for Real-Time Detection of Sitophilus zeamais in Maize Grain

Silva

Barroso

et al. 2021

Foods

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“…Acoustic technology has a long history of supporting insect pest managers with decision tools and providing contextual information on insect life history, feeding, and movement [ 1 , 2 , 3 , 4 , 5 , 6 , 7 , 8 , 9 ]. Similar technology is being applied in other areas of agriculture, such as the analysis of cattle behaviors [ 10 , 11 ].…”

Section: Introductionmentioning

confidence: 99%

Automated Applications of Acoustics for Stored Product Insect Detection, Monitoring, and Management

Mankin

Hagstrum

Guo

et al. 2021

Insects

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Acoustic technology provides information difficult to obtain about stored insect behavior, physiology, abundance, and distribution. For example, acoustic detection of immature insects feeding hidden within grain is helpful for accurate monitoring because they can be more abundant than adults and be present in samples without adults. Modern engineering and acoustics have been incorporated into decision support systems for stored product insect management, but with somewhat limited use due to device costs and the skills needed to interpret the data collected. However, inexpensive modern tools may facilitate further incorporation of acoustic technology into the mainstream of pest management and precision agriculture. One such system was tested herein to describe Sitophilus oryzae (Coleoptera: Curculionidae) adult and larval movement and feeding in stored grain. Development of improved methods to identify sounds of targeted pest insects, distinguishing them from each other and from background noise, is an active area of current research. The most powerful of the new methods may be machine learning. The methods have different strengths and weaknesses depending on the types of background noise and the signal characteristic of target insect sounds. It is likely that they will facilitate automation of detection and decrease costs of managing stored product insects in the future.

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“…Insect secretions are often excreted as uric acid in order to prevent significant loss of water in the body. As an end product of insect nitrogen metabolism, uric acid has been suggested as an indicator of insect contamination on seeds and grain products [ 22 , 23 ]. The current methods for uric acid determination mainly include high performance liquid chromatography, the uric acid enzyme method, and the phosphotungstic acid reduction method [ 24 ].…”

Section: Introductionmentioning

confidence: 99%

An Analytical Method Based on Electrochemical Sensor for the Assessment of Insect Infestation in Flour

Zhu

Karimi-Maleh

2021

Biosensors

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Uric acid is an important indicator of the insect infestation assessment in flour. In this work, we propose a method for uric acid detection based on voltammetry. This technique is particularly considered for the physicochemical properties of flour and contains a simple pretreatment process to rapidly achieve extraction and adsorption of uric acid in flour. To achieve specific recognition of uric acid, graphene and poly(3,4-ethylenedioxythiophene) (PEDOT) were used for the adsorption and concentration of uric acid in flour. The adsorbed mixture was immobilized on the surface of a screen-printed electrode for highly sensitive detection of the uric acid. The results showed that electrocatalytic oxidation of uric acid could be achieved after adsorption by graphene and PEDOT. This electrocatalytic reaction allows its oxidation peak to be distinguished from those of other substances that commonly possess electrochemical activity. This voltammetry-based detection method is a portable and disposable analytical method. Because it is simple to operate, requires no professional training, and is inexpensive, it is a field analysis method that can be promoted.

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Techniques for insect detection in stored food grains: An overview

Cited by 68 publications

References 54 publications

Convolutional Neural Networks Using Enhanced Radiographs for Real-Time Detection of Sitophilus zeamais in Maize Grain

Convolutional Neural Networks Using Enhanced Radiographs for Real-Time Detection of Sitophilus zeamais in Maize Grain

Automated Applications of Acoustics for Stored Product Insect Detection, Monitoring, and Management

An Analytical Method Based on Electrochemical Sensor for the Assessment of Insect Infestation in Flour

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