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Yang D, Zhou Y, Jie Y, Li Q, Shi T. Non-destructive detection of defective maize kernels using hyperspectral imaging and convolutional neural network with attention module. SPECTROCHIMICA ACTA. PART A, MOLECULAR AND BIOMOLECULAR SPECTROSCOPY 2024; 313:124166. [PMID: 38493512 DOI: 10.1016/j.saa.2024.124166] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/08/2023] [Revised: 03/04/2024] [Accepted: 03/14/2024] [Indexed: 03/19/2024]
Abstract
Rapid, effective and non-destructive detection of the defective maize kernels is crucial for their high-quality storage in granary. Hyperspectral imaging (HSI) coupled with convolutional neural network (CNN) based on spectral and spatial attention (Spl-Spal-At) module was proposed for identifying the different types of maize kernels. The HSI data within 380-1000 nm of six classes of sprouted, heat-damaged, insect-damaged, moldy, broken and healthy kernels was collected. The CNN-Spl-At, CNN-Spal-At and CNN-Spl-Spal-At models were established based on the spectra, images and their fusion features as inputs for the recognition of different kernels. Further compared the performances of proposed models and conventional models were built by support vector machine (SVM) and extreme learning machine (ELM). The results indicated that the recognition ability of CNN with attention series models was significantly better than that of SVM and ELM models and fused features were more conducive to expressing the appearance of different kernels than single features. And the CNN-Spl-Spal-At model had an optimal recognition result with high average classification accuracy of 98.04 % and 94.56 % for the training and testing sets, respectively. The recognition results were visually presented on the surface image of kernels with different colors. The CNN-Spl-Spal-At model was built in this study could effectively detect defective maize kernels, and it also had great potential to provide the analysis approaches for the development of non-destructive testing equipment based on HSI technique for maize quality.
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Affiliation(s)
- Dong Yang
- Academy of National Food and Strategic Reserves Administration, Beijing 100037, China; National Engineering Research Center of Grain Storage and Logistics, Beijing 100037, China
| | - Yuxing Zhou
- Academy of National Food and Strategic Reserves Administration, Beijing 100037, China; National Engineering Research Center of Grain Storage and Logistics, Beijing 100037, China
| | - Yu Jie
- Academy of National Food and Strategic Reserves Administration, Beijing 100037, China; National Engineering Research Center of Grain Storage and Logistics, Beijing 100037, China
| | - Qianqian Li
- Academy of National Food and Strategic Reserves Administration, Beijing 100037, China; National Engineering Research Center of Grain Storage and Logistics, Beijing 100037, China
| | - Tianyu Shi
- Academy of National Food and Strategic Reserves Administration, Beijing 100037, China; National Engineering Research Center of Grain Storage and Logistics, Beijing 100037, China.
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Abi-Rizk H, Jouan-Rimbaud Bouveresse D, Chamberland J, Cordella CBY. Recent developments of e-sensing devices coupled to data processing techniques in food quality evaluation: a critical review. ANALYTICAL METHODS : ADVANCING METHODS AND APPLICATIONS 2023; 15:5410-5440. [PMID: 37818969 DOI: 10.1039/d3ay01132a] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/13/2023]
Abstract
A greater demand for high-quality food is being driven by the growth of economic and technological advancements. In this context, consumers are currently paying special attention to organoleptic characteristics such as smell, taste, and appearance. Motivated to mimic human senses, scientists developed electronic devices such as e-noses, e-tongues, and e-eyes, to spot signals relative to different chemical substances prevalent in food systems. To interpret the information provided by the sensors' responses, multiple chemometric approaches are used depending on the aim of the study. This review based on the Web of Science database, endeavored to scrutinize three e-sensing systems coupled to chemometric approaches for food quality evaluation. A total of 122 eligible articles pertaining to the e-nose, e-tongue and e-eye devices were selected to conduct this review. Most of the performed studies used exploratory analysis based on linear factorial methods, while classification and regression techniques came in the second position. Although their applications have been less common in food science, it is to be noted that nonlinear approaches based on artificial intelligence and machine learning deployed in a big-data context have generally yielded better results for classification and regression purposes, providing new perspectives for future studies.
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Affiliation(s)
- Hala Abi-Rizk
- LAboratoire de Recherche et de Traitement de l'Information Chimiosensorielle - LARTIC, Institute of Nutrition and Functional Foods (INAF), Université Laval, Québec, QC, G1V 0A6, Canada.
| | | | - Julien Chamberland
- Department of Food Sciences, STELA Dairy Research Center, Institute of Nutrition and Functional Foods (INAF), Université Laval, Québec, QC, G1V 0A6, Canada
| | - Christophe B Y Cordella
- LAboratoire de Recherche et de Traitement de l'Information Chimiosensorielle - LARTIC, Institute of Nutrition and Functional Foods (INAF), Université Laval, Québec, QC, G1V 0A6, Canada.
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Qu M, Tian S, Yu H, Liu D, Zhang C, He Y, Cheng F. Single-kernel classification of deoxynivalenol and zearalenone contaminated maize based on visible light imaging under ultraviolet light excitation combined with polarized light imaging. Food Control 2022. [DOI: 10.1016/j.foodcont.2022.109354] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/14/2022]
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4
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Matese A, Di Gennaro SF, Orlandi G, Gatti M, Poni S. Assessing Grapevine Biophysical Parameters From Unmanned Aerial Vehicles Hyperspectral Imagery. FRONTIERS IN PLANT SCIENCE 2022; 13:898722. [PMID: 35769294 PMCID: PMC9235871 DOI: 10.3389/fpls.2022.898722] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/17/2022] [Accepted: 05/12/2022] [Indexed: 05/04/2023]
Abstract
Over the last 50 years, many approaches for extracting plant key parameters from remotely sensed data have been developed, especially in the last decade with the spread of unmanned aerial vehicles (UAVs) in agriculture. Multispectral sensors are very useful for the elaboration of common vegetation indices (VIs), however, the spectral accuracy and range may not be enough. In this scenario, hyperspectral (HS) technologies are gaining particular attention thanks to the highest spectral resolution, which allows deep characterization of vegetative/soil response. Literature presents few papers encompassing UAV-based HS applications in vineyard, a challenging conditions respect to other crops due to high presence of bare soil, grass cover, shadows and high heterogeneity canopy structure with different leaf inclination. The purpose of this paper is to present the first contribution combining traditional and multivariate HS data elaboration techniques, supported by strong ground truthing of vine ecophysiological, vegetative and productive variables. Firstly the research describes the UAV image acquisition and processing workflow to generate a 50 bands HS orthomosaic of a study vineyard. Subsequently, the spectral data extracted from 60 sample vines were elaborated both investigating the relationship between traditional narrowband VIs and grapevine traits. Then, multivariate calibration models were built using a double approach based on Partial Least Square (PLS) regression and interval-PLS (iPLS), to evaluate the correlation performance between the biophysical parameters and HS imagery using the whole spectral range and a selection of more relevant bands applying a variable selection algorithm, respectively. All techniques (VIs, PLS and iPLS) provided satisfactory correlation performances for the ecophysiological (R 2 = 0.65), productive (R 2 = 0.48), and qualitative (R 2 = 0.63) grape parameters. The novelty of this work is represented by the first assessment of a UAV HS dataset with the expression of the entire vine ecosystem, from the physiological and vegetative state to grapes production and quality, using narrowband VIs and multivariate PLS regressions. A correct non-destructive estimation of key parameters in vineyard, above all physiological parameters which must be measured in a short time as they are extremely influenced by the variability of environmental conditions during the day, represents a powerful tool to support the winegrower in vineyard management.
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Affiliation(s)
- Alessandro Matese
- Institute of BioEconomy, National Research Council (CNR-IBE), Firenze, Italy
| | | | - Giorgia Orlandi
- Institute of BioEconomy, National Research Council (CNR-IBE), Firenze, Italy
| | - Matteo Gatti
- Department of Sustainable Crop Production (DI.PRO.VE.S.), Università Cattolica del Sacro Cuore, Piacenza, Italy
| | - Stefano Poni
- Department of Sustainable Crop Production (DI.PRO.VE.S.), Università Cattolica del Sacro Cuore, Piacenza, Italy
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An eco-friendly analytical methodology based on digital images for quality control of commercial Mikania glomerata syrups. Microchem J 2022. [DOI: 10.1016/j.microc.2022.107338] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/21/2022]
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ZHU Y, MA Z, HAN M, LI Y, XING L, LU E, GAO H. Quantitative damage detection of direct maize kernel harvest based on image processing and BP neural network. FOOD SCIENCE AND TECHNOLOGY 2022. [DOI: 10.1590/fst.54322] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/22/2022]
Affiliation(s)
- Yongle ZHU
- Ministry of Education & Jiangsu Province, China; Jiangsu University, China
| | - Zheng MA
- Ministry of Education & Jiangsu Province, China; Jiangsu University, China
| | - Min HAN
- Ministry of Education & Jiangsu Province, China; Jiangsu University, China
| | - Yaoming LI
- Ministry of Education & Jiangsu Province, China; Jiangsu University, China
| | - Licheng XING
- Jiangsu World Agricultural Machinery Co., Ltd., China
| | - En LU
- Ministry of Education & Jiangsu Province, China; Jiangsu University, China
| | - Hongyan GAO
- Ministry of Education & Jiangsu Province, China; Jiangsu University, China
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Meenu M, Kurade C, Neelapu BC, Kalra S, Ramaswamy HS, Yu Y. A concise review on food quality assessment using digital image processing. Trends Food Sci Technol 2021. [DOI: 10.1016/j.tifs.2021.09.014] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/29/2022]
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Mishra G, Panda BK, Ramirez WA, Jung H, Singh CB, Lee SH, Lee I. Research advancements in optical imaging and spectroscopic techniques for nondestructive detection of mold infection and mycotoxins in cereal grains and nuts. Compr Rev Food Sci Food Saf 2021; 20:4612-4651. [PMID: 34338431 DOI: 10.1111/1541-4337.12801] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/04/2020] [Revised: 06/07/2021] [Accepted: 06/15/2021] [Indexed: 12/01/2022]
Abstract
Cereal grains and nuts are represented as the economic backbone of many developed and developing countries. Kernels of cereal grains and nuts are prone to mold infection under high relative humidity and suitable temperature conditions in the field as well as storage conditions. Health risks caused by molds and their molecular metabolite mycotoxins are, therefore, important topics to investigate. Strict regulations have been developed by international trade regulatory bodies for the detection of mold growth and mycotoxin contamination across the food chain starting from the harvest to storage and consumption. Molds and aflatoxins are not evenly distributed over the bulk of grains, thus appropriate sampling for detection and quantification is crucial. Existing reference methods for mold and mycotoxin detection are destructive in nature as well as involve skilled labor and hazardous chemicals. Also, these methods cannot be used for inline sorting of the infected kernels. Thus, analytical methods have been extensively researched to develop the one that is more practical to be used in commercial detection and sorting processes. Among various analytical techniques, optical imaging and spectroscopic techniques are attracting growers' attention for their potential of nondestructive and rapid inline identification and quantification of molds and mycotoxins in various food products. This review summarizes the recent application of rapid and nondestructive optical imaging and spectroscopic techniques, including digital color imaging, X-ray imaging, near-infrared spectroscopy, fluorescent, multispectral, and hyperspectral imaging. Advance chemometric techniques to identify very low-level mold growth and mycotoxin contamination are also discussed. Benefits, limitations, and challenges of deploying these techniques in practice are also presented in this paper.
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Affiliation(s)
- Gayatri Mishra
- UniSA STEM, University of South Australia, Mawson Lakes, South Australia, Australia
| | - Brajesh Kumar Panda
- UniSA STEM, University of South Australia, Mawson Lakes, South Australia, Australia
| | - Wilmer Ariza Ramirez
- UniSA STEM, University of South Australia, Mawson Lakes, South Australia, Australia
| | - Hyewon Jung
- UniSA STEM, University of South Australia, Mawson Lakes, South Australia, Australia
| | - Chandra B Singh
- UniSA STEM, University of South Australia, Mawson Lakes, South Australia, Australia.,Centre for Applied Research, Innovation and Entrepreneurship, Lethbridge College, Lethbridge, Alberta, Canada
| | - Sang-Heon Lee
- UniSA STEM, University of South Australia, Mawson Lakes, South Australia, Australia
| | - Ivan Lee
- UniSA STEM, University of South Australia, Mawson Lakes, South Australia, Australia
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Calvini R, Luciano A, Ottoboni M, Ulrici A, Tretola M, Pinotti L. Multivariate image analysis for the rapid detection of residues from packaging remnants in former foodstuff products (FFPs) – a feasibility study. Food Addit Contam Part A Chem Anal Control Expo Risk Assess 2020; 37:1399-1411. [DOI: 10.1080/19440049.2020.1769195] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/24/2022]
Affiliation(s)
- Rosalba Calvini
- Department of Life Sciences and Interdepartmental Centre BIOGEST-SITEIA, University of Modena and Reggio Emilia, Reggio Emilia, Italy
| | - Alice Luciano
- Department of Health, Animal Science and Food Safety, VESPA, University of Milan, Milano, Italy
| | - Matteo Ottoboni
- Department of Health, Animal Science and Food Safety, VESPA, University of Milan, Milano, Italy
| | - Alessandro Ulrici
- Department of Life Sciences and Interdepartmental Centre BIOGEST-SITEIA, University of Modena and Reggio Emilia, Reggio Emilia, Italy
| | - Marco Tretola
- Department of Health, Animal Science and Food Safety, VESPA, University of Milan, Milano, Italy
- Agroscope, Institute for Livestock Sciences, Posieux, Switzerland
| | - Luciano Pinotti
- Department of Health, Animal Science and Food Safety, VESPA, University of Milan, Milano, Italy
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Wu D, Cai Z, Han J, Qin H. Automatic kernel counting on maize ear using RGB images. PLANT METHODS 2020; 16:79. [PMID: 32518581 PMCID: PMC7268725 DOI: 10.1186/s13007-020-00619-z] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 10/07/2019] [Accepted: 05/20/2020] [Indexed: 06/11/2023]
Abstract
BACKGROUND The number of kernels per ear is one of the major agronomic yield indicators for maize. Manual assessment of kernel traits can be time consuming and laborious. Moreover, manually acquired data can be influenced by subjective bias of the observer. Existing methods for counting of kernel number are often unstable and costly. Machine vision technology allows objective extraction of features from image sensor data, offering high-throughput and low-cost advantages. RESULTS Here, we propose an automatic kernel recognition method which has been applied to count the kernel number based on digital colour photos of the maize ears. Images were acquired under both LED diffuse (indoors) and natural light (outdoor) conditions. Field trials were carried out at two sites in China using 8 maize varieties. This method comprises five steps: (1) a Gaussian Pyramid for image compression to improve the processing efficiency, (2) separating the maize fruit from the background by Mean Shift Filtering algorithm, (3) a Colour Deconvolution (CD) algorithm to enhance the kernel edges, (4) segmentation of kernel zones using a local adaptive threshold, (5) an improved Find-Local-Maxima to recognize the local grayscale peaks and determine the maize kernel number within the image. The results showed good agreement (> 93%) in terms of accuracy and precision between ground truth (manual counting) and the image-based counting. CONCLUSIONS The proposed algorithm has robust and superior performance in maize ear kernel counting under various illumination conditions. In addition, the approach is highly-efficient and low-cost. The performance of this method makes it applicable and satisfactory for real-world breeding programs.
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Affiliation(s)
- Di Wu
- Institute of Mechanical Engineering, Hangzhou Dianzi University, Hangzhou, 310018 Zhejiang People’s Republic of China
| | - Zhen Cai
- Ocean College, Zhejiang University, Zhoushan, 316021 Zhejiang People’s Republic of China
| | - Jiwan Han
- School of Software, Shanxi Agricultural University, Taigu, 030801 Shanxi People’s Republic of China
| | - Huawei Qin
- Institute of Mechanical Engineering, Hangzhou Dianzi University, Hangzhou, 310018 Zhejiang People’s Republic of China
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Exploring the potential of NIR hyperspectral imaging for automated quantification of rind amount in grated Parmigiano Reggiano cheese. Food Control 2020. [DOI: 10.1016/j.foodcont.2020.107111] [Citation(s) in RCA: 14] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/06/2023]
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12
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Orlandi G, Calvini R, Foca G, Pigani L, Vasile Simone G, Ulrici A. Data fusion of electronic eye and electronic tongue signals to monitor grape ripening. Talanta 2019; 195:181-189. [DOI: 10.1016/j.talanta.2018.11.046] [Citation(s) in RCA: 23] [Impact Index Per Article: 4.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/23/2018] [Revised: 11/08/2018] [Accepted: 11/14/2018] [Indexed: 11/30/2022]
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13
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Seidi S, Ranjbar MH, Baharfar M, Shanehsaz M, Tajik M. A promising design of microfluidic electromembrane extraction coupled with sensitive colorimetric detection for colorless compounds based on quantum dots fluorescence. Talanta 2019; 194:298-307. [DOI: 10.1016/j.talanta.2018.10.046] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/06/2018] [Revised: 10/12/2018] [Accepted: 10/13/2018] [Indexed: 11/25/2022]
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14
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Córdova A, Saavedra J, Mondaca V, Vidal J, Astudillo-Castro C. Quality assessment and multivariate calibration of 5-hydroxymethylfurfural during a concentration process for clarified apple juice. Food Control 2019. [DOI: 10.1016/j.foodcont.2018.07.050] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/20/2023]
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15
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Development of an automated method for the identification of defective hazelnuts based on RGB image analysis and colourgrams. Food Control 2018. [DOI: 10.1016/j.foodcont.2018.07.018] [Citation(s) in RCA: 22] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/21/2022]
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16
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Orlandi G, Calvini R, Pigani L, Foca G, Vasile Simone G, Antonelli A, Ulrici A. Electronic eye for the prediction of parameters related to grape ripening. Talanta 2018; 186:381-388. [DOI: 10.1016/j.talanta.2018.04.076] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/26/2018] [Revised: 04/19/2018] [Accepted: 04/23/2018] [Indexed: 02/04/2023]
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