Pixel-level aflatoxin detecting in maize based on feature selection and hyperspectral imaging

Gao, Jiyue; Ni, Jiangong; Wang, Dawei; Deng, Limiao; Li, Juan; Han, Zhongzhi

doi:10.1016/j.saa.2020.118269

Cited by 22 publications

(10 citation statements)

References 18 publications

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“…It will take more time to determine if a pixel is aflatoxincontaminated or not. [29,30] International Journal of Advances in Scientific Research and Engineering (ijasre), Vol 8 (2), February -2022…”

Section: Aflatoxin Detection Methodsmentioning

confidence: 99%

Development of Optical-based and Imaging Technology Detection, Diagnosis and Prevention of Aflatoxin Contamination on Maize Crop

Rashid¹,

Kisangiri²,

Mbega³

2022

IJASRE

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Aflatoxins are mycotoxins produced naturally by some mold strains, such as Aspergillus flavus and Aspergillus parasiticus. Weather factors, notably moisture levels of more than 18 percent and temperatures of more than 32°C, influence the proliferation of aflatoxin-producing fungus. Aflatoxin pollution is a danger to human and animal healthiness. As a result, there is a pressing need to control aflatoxin, particularly in crops like maize, in countries where aflatoxin susceptibility is high, such as Tanzania. Several aflatoxin management solutions are being developed, however, for them to be effective, accurate identification of fungus or aflatoxins contamination on grain surfaces is required. While the majority of the known solutions include growing and identifying the fungus responsible or using Higher-Performance Liquid Chromatography (HPCL), Thin Layer Chromatography (TLC), and Enzyme-Linked Immunosorbent Assays (ELISA) to detect aflatoxin early, this review underlines the prospect of employing computer-based techniques to detect aflatoxin early. Because of these options, producers will be able to quickly detect aflatoxin contamination and, as a result, make informed decisions about how to prevent and/or reduce aflatoxin contamination, hence enhancing maize quality and storability.

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Section: Aflatoxin Detection Methodsmentioning

confidence: 99%

Development of Optical-based and Imaging Technology Detection, Diagnosis and Prevention of Aflatoxin Contamination on Maize Crop

Rashid¹,

Kisangiri²,

Mbega³

2022

IJASRE

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“…(2017) Classification of coffee species 900–1700 Standard Normal Variate; 1st derivative; mean centering Image thresholding 5 73:27 MATLAB 7.0 100% Calvini et al. (2015) Detection of aflatoxin in maize 400–1000 Multiplicative signal correction (MSC) Image thresholding – 82:18 MATLAB R2018b 99% Gao et al. (2020) Detection of pesticide residue on spinach leaves 900–1700 Multiplicative signal correction (MSC) Image thresholding – 80:20 MATLAB R2016b; PYTHON 3.6 99% Zhan-qi et al.…”

Section: Machine Learning Techniquesmentioning

confidence: 99%

Machine learning techniques for analysis of hyperspectral images to determine quality of food products: A review

Saha

Manickavasagan

2021

Current Research in Food Science

265

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Non-destructive testing techniques have gained importance in monitoring food quality over the years. Hyperspectral imaging is one of the important non-destructive quality testing techniques which provides both spatial and spectral information. Advancement in machine learning techniques for rapid analysis with higher classification accuracy have improved the potential of using this technique for food applications. This paper provides an overview of the application of different machine learning techniques in analysis of hyperspectral images for determination of food quality. It covers the principle underlying hyperspectral imaging, the advantages, and the limitations of each machine learning technique. The machine learning techniques exhibited rapid analysis of hyperspectral images of food products with high accuracy thereby enabling robust classification or regression models. The selection of effective wavelengths from the hyperspectral data is of paramount importance since it greatly reduces the computational load and time which enhances the scope for real time applications. Due to the feature learning nature of deep learning, it is one of the most promising and powerful techniques for real time applications. However, the field of deep learning is relatively new and need further research for its full utilization. Similarly, lifelong machine learning paves the way for real time HSI applications but needs further research to incorporate the seasonal variations in food quality. Further, the research gaps in machine learning techniques for hyperspectral image analysis, and the prospects are discussed.

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“…Fortunately, significant progress has been made in hyperspectral imaging in the past thirty years. As a non-contact detection method, it has attracted intensive interest in food quality evaluation, item safety evaluation, vegetation detection, precision agriculture, and medical diagnosis [17][18][19][20][21][22][23][24][25][26][27]. For example, Xin Zhao, et al [17] utilized near-infrared hyperspectral imaging to detect low-level peanut powder contamination of whole wheat flour, with the peanut powder concentrations as low as 0.3% in spring wheat flour and as low as 0.5% in winter wheat flour.…”

Section: Introductionmentioning

confidence: 99%

“…Na Wu, et al [24] proposed an effective method for RFS detection based on microscopic molecular detection technology with macroscopic spectral imaging technology, detected rice kernels with different varieties, different infection conditions and different infection status, and achieved high infection degrees as 99.33% on calibration set and 99.20% on prediction set. Jiyue Gao, et al [27] detected pixel-level aflatoxin in maize based on feature selection and hyperspectral imaging, and used the feature selection method to classify the aflatoxin-contaminated corns and reached high accuracy of 99.38%.…”

Section: Introductionmentioning

confidence: 99%

Identification of Typical Solid Hazardous Chemicals Based on Hyperspectral Imaging

et al. 2021

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The identification of hazardous chemicals based on hyperspectral imaging is an important emergent means for the prevention of explosion accidents and the early warning of secondary hazards. In this study, we used a combination of spectral curve matching based on full-waveform characteristics and spectral matching based on spectral characteristics to identify the hazardous chemicals, and proposed a method to quantitatively characterize the matching degree of the spectral curves of hazardous chemicals. The results showed that the four hazardous chemicals, sulfur, red phosphorus, potassium permanganate, and corn starch had bright colors, distinct spectral curve characteristics, and obvious changes in reflectivity, which were easy to identify. Moreover, the matching degree of their spectral curves was positively correlated with their reflectivity. However, the spectral characteristics of carbon powder, strontium nitrate, wheat starch, and magnesium–aluminum alloy powder were not obvious, with no obvious characteristic peaks or trends of change in reflectivity. Except for the reflectivity and the matching degree of the carbon powder being maintained at a low level, the reflectivity of the remaining three samples was relatively close, so that it was difficult to identify with the spectral curves alone, and color information should be considered for further identification.

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Pixel-level aflatoxin detecting in maize based on feature selection and hyperspectral imaging

Cited by 22 publications

References 18 publications

Development of Optical-based and Imaging Technology Detection, Diagnosis and Prevention of Aflatoxin Contamination on Maize Crop

Development of Optical-based and Imaging Technology Detection, Diagnosis and Prevention of Aflatoxin Contamination on Maize Crop

Machine learning techniques for analysis of hyperspectral images to determine quality of food products: A review

Identification of Typical Solid Hazardous Chemicals Based on Hyperspectral Imaging

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